WorldWideScience

Sample records for robust sensor faults

  1. Robust Sensor Faults Reconstruction for a Class of Uncertain Linear Systems Using a Sliding Mode Observer: An LMI Approach

    International Nuclear Information System (INIS)

    Iskander, Boulaabi; Faycal, Ben Hmida; Moncef, Gossa; Anis, Sellami

    2009-01-01

    This paper presents a design method of a Sliding Mode Observer (SMO) for robust sensor faults reconstruction of systems with matched uncertainty. This class of uncertainty requires a known upper bound. The basic idea is to use the H ∞ concept to design the observer, which minimizes the effect of the uncertainty on the reconstruction of the sensor faults. Specifically, we applied the equivalent output error injection concept from previous work in Fault Detection and Isolation (FDI) scheme. Then, these two problems of design and reconstruction can be expressed and numerically formulate via Linear Matrix Inequalities (LMIs) optimization. Finally, a numerical example is given to illustrate the validity and the applicability of the proposed approach.

  2. Extreme temperature robust optical sensor designs and fault-tolerant signal processing

    Science.gov (United States)

    Riza, Nabeel Agha [Oviedo, FL; Perez, Frank [Tujunga, CA

    2012-01-17

    Silicon Carbide (SiC) probe designs for extreme temperature and pressure sensing uses a single crystal SiC optical chip encased in a sintered SiC material probe. The SiC chip may be protected for high temperature only use or exposed for both temperature and pressure sensing. Hybrid signal processing techniques allow fault-tolerant extreme temperature sensing. Wavelength peak-to-peak (or null-to-null) collective spectrum spread measurement to detect wavelength peak/null shift measurement forms a coarse-fine temperature measurement using broadband spectrum monitoring. The SiC probe frontend acts as a stable emissivity Black-body radiator and monitoring the shift in radiation spectrum enables a pyrometer. This application combines all-SiC pyrometry with thick SiC etalon laser interferometry within a free-spectral range to form a coarse-fine temperature measurement sensor. RF notch filtering techniques improve the sensitivity of the temperature measurement where fine spectral shift or spectrum measurements are needed to deduce temperature.

  3. On the Generation of a Robust Residual for Closed-loopControl systems that Exhibit Sensor Faults

    DEFF Research Database (Denmark)

    Alavi, Seyed Mohammad Mahdi; Izadi-Zamanabadi, Roozbeh; Hayes, Martin J.

    2007-01-01

    This paper presents a novel design methodology, based on shaping the system frequency response, for the generation of an appropriate residual signal that is sensitive to sensor faults in the presence of model uncertainty and exogenous unknown (unmeasured) disturbances. An integrated feedback cont...

  4. Robust fault detection and isolation technique for single-input/single-output closed-loop control systems that exhibit actuator and sensor faults

    DEFF Research Database (Denmark)

    Izadi-Zamanabadi, Roozbeh; Alavi, S. M. Mahdi; Hayes, M. J.

    2008-01-01

    An integrated quantitative feedback design and frequency-based fault detection and isolation (FDI) approach is presented for single-input/single-output systems. A novel design methodology, based on shaping the system frequency response, is proposed to generate an appropriate residual signal...

  5. Fault detection in finite frequency domain for Takagi-Sugeno fuzzy systems with sensor faults.

    Science.gov (United States)

    Li, Xiao-Jian; Yang, Guang-Hong

    2014-08-01

    This paper is concerned with the fault detection (FD) problem in finite frequency domain for continuous-time Takagi-Sugeno fuzzy systems with sensor faults. Some finite-frequency performance indices are initially introduced to measure the fault/reference input sensitivity and disturbance robustness. Based on these performance indices, an effective FD scheme is then presented such that the generated residual is designed to be sensitive to both fault and reference input for faulty cases, while robust against the reference input for fault-free case. As the additional reference input sensitivity for faulty cases is considered, it is shown that the proposed method improves the existing FD techniques and achieves a better FD performance. The theory is supported by simulation results related to the detection of sensor faults in a tunnel-diode circuit.

  6. Fault detection for discrete-time switched systems with sensor stuck faults and servo inputs.

    Science.gov (United States)

    Zhong, Guang-Xin; Yang, Guang-Hong

    2015-09-01

    This paper addresses the fault detection problem of switched systems with servo inputs and sensor stuck faults. The attention is focused on designing a switching law and its associated fault detection filters (FDFs). The proposed switching law uses only the current states of FDFs, which guarantees the residuals are sensitive to the servo inputs with known frequency ranges in faulty cases and robust against them in fault-free case. Thus, the arbitrarily small sensor stuck faults, including outage faults can be detected in finite-frequency domain. The levels of sensitivity and robustness are measured in terms of the finite-frequency H- index and l2-gain. Finally, the switching law and FDFs are obtained by the solution of a convex optimization problem. Copyright © 2015 ISA. Published by Elsevier Ltd. All rights reserved.

  7. Particle Filter for Fault Diagnosis and Robust Navigation of Underwater Robot

    DEFF Research Database (Denmark)

    Zhao, Bo; Skjetne, Roger; Blanke, Mogens

    2014-01-01

    A particle filter based robust navigation with fault diagnosis is designed for an underwater robot, where 10 failure modes of sensors and thrusters are considered. The nominal underwater robot and its anomaly are described by a switchingmode hidden Markov model. By extensively running a particle...... filter on the model, the fault diagnosis and robust navigation are achieved. Closed-loop full-scale experimental results show that the proposed method is robust, can diagnose faults effectively, and can provide good state estimation even in cases where multiple faults occur. Comparing with other methods...

  8. Active Fault-Tolerant Control for Wind Turbine with Simultaneous Actuator and Sensor Faults

    Directory of Open Access Journals (Sweden)

    Lei Wang

    2017-01-01

    Full Text Available The purpose of this paper is to show a novel fault-tolerant tracking control (FTC strategy with robust fault estimation and compensating for simultaneous actuator sensor faults. Based on the framework of fault-tolerant control, developing an FTC design method for wind turbines is a challenge and, thus, they can tolerate simultaneous pitch actuator and pitch sensor faults having bounded first time derivatives. The paper’s key contribution is proposing a descriptor sliding mode method, in which for establishing a novel augmented descriptor system, with which we can estimate the state of system and reconstruct fault by designing descriptor sliding mode observer, the paper introduces an auxiliary descriptor state vector composed by a system state vector, actuator fault vector, and sensor fault vector. By the optimized method of LMI, the conditions for stability that estimated error dynamics are set up to promote the determination of the parameters designed. With this estimation, and designing a fault-tolerant controller, the system’s stability can be maintained. The effectiveness of the design strategy is verified by implementing the controller in the National Renewable Energy Laboratory’s 5-MW nonlinear, high-fidelity wind turbine model (FAST and simulating it in MATLAB/Simulink.

  9. Fault Reconnaissance Agent for Sensor Networks

    Directory of Open Access Journals (Sweden)

    Elhadi M. Shakshuki

    2010-01-01

    Full Text Available One of the key prerequisite for a scalable, effective and efficient sensor network is the utilization of low-cost, low-overhead and high-resilient fault-inference techniques. To this end, we propose an intelligent agent system with a problem solving capability to address the issue of fault inference in sensor network environments. The intelligent agent system is designed and implemented at base-station side. The core of the agent system – problem solver – implements a fault-detection inference engine which harnesses Expectation Maximization (EM algorithm to estimate fault probabilities of sensor nodes. To validate the correctness and effectiveness of the intelligent agent system, a set of experiments in a wireless sensor testbed are conducted. The experimental results show that our intelligent agent system is able to precisely estimate the fault probability of sensor nodes.

  10. Robust Parametric Fault Estimation in a Hopper System

    DEFF Research Database (Denmark)

    Soltani, Mohsen; Izadi-Zamanabadi, Roozbeh; Wisniewski, Rafal

    2012-01-01

    The ability of diagnosis of the possible faults is a necessity for satellite launch vehicles during their mission. In this paper, a structural analysis method is employed to divide the complex propulsion system into simpler subsystems for fault diagnosis filter design. A robust fault diagnosis me...

  11. Optimal Robust Fault Detection for Linear Discrete Time Systems

    Directory of Open Access Journals (Sweden)

    Nike Liu

    2008-01-01

    Full Text Available This paper considers robust fault-detection problems for linear discrete time systems. It is shown that the optimal robust detection filters for several well-recognized robust fault-detection problems, such as ℋ−/ℋ∞, ℋ2/ℋ∞, and ℋ∞/ℋ∞ problems, are the same and can be obtained by solving a standard algebraic Riccati equation. Optimal filters are also derived for many other optimization criteria and it is shown that some well-studied and seeming-sensible optimization criteria for fault-detection filter design could lead to (optimal but useless fault-detection filters.

  12. Fault-tolerant system for catastrophic faults in AMR sensors

    NARCIS (Netherlands)

    Zambrano Constantini, A.C.; Kerkhoff, Hans G.

    Anisotropic Magnetoresistance angle sensors are widely used in automotive applications considered to be safety-critical applications. Therefore dependability is an important requirement and fault-tolerant strategies must be used to guarantee the correct operation of the sensors even in case of

  13. Robust Fault Diagnosis Design for Linear Multiagent Systems with Incipient Faults

    Directory of Open Access Journals (Sweden)

    Jingping Xia

    2015-01-01

    Full Text Available The design of a robust fault estimation observer is studied for linear multiagent systems subject to incipient faults. By considering the fact that incipient faults are in low-frequency domain, the fault estimation of such faults is proposed for discrete-time multiagent systems based on finite-frequency technique. Moreover, using the decomposition design, an equivalent conclusion is given. Simulation results of a numerical example are presented to demonstrate the effectiveness of the proposed techniques.

  14. Tolerance Towards Sensor Faults: An Application to a Flexible Arm Manipulator

    Directory of Open Access Journals (Sweden)

    Chee Pin Tan

    2006-12-01

    Full Text Available As more engineering operations become automatic, the need for robustness towards faults increases. Hence, a fault tolerant control (FTC scheme is a valuable asset. This paper presents a robust sensor fault FTC scheme implemented on a flexible arm manipulator, which has many applications in automation. Sensor faults affect the system's performance in the closed loop when the faulty sensor readings are used to generate the control input. In this paper, the non-faulty sensors are used to reconstruct the faults on the potentially faulty sensors. The reconstruction is subtracted from the faulty sensors to form a compensated ‘virtual sensor’ and this signal (instead of the normally used faulty sensor output is then used to generate the control input. A design method is also presented in which the FTC scheme is made insensitive to any system uncertainties. Two fault conditions are tested; total failure and incipient faults. Then the scheme robustness is tested by implementing the flexible joint's FTC scheme on a flexible link, which has different parameters. Excellent results have been obtained for both cases (joint and link; the FTC scheme caused the system performance is almost identical to the fault-free scenario, whilst providing an indication that a fault is present, even for simultaneous faults.

  15. Data Fault Detection in Medical Sensor Networks

    Directory of Open Access Journals (Sweden)

    Yang Yang

    2015-03-01

    Full Text Available Medical body sensors can be implanted or attached to the human body to monitor the physiological parameters of patients all the time. Inaccurate data due to sensor faults or incorrect placement on the body will seriously influence clinicians’ diagnosis, therefore detecting sensor data faults has been widely researched in recent years. Most of the typical approaches to sensor fault detection in the medical area ignore the fact that the physiological indexes of patients aren’t changing synchronously at the same time, and fault values mixed with abnormal physiological data due to illness make it difficult to determine true faults. Based on these facts, we propose a Data Fault Detection mechanism in Medical sensor networks (DFD-M. Its mechanism includes: (1 use of a dynamic-local outlier factor (D-LOF algorithm to identify outlying sensed data vectors; (2 use of a linear regression model based on trapezoidal fuzzy numbers to predict which readings in the outlying data vector are suspected to be faulty; (3 the proposal of a novel judgment criterion of fault state according to the prediction values. The simulation results demonstrate the efficiency and superiority of DFD-M.

  16. A comparative study of sensor fault diagnosis methods based on observer for ECAS system

    Science.gov (United States)

    Xu, Xing; Wang, Wei; Zou, Nannan; Chen, Long; Cui, Xiaoli

    2017-03-01

    The performance and practicality of electronically controlled air suspension (ECAS) system are highly dependent on the state information supplied by kinds of sensors, but faults of sensors occur frequently. Based on a non-linearized 3-DOF 1/4 vehicle model, different methods of fault detection and isolation (FDI) are used to diagnose the sensor faults for ECAS system. The considered approaches include an extended Kalman filter (EKF) with concise algorithm, a strong tracking filter (STF) with robust tracking ability, and the cubature Kalman filter (CKF) with numerical precision. We propose three filters of EKF, STF, and CKF to design a state observer of ECAS system under typical sensor faults and noise. Results show that three approaches can successfully detect and isolate faults respectively despite of the existence of environmental noise, FDI time delay and fault sensitivity of different algorithms are different, meanwhile, compared with EKF and STF, CKF method has best performing FDI of sensor faults for ECAS system.

  17. Identifiability of Additive Actuator and Sensor Faults by State Augmentation

    Science.gov (United States)

    Joshi, Suresh; Gonzalez, Oscar R.; Upchurch, Jason M.

    2014-01-01

    A class of fault detection and identification (FDI) methods for bias-type actuator and sensor faults is explored in detail from the point of view of fault identifiability. The methods use state augmentation along with banks of Kalman-Bucy filters for fault detection, fault pattern determination, and fault value estimation. A complete characterization of conditions for identifiability of bias-type actuator faults, sensor faults, and simultaneous actuator and sensor faults is presented. It is shown that FDI of simultaneous actuator and sensor faults is not possible using these methods when all sensors have unknown biases. The fault identifiability conditions are demonstrated via numerical examples. The analytical and numerical results indicate that caution must be exercised to ensure fault identifiability for different fault patterns when using such methods.

  18. Vehicle Fault Diagnose Based on Smart Sensor

    Science.gov (United States)

    Zhining, Li; Peng, Wang; Jianmin, Mei; Jianwei, Li; Fei, Teng

    In the vehicle's traditional fault diagnose system, we usually use a computer system with a A/D card and with many sensors connected to it. The disadvantage of this system is that these sensor can hardly be shared with control system and other systems, there are too many connect lines and the electro magnetic compatibility(EMC) will be affected. In this paper, smart speed sensor, smart acoustic press sensor, smart oil press sensor, smart acceleration sensor and smart order tracking sensor were designed to solve this problem. With the CAN BUS these smart sensors, fault diagnose computer and other computer could be connected together to establish a network system which can monitor and control the vehicle's diesel and other system without any duplicate sensor. The hard and soft ware of the smart sensor system was introduced, the oil press, vibration and acoustic signal are resampled by constant angle increment to eliminate the influence of the rotate speed. After the resample, the signal in every working cycle could be averaged in angle domain and do other analysis like order spectrum.

  19. Robustness to Faults Promotes Evolvability: Insights from Evolving Digital Circuits.

    Science.gov (United States)

    Milano, Nicola; Nolfi, Stefano

    2016-01-01

    We demonstrate how the need to cope with operational faults enables evolving circuits to find more fit solutions. The analysis of the results obtained in different experimental conditions indicates that, in absence of faults, evolution tends to select circuits that are small and have low phenotypic variability and evolvability. The need to face operation faults, instead, drives evolution toward the selection of larger circuits that are truly robust with respect to genetic variations and that have a greater level of phenotypic variability and evolvability. Overall our results indicate that the need to cope with operation faults leads to the selection of circuits that have a greater probability to generate better circuits as a result of genetic variation with respect to a control condition in which circuits are not subjected to faults.

  20. Robust Fault Detection for Switched Fuzzy Systems With Unknown Input.

    Science.gov (United States)

    Han, Jian; Zhang, Huaguang; Wang, Yingchun; Sun, Xun

    2017-10-03

    This paper investigates the fault detection problem for a class of switched nonlinear systems in the T-S fuzzy framework. The unknown input is considered in the systems. A novel fault detection unknown input observer design method is proposed. Based on the proposed observer, the unknown input can be removed from the fault detection residual. The weighted H∞ performance level is considered to ensure the robustness. In addition, the weighted H₋ performance level is introduced, which can increase the sensibility of the proposed detection method. To verify the proposed scheme, a numerical simulation example and an electromechanical system simulation example are provided at the end of this paper.

  1. Modeling, Detection, and Disambiguation of Sensor Faults for Aerospace Applications

    Data.gov (United States)

    National Aeronautics and Space Administration — Sensor faults continue to be a major hurdle for sys- tems health management to reach its full potential. At the same time, few recorded instances of sensor faults...

  2. Robust Mpc for Actuator–Fault Tolerance Using Set–Based Passive Fault Detection and Active Fault Isolation

    Directory of Open Access Journals (Sweden)

    Xu Feng

    2017-03-01

    Full Text Available In this paper, a fault-tolerant control (FTC scheme is proposed for actuator faults, which is built upon tube-based model predictive control (MPC as well as set-based fault detection and isolation (FDI. In the class of MPC techniques, tubebased MPC can effectively deal with system constraints and uncertainties with relatively low computational complexity compared with other robust MPC techniques such as min-max MPC. Set-based FDI, generally considering the worst case of uncertainties, can robustly detect and isolate actuator faults. In the proposed FTC scheme, fault detection (FD is passive by using invariant sets, while fault isolation (FI is active by means of MPC and tubes. The active FI method proposed in this paper is implemented by making use of the constraint-handling ability of MPC to manipulate the bounds of inputs.

  3. Robust Solar Position Sensor for Tracking Systems

    DEFF Research Database (Denmark)

    Ritchie, Ewen; Argeseanu, Alin; Leban, Krisztina Monika

    2009-01-01

    The paper proposes a new solar position sensor used in tracking system control. The main advantages of the new solution are the robustness and the economical aspect. Positioning accuracy of the tracking system that uses the new sensor is better than 1°. The new sensor uses the ancient principle...... of the solar clock. The sensitive elements are eight ordinary photo-resistors. It is important to note that all the sensors are not selected simultaneously. It is not necessary for sensor operating characteristics to be quasi-identical because the sensor principle is based on extreme operating duty measurement...... (bright or dark). In addition, the proposed solar sensor significantly simplifies the operation of the tracking control device....

  4. Robust fault detection in open loop vs. closed loop

    DEFF Research Database (Denmark)

    Niemann, Hans Henrik; Stoustrup, J.

    1997-01-01

    The robustness aspects of fault detection and isolation (FDI) for uncertain systems are considered. The FDI problem is considered in a standard problem formulation. The FDI design problem is analyzed both in the case where the control input signal is considered as a known external input signal (o...... (open loop) and when the input signal is generated by a feedback controller...

  5. Nonlinear Robust Observer-Based Fault Detection for Networked Suspension Control System of Maglev Train

    Directory of Open Access Journals (Sweden)

    Yun Li

    2013-01-01

    Full Text Available A fault detection approach based on nonlinear robust observer is designed for the networked suspension control system of Maglev train with random induced time delay. First, considering random bounded time-delay and external disturbance, the nonlinear model of the networked suspension control system is established. Then, a nonlinear robust observer is designed using the input of the suspension gap. And the estimate error is proved to be bounded with arbitrary precision by adopting an appropriate parameter. When sensor faults happen, the residual between the real states and the observer outputs indicates which kind of sensor failures occurs. Finally, simulation results using the actual parameters of CMS-04 maglev train indicate that the proposed method is effective for maglev train.

  6. Robust fault detection of wind energy conversion systems based on dynamic neural networks.

    Science.gov (United States)

    Talebi, Nasser; Sadrnia, Mohammad Ali; Darabi, Ahmad

    2014-01-01

    Occurrence of faults in wind energy conversion systems (WECSs) is inevitable. In order to detect the occurred faults at the appropriate time, avoid heavy economic losses, ensure safe system operation, prevent damage to adjacent relevant systems, and facilitate timely repair of failed components; a fault detection system (FDS) is required. Recurrent neural networks (RNNs) have gained a noticeable position in FDSs and they have been widely used for modeling of complex dynamical systems. One method for designing an FDS is to prepare a dynamic neural model emulating the normal system behavior. By comparing the outputs of the real system and neural model, incidence of the faults can be identified. In this paper, by utilizing a comprehensive dynamic model which contains both mechanical and electrical components of the WECS, an FDS is suggested using dynamic RNNs. The presented FDS detects faults of the generator's angular velocity sensor, pitch angle sensors, and pitch actuators. Robustness of the FDS is achieved by employing an adaptive threshold. Simulation results show that the proposed scheme is capable to detect the faults shortly and it has very low false and missed alarms rate.

  7. Robust high temperature oxygen sensor electrodes

    DEFF Research Database (Denmark)

    Lund, Anders

    Platinum is the most widely used material in high temperature oxygen sensor electrodes. However, platinum is expensive and the platinum electrode may, under certain conditions, suffer from poisoning, which is detrimental for an oxygen sensor. The objective of this thesis is to evaluate electrode...... materials as candidates for robust oxygen sensor electrodes. The present work focuses on characterising the electrochemical properties of a few electrode materials to understand which oxygen electrode processes are limiting for the response time of the sensor electrode. Three types of porous platinum......-Dansensor. The electrochemical properties of the electrodes were characterised by electrochemical impedance spectroscopy (EIS), and the structures were characterised by x-ray diffraction and electron microscopy. At an oxygen partial pressures of 0.2 bar, the response time of the sensor electrode was determined by oxygen...

  8. Robust giant magnetoresistive effect type multilayer sensor

    NARCIS (Netherlands)

    Lenssen, K.M.H.; Kuiper, A.E.T.; Roozeboom, F.

    2002-01-01

    A robust Giant Magneto Resistive effect type multilayer sensor comprising a free and a pinned ferromagnetic layer, which can withstand high temperatures and strong magnetic fields as required in automotive applications. The GMR multi-layer has an asymmetric magneto-resistive curve and enables

  9. Integrated Fault Diagnosis Algorithm for Motor Sensors of In-Wheel Independent Drive Electric Vehicles

    Science.gov (United States)

    Jeon, Namju; Lee, Hyeongcheol

    2016-01-01

    An integrated fault-diagnosis algorithm for a motor sensor of in-wheel independent drive electric vehicles is presented. This paper proposes a method that integrates the high- and low-level fault diagnoses to improve the robustness and performance of the system. For the high-level fault diagnosis of vehicle dynamics, a planar two-track non-linear model is first selected, and the longitudinal and lateral forces are calculated. To ensure redundancy of the system, correlation between the sensor and residual in the vehicle dynamics is analyzed to detect and separate the fault of the drive motor system of each wheel. To diagnose the motor system for low-level faults, the state equation of an interior permanent magnet synchronous motor is developed, and a parity equation is used to diagnose the fault of the electric current and position sensors. The validity of the high-level fault-diagnosis algorithm is verified using Carsim and Matlab/Simulink co-simulation. The low-level fault diagnosis is verified through Matlab/Simulink simulation and experiments. Finally, according to the residuals of the high- and low-level fault diagnoses, fault-detection flags are defined. On the basis of this information, an integrated fault-diagnosis strategy is proposed. PMID:27973431

  10. Integrated Fault Diagnosis Algorithm for Motor Sensors of In-Wheel Independent Drive Electric Vehicles.

    Science.gov (United States)

    Jeon, Namju; Lee, Hyeongcheol

    2016-12-12

    An integrated fault-diagnosis algorithm for a motor sensor of in-wheel independent drive electric vehicles is presented. This paper proposes a method that integrates the high- and low-level fault diagnoses to improve the robustness and performance of the system. For the high-level fault diagnosis of vehicle dynamics, a planar two-track non-linear model is first selected, and the longitudinal and lateral forces are calculated. To ensure redundancy of the system, correlation between the sensor and residual in the vehicle dynamics is analyzed to detect and separate the fault of the drive motor system of each wheel. To diagnose the motor system for low-level faults, the state equation of an interior permanent magnet synchronous motor is developed, and a parity equation is used to diagnose the fault of the electric current and position sensors. The validity of the high-level fault-diagnosis algorithm is verified using Carsim and Matlab/Simulink co-simulation. The low-level fault diagnosis is verified through Matlab/Simulink simulation and experiments. Finally, according to the residuals of the high- and low-level fault diagnoses, fault-detection flags are defined. On the basis of this information, an integrated fault-diagnosis strategy is proposed.

  11. Simultaneous Sensor and Process Fault Diagnostics for Propellant Feed System

    Science.gov (United States)

    Cao, J.; Kwan, C.; Figueroa, F.; Xu, R.

    2006-01-01

    The main objective of this research is to extract fault features from sensor faults and process faults by using advanced fault detection and isolation (FDI) algorithms. A tank system that has some common characteristics to a NASA testbed at Stennis Space Center was used to verify our proposed algorithms. First, a generic tank system was modeled. Second, a mathematical model suitable for FDI has been derived for the tank system. Third, a new and general FDI procedure has been designed to distinguish process faults and sensor faults. Extensive simulations clearly demonstrated the advantages of the new design.

  12. Fault-tolerant cooperative output regulation for multi-vehicle systems with sensor faults

    Science.gov (United States)

    Qin, Liguo; He, Xiao; Zhou, D. H.

    2017-10-01

    This paper presents a unified framework of fault diagnosis and fault-tolerant cooperative output regulation (FTCOR) for a linear discrete-time multi-vehicle system with sensor faults. The FTCOR control law is designed through three steps. A cooperative output regulation (COR) controller is designed based on the internal mode principle when there are no sensor faults. A sufficient condition on the existence of the COR controller is given based on the discrete-time algebraic Riccati equation (DARE). Then, a decentralised fault diagnosis scheme is designed to cope with sensor faults occurring in followers. A residual generator is developed to detect sensor faults of each follower, and a bank of fault-matching estimators are proposed to isolate and estimate sensor faults of each follower. Unlike the current distributed fault diagnosis for multi-vehicle systems, the presented decentralised fault diagnosis scheme in each vehicle reduces the communication and computation load by only using the information of the vehicle. By combing the sensor fault estimation and the COR control law, an FTCOR controller is proposed. Finally, the simulation results demonstrate the effectiveness of the FTCOR controller.

  13. SENSORS FAULT DIAGNOSIS ALGORITHM DESIGN OF A HYDRAULIC SYSTEM

    Directory of Open Access Journals (Sweden)

    Matej ORAVEC

    2017-06-01

    Full Text Available This article presents the sensors fault diagnosis system design for the hydraulic system, which is based on the group of the three fault estimation filters. These filters are used for estimation of the system states and sensors fault magnitude. Also, this article briefly stated the hydraulic system state control design with integrator, which is important assumption for the fault diagnosis system design. The sensors fault diagnosis system is implemented into the Matlab/Simulink environment and it is verified using the controlled hydraulic system simulation model. Verification of the designed fault diagnosis system is realized by series of experiments, which simulates sensors faults. The results of the experiments are briefly presented in the last part of this article.

  14. Sensor fault detection and recovery in satellite attitude control

    Science.gov (United States)

    Nasrolahi, Seiied Saeed; Abdollahi, Farzaneh

    2018-04-01

    This paper proposes an integrated sensor fault detection and recovery for the satellite attitude control system. By introducing a nonlinear observer, the healthy sensor measurements are provided. Considering attitude dynamics and kinematic, a novel observer is developed to detect the fault in angular rate as well as attitude sensors individually or simultaneously. There is no limit on type and configuration of attitude sensors. By designing a state feedback based control signal and Lyapunov stability criterion, the uniformly ultimately boundedness of tracking errors in the presence of sensor faults is guaranteed. Finally, simulation results are presented to illustrate the performance of the integrated scheme.

  15. Energy Efficient Distributed Fault Identification Algorithm in Wireless Sensor Networks

    Directory of Open Access Journals (Sweden)

    Meenakshi Panda

    2014-01-01

    Full Text Available A distributed fault identification algorithm is proposed here to find both hard and soft faulty sensor nodes present in wireless sensor networks. The algorithm is distributed, self-detectable, and can detect the most common byzantine faults such as stuck at zero, stuck at one, and random data. In the proposed approach, each sensor node gathered the observed data from the neighbors and computed the mean to check whether faulty sensor node is present or not. If a node found the presence of faulty sensor node, then compares observed data with the data of the neighbors and predict probable fault status. The final fault status is determined by diffusing the fault information from the neighbors. The accuracy and completeness of the algorithm are verified with the help of statistical model of the sensors data. The performance is evaluated in terms of detection accuracy, false alarm rate, detection latency and message complexity.

  16. Observer Based Detection of Sensor Faults in Wind Turbines

    DEFF Research Database (Denmark)

    Odgaard, Peter Fogh; Stoustrup, Jakob; Nielsen, R.

    2009-01-01

    , if an unknown input observer the fault detection  scheme can be non dependent on the actual wind speed. The scheme  is validated on data from a more advanced and detailed simulation  model. The proposed scheme detects the sensor faults a few samples  after the beginning of the faults....

  17. Guaranteed Cost Fault-Tolerant Control for Networked Control Systems with Sensor Faults

    Directory of Open Access Journals (Sweden)

    Qixin Zhu

    2015-01-01

    Full Text Available For the large scale and complicated structure of networked control systems, time-varying sensor faults could inevitably occur when the system works in a poor environment. Guaranteed cost fault-tolerant controller for the new networked control systems with time-varying sensor faults is designed in this paper. Based on time delay of the network transmission environment, the networked control systems with sensor faults are modeled as a discrete-time system with uncertain parameters. And the model of networked control systems is related to the boundary values of the sensor faults. Moreover, using Lyapunov stability theory and linear matrix inequalities (LMI approach, the guaranteed cost fault-tolerant controller is verified to render such networked control systems asymptotically stable. Finally, simulations are included to demonstrate the theoretical results.

  18. Fault-tolerant control for current sensors of doubly fed induction generators based on an improved fault detection method

    DEFF Research Database (Denmark)

    Li, Hui; Yang, Chao; Hu, Yaogang

    2014-01-01

    Fault-tolerant control of current sensors is studied in this paper to improve the reliability of a doubly fed induction generator (DFIG). A fault-tolerant control system of current sensors is presented for the DFIG, which consists of a new current observer and an improved current sensor fault...... detection algorithm, and fault-tolerant control system are investigated by simulation. The results indicate that the outputs of the observer and the sensor are highly coherent. The fault detection algorithm can efficiently detect both soft and hard faults in current sensors, and the fault-tolerant control...

  19. Hall Sensor Output Signal Fault-Detection & Safety Implementation Logic

    Directory of Open Access Journals (Sweden)

    Lee SangHun

    2016-01-01

    Full Text Available Recently BLDC motors have been popular in various industrial applications and electric mobility. Recently BLDC motors have been popular in various industrial applications and electric mobility. In most brushless direct current (BLDC motor drives, there are three hall sensors as a position reference. Low resolution hall effect sensor is popularly used to estimate the rotor position because of its good comprehensive performance such as low cost, high reliability and sufficient precision. Various possible faults may happen in a hall effect sensor. This paper presents a fault-tolerant operation method that allows the control of a BLDC motor with one faulty hall sensor and presents the hall sensor output fault-tolerant control strategy. The situations considered are when the output from a hall sensor stays continuously at low or high levels, or a short-time pulse appears on a hall sensor signal. For fault detection, identification of a faulty signal and generating a substitute signal, this method only needs the information from the hall sensors. There are a few research work on hall effect sensor failure of BLDC motor. The conventional fault diagnosis methods are signal analysis, model based analysis and knowledge based analysis. The proposed method is signal based analysis using a compensation signal for reconfiguration and therefore fault diagnosis can be fast. The proposed method is validated to execute the simulation using PSIM.

  20. Indirect adaptive fuzzy fault-tolerant tracking control for MIMO nonlinear systems with actuator and sensor failures.

    Science.gov (United States)

    Bounemeur, Abdelhamid; Chemachema, Mohamed; Essounbouli, Najib

    2018-05-10

    In this paper, an active fuzzy fault tolerant tracking control (AFFTTC) scheme is developed for a class of multi-input multi-output (MIMO) unknown nonlinear systems in the presence of unknown actuator faults, sensor failures and external disturbance. The developed control scheme deals with four kinds of faults for both sensors and actuators. The bias, drift, and loss of accuracy additive faults are considered along with the loss of effectiveness multiplicative fault. A fuzzy adaptive controller based on back-stepping design is developed to deal with actuator failures and unknown system dynamics. However, an additional robust control term is added to deal with sensor faults, approximation errors, and external disturbances. Lyapunov theory is used to prove the stability of the closed loop system. Numerical simulations on a quadrotor are presented to show the effectiveness of the proposed approach. Copyright © 2018 ISA. Published by Elsevier Ltd. All rights reserved.

  1. Distributed adaptive diagnosis of sensor faults using structural response data

    Science.gov (United States)

    Dragos, Kosmas; Smarsly, Kay

    2016-10-01

    The reliability and consistency of wireless structural health monitoring (SHM) systems can be compromised by sensor faults, leading to miscalibrations, corrupted data, or even data loss. Several research approaches towards fault diagnosis, referred to as ‘analytical redundancy’, have been proposed that analyze the correlations between different sensor outputs. In wireless SHM, most analytical redundancy approaches require centralized data storage on a server for data analysis, while other approaches exploit the on-board computing capabilities of wireless sensor nodes, analyzing the raw sensor data directly on board. However, using raw sensor data poses an operational constraint due to the limited power resources of wireless sensor nodes. In this paper, a new distributed autonomous approach towards sensor fault diagnosis based on processed structural response data is presented. The inherent correlations among Fourier amplitudes of acceleration response data, at peaks corresponding to the eigenfrequencies of the structure, are used for diagnosis of abnormal sensor outputs at a given structural condition. Representing an entirely data-driven analytical redundancy approach that does not require any a priori knowledge of the monitored structure or of the SHM system, artificial neural networks (ANN) are embedded into the sensor nodes enabling cooperative fault diagnosis in a fully decentralized manner. The distributed analytical redundancy approach is implemented into a wireless SHM system and validated in laboratory experiments, demonstrating the ability of wireless sensor nodes to self-diagnose sensor faults accurately and efficiently with minimal data traffic. Besides enabling distributed autonomous fault diagnosis, the embedded ANNs are able to adapt to the actual condition of the structure, thus ensuring accurate and efficient fault diagnosis even in case of structural changes.

  2. Sensor fault diagnosis for automotive engines with real data ...

    African Journals Online (AJOL)

    In this paper, a new fault diagnosis method using an adaptive neural network for automotive engines is developed. A redial basis function (RBF) network is used as a ... The real data experiments confirm that sensor faults as small as 2% can be detected and isolated clearly. The developed scheme is capable of diagnosing ...

  3. Modeling Sensor Reliability in Fault Diagnosis Based on Evidence Theory

    Directory of Open Access Journals (Sweden)

    Kaijuan Yuan

    2016-01-01

    Full Text Available Sensor data fusion plays an important role in fault diagnosis. Dempster–Shafer (D-R evidence theory is widely used in fault diagnosis, since it is efficient to combine evidence from different sensors. However, under the situation where the evidence highly conflicts, it may obtain a counterintuitive result. To address the issue, a new method is proposed in this paper. Not only the statistic sensor reliability, but also the dynamic sensor reliability are taken into consideration. The evidence distance function and the belief entropy are combined to obtain the dynamic reliability of each sensor report. A weighted averaging method is adopted to modify the conflict evidence by assigning different weights to evidence according to sensor reliability. The proposed method has better performance in conflict management and fault diagnosis due to the fact that the information volume of each sensor report is taken into consideration. An application in fault diagnosis based on sensor fusion is illustrated to show the efficiency of the proposed method. The results show that the proposed method improves the accuracy of fault diagnosis from 81.19% to 89.48% compared to the existing methods.

  4. Enhanced Bank of Kalman Filters Developed and Demonstrated for In-Flight Aircraft Engine Sensor Fault Diagnostics

    Science.gov (United States)

    Kobayashi, Takahisa; Simon, Donald L.

    2005-01-01

    In-flight sensor fault detection and isolation (FDI) is critical to maintaining reliable engine operation during flight. The aircraft engine control system, which computes control commands on the basis of sensor measurements, operates the propulsion systems at the demanded conditions. Any undetected sensor faults, therefore, may cause the control system to drive the engine into an undesirable operating condition. It is critical to detect and isolate failed sensors as soon as possible so that such scenarios can be avoided. A challenging issue in developing reliable sensor FDI systems is to make them robust to changes in engine operating characteristics due to degradation with usage and other faults that can occur during flight. A sensor FDI system that cannot appropriately account for such scenarios may result in false alarms, missed detections, or misclassifications when such faults do occur. To address this issue, an enhanced bank of Kalman filters was developed, and its performance and robustness were demonstrated in a simulation environment. The bank of filters is composed of m + 1 Kalman filters, where m is the number of sensors being used by the control system and, thus, in need of monitoring. Each Kalman filter is designed on the basis of a unique fault hypothesis so that it will be able to maintain its performance if a particular fault scenario, hypothesized by that particular filter, takes place.

  5. Integral Sensor Fault Detection and Isolation for Railway Traction Drive.

    Science.gov (United States)

    Garramiola, Fernando; Del Olmo, Jon; Poza, Javier; Madina, Patxi; Almandoz, Gaizka

    2018-05-13

    Due to the increasing importance of reliability and availability of electric traction drives in Railway applications, early detection of faults has become an important key for Railway traction drive manufacturers. Sensor faults are important sources of failures. Among the different fault diagnosis approaches, in this article an integral diagnosis strategy for sensors in traction drives is presented. Such strategy is composed of an observer-based approach for direct current (DC)-link voltage and catenary current sensors, a frequency analysis approach for motor current phase sensors and a hardware redundancy solution for speed sensors. None of them requires any hardware change requirement in the actual traction drive. All the fault detection and isolation approaches have been validated in a Hardware-in-the-loop platform comprising a Real Time Simulator and a commercial Traction Control Unit for a tram. In comparison to safety-critical systems in Aerospace applications, Railway applications do not need instantaneous detection, and the diagnosis is validated in a short time period for reliable decision. Combining the different approaches and existing hardware redundancy, an integral fault diagnosis solution is provided, to detect and isolate faults in all the sensors installed in the traction drive.

  6. A Wideband Magnetoresistive Sensor for Monitoring Dynamic Fault Slip in Laboratory Fault Friction Experiments.

    Science.gov (United States)

    Kilgore, Brian D

    2017-12-02

    A non-contact, wideband method of sensing dynamic fault slip in laboratory geophysical experiments employs an inexpensive magnetoresistive sensor, a small neodymium rare earth magnet, and user built application-specific wideband signal conditioning. The magnetoresistive sensor generates a voltage proportional to the changing angles of magnetic flux lines, generated by differential motion or rotation of the near-by magnet, through the sensor. The performance of an array of these sensors compares favorably to other conventional position sensing methods employed at multiple locations along a 2 m long × 0.4 m deep laboratory strike-slip fault. For these magnetoresistive sensors, the lack of resonance signals commonly encountered with cantilever-type position sensor mounting, the wide band response (DC to ≈ 100 kHz) that exceeds the capabilities of many traditional position sensors, and the small space required on the sample, make them attractive options for capturing high speed fault slip measurements in these laboratory experiments. An unanticipated observation of this study is the apparent sensitivity of this sensor to high frequency electomagnetic signals associated with fault rupture and (or) rupture propagation, which may offer new insights into the physics of earthquake faulting.

  7. Sensor Fault Detection and Diagnosis for autonomous vehicles

    Directory of Open Access Journals (Sweden)

    Realpe Miguel

    2015-01-01

    Full Text Available In recent years testing autonomous vehicles on public roads has become a reality. However, before having autonomous vehicles completely accepted on the roads, they have to demonstrate safe operation and reliable interaction with other traffic participants. Furthermore, in real situations and long term operation, there is always the possibility that diverse components may fail. This paper deals with possible sensor faults by defining a federated sensor data fusion architecture. The proposed architecture is designed to detect obstacles in an autonomous vehicle’s environment while detecting a faulty sensor using SVM models for fault detection and diagnosis. Experimental results using sensor information from the KITTI dataset confirm the feasibility of the proposed architecture to detect soft and hard faults from a particular sensor.

  8. Robust Fault Detection for a Class of Uncertain Nonlinear Systems Based on Multiobjective Optimization

    Directory of Open Access Journals (Sweden)

    Bingyong Yan

    2015-01-01

    Full Text Available A robust fault detection scheme for a class of nonlinear systems with uncertainty is proposed. The proposed approach utilizes robust control theory and parameter optimization algorithm to design the gain matrix of fault tracking approximator (FTA for fault detection. The gain matrix of FTA is designed to minimize the effects of system uncertainty on residual signals while maximizing the effects of system faults on residual signals. The design of the gain matrix of FTA takes into account the robustness of residual signals to system uncertainty and sensitivity of residual signals to system faults simultaneously, which leads to a multiobjective optimization problem. Then, the detectability of system faults is rigorously analyzed by investigating the threshold of residual signals. Finally, simulation results are provided to show the validity and applicability of the proposed approach.

  9. Design of integrated systems for control and detection of actuator/sensor faults

    DEFF Research Database (Denmark)

    Stoustrup, J.; Grimble, M.J.; Niemann, Hans Henrik

    1997-01-01

    Consider control systems operating under potentially faulty conditions. Discusses the problems of designing a single unit which not only handle the required control but also identified faults occuring in actuators and sensors. In common practice, unites for control and for diagnosis are designed......-integrated design of control and diagnosis unit. Shows how a combined module for control and diagnosis can be designed which is able to follow references and reject disturbances robustly, control the system so that the undertected faults do not have disastrous effect, reduce the number of false alarams and indetify...

  10. Fault diagnosis of sensor networked structures with multiple faults using a virtual beam based approach

    Science.gov (United States)

    Wang, H.; Jing, X. J.

    2017-07-01

    This paper presents a virtual beam based approach suitable for conducting diagnosis of multiple faults in complex structures with limited prior knowledge of the faults involved. The "virtual beam", a recently-proposed concept for fault detection in complex structures, is applied, which consists of a chain of sensors representing a vibration energy transmission path embedded in the complex structure. Statistical tests and adaptive threshold are particularly adopted for fault detection due to limited prior knowledge of normal operational conditions and fault conditions. To isolate the multiple faults within a specific structure or substructure of a more complex one, a 'biased running' strategy is developed and embedded within the bacterial-based optimization method to construct effective virtual beams and thus to improve the accuracy of localization. The proposed method is easy and efficient to implement for multiple fault localization with limited prior knowledge of normal conditions and faults. With extensive experimental results, it is validated that the proposed method can localize both single fault and multiple faults more effectively than the classical trust index subtract on negative add on positive (TI-SNAP) method.

  11. Robust optical sensors for safety critical automotive applications

    Science.gov (United States)

    De Locht, Cliff; De Knibber, Sven; Maddalena, Sam

    2008-02-01

    Optical sensors for the automotive industry need to be robust, high performing and low cost. This paper focuses on the impact of automotive requirements on optical sensor design and packaging. Main strategies to lower optical sensor entry barriers in the automotive market include: Perform sensor calibration and tuning by the sensor manufacturer, sensor test modes on chip to guarantee functional integrity at operation, and package technology is key. As a conclusion, optical sensor applications are growing in automotive. Optical sensor robustness matured to the level of safety critical applications like Electrical Power Assisted Steering (EPAS) and Drive-by-Wire by optical linear arrays based systems and Automated Cruise Control (ACC), Lane Change Assist and Driver Classification/Smart Airbag Deployment by camera imagers based systems.

  12. Fault-tolerant Sensor Fusion for Marine Navigation

    DEFF Research Database (Denmark)

    Blanke, Mogens

    2006-01-01

    Reliability of navigation data are critical for steering and manoeuvring control, and in particular so at high speed or in critical phases of a mission. Should faults occur, faulty instruments need be autonomously isolated and faulty information discarded. This paper designs a navigation solution...... where essential navigation information is provided even with multiple faults in instrumentation. The paper proposes a provable correct implementation through auto-generated state-event logics in a supervisory part of the algorithms. Test results from naval vessels document the performance and shows...... events where the fault-tolerant sensor fusion provided uninterrupted navigation data despite temporal instrument defects...

  13. Simultaneous Robust Fault and State Estimation for Linear Discrete-Time Uncertain Systems

    Directory of Open Access Journals (Sweden)

    Feten Gannouni

    2017-01-01

    Full Text Available We consider the problem of robust simultaneous fault and state estimation for linear uncertain discrete-time systems with unknown faults which affect both the state and the observation matrices. Using transformation of the original system, a new robust proportional integral filter (RPIF having an error variance with an optimized guaranteed upper bound for any allowed uncertainty is proposed to improve robust estimation of unknown time-varying faults and to improve robustness against uncertainties. In this study, the minimization problem of the upper bound of the estimation error variance is formulated as a convex optimization problem subject to linear matrix inequalities (LMI for all admissible uncertainties. The proportional and the integral gains are optimally chosen by solving the convex optimization problem. Simulation results are given in order to illustrate the performance of the proposed filter, in particular to solve the problem of joint fault and state estimation.

  14. Unknown input observer based detection of sensor faults in a wind turbine

    DEFF Research Database (Denmark)

    Odgaard, Peter Fogh; Stoustrup, Jakob

    2010-01-01

    In this paper an unknown input observer is designed to detect three different sensor fault scenarios in a specified bench mark model for fault detection and accommodation of wind turbines. In this paper a subset of faults is dealt with, it are faults in the rotor and generator speed sensors as well...... as a converter sensor fault. The proposed scheme detects the speed sensor faults in question within the specified requirements given in the bench mark model, while the converter fault is detected but not within the required time to detect....

  15. Massive Sensor Array Fault Tolerance: Tolerance Mechanism and Fault Injection for Validation

    Directory of Open Access Journals (Sweden)

    Dugan Um

    2010-01-01

    Full Text Available As today's machines become increasingly complex in order to handle intricate tasks, the number of sensors must increase for intelligent operations. Given the large number of sensors, detecting, isolating, and then tolerating faulty sensors is especially important. In this paper, we propose fault tolerance architecture suitable for a massive sensor array often found in highly advanced systems such as autonomous robots. One example is the sensitive skin, a type of massive sensor array. The objective of the sensitive skin is autonomous guidance of machines in unknown environments, requiring elongated operations in a remote site. The entirety of such a system needs to be able to work remotely without human attendance for an extended period of time. To that end, we propose a fault-tolerant architecture whereby component and analytical redundancies are integrated cohesively for effective failure tolerance of a massive array type sensor or sensor system. In addition, we discuss the evaluation results of the proposed tolerance scheme by means of fault injection and validation analysis as a measure of system reliability and performance.

  16. A Denoising Based Autoassociative Model for Robust Sensor Monitoring in Nuclear Power Plants

    Directory of Open Access Journals (Sweden)

    Ahmad Shaheryar

    2016-01-01

    Full Text Available Sensors health monitoring is essentially important for reliable functioning of safety-critical chemical and nuclear power plants. Autoassociative neural network (AANN based empirical sensor models have widely been reported for sensor calibration monitoring. However, such ill-posed data driven models may result in poor generalization and robustness. To address above-mentioned issues, several regularization heuristics such as training with jitter, weight decay, and cross-validation are suggested in literature. Apart from these regularization heuristics, traditional error gradient based supervised learning algorithms for multilayered AANN models are highly susceptible of being trapped in local optimum. In order to address poor regularization and robust learning issues, here, we propose a denoised autoassociative sensor model (DAASM based on deep learning framework. Proposed DAASM model comprises multiple hidden layers which are pretrained greedily in an unsupervised fashion under denoising autoencoder architecture. In order to improve robustness, dropout heuristic and domain specific data corruption processes are exercised during unsupervised pretraining phase. The proposed sensor model is trained and tested on sensor data from a PWR type nuclear power plant. Accuracy, autosensitivity, spillover, and sequential probability ratio test (SPRT based fault detectability metrics are used for performance assessment and comparison with extensively reported five-layer AANN model by Kramer.

  17. Robust filtering and fault detection of switched delay systems

    CERN Document Server

    Wang, Dong; Wang, Wei

    2013-01-01

    Switched delay systems appear in a wide field of applications including networked control systems, power systems, memristive systems. Though the large amount of ideas with respect to such systems have generated, until now, it still lacks a framework to focus on filter design and fault detection issues which are relevant to life safety and property loss. Beginning with the comprehensive coverage of the new developments in the analysis and control synthesis for switched delay systems, the monograph not only provides a systematic approach to designing the filter and detecting the fault of switched delay systems, but it also covers the model reduction issues. Specific topics covered include: (1) Arbitrary switching signal where delay-independent and delay-dependent conditions are presented by proposing a linearization technique. (2) Average dwell time where a weighted Lyapunov function is come up with dealing with filter design and fault detection issues beside taking model reduction problems. The monograph is in...

  18. A Virtual Sensor for Online Fault Detection of Multitooth-Tools

    Directory of Open Access Journals (Sweden)

    Andres Bustillo

    2011-03-01

    Full Text Available The installation of suitable sensors close to the tool tip on milling centres is not possible in industrial environments. It is therefore necessary to design virtual sensors for these machines to perform online fault detection in many industrial tasks. This paper presents a virtual sensor for online fault detection of multitooth tools based on a Bayesian classifier. The device that performs this task applies mathematical models that function in conjunction with physical sensors. Only two experimental variables are collected from the milling centre that performs the machining operations: the electrical power consumption of the feed drive and the time required for machining each workpiece. The task of achieving reliable signals from a milling process is especially complex when multitooth tools are used, because each kind of cutting insert in the milling centre only works on each workpiece during a certain time window. Great effort has gone into designing a robust virtual sensor that can avoid re-calibration due to, e.g., maintenance operations. The virtual sensor developed as a result of this research is successfully validated under real conditions on a milling centre used for the mass production of automobile engine crankshafts. Recognition accuracy, calculated with a k-fold cross validation, had on average 0.957 of true positives and 0.986 of true negatives. Moreover, measured accuracy was 98%, which suggests that the virtual sensor correctly identifies new cases.

  19. A Virtual Sensor for Online Fault Detection of Multitooth-Tools

    Science.gov (United States)

    Bustillo, Andres; Correa, Maritza; Reñones, Anibal

    2011-01-01

    The installation of suitable sensors close to the tool tip on milling centres is not possible in industrial environments. It is therefore necessary to design virtual sensors for these machines to perform online fault detection in many industrial tasks. This paper presents a virtual sensor for online fault detection of multitooth tools based on a Bayesian classifier. The device that performs this task applies mathematical models that function in conjunction with physical sensors. Only two experimental variables are collected from the milling centre that performs the machining operations: the electrical power consumption of the feed drive and the time required for machining each workpiece. The task of achieving reliable signals from a milling process is especially complex when multitooth tools are used, because each kind of cutting insert in the milling centre only works on each workpiece during a certain time window. Great effort has gone into designing a robust virtual sensor that can avoid re-calibration due to, e.g., maintenance operations. The virtual sensor developed as a result of this research is successfully validated under real conditions on a milling centre used for the mass production of automobile engine crankshafts. Recognition accuracy, calculated with a k-fold cross validation, had on average 0.957 of true positives and 0.986 of true negatives. Moreover, measured accuracy was 98%, which suggests that the virtual sensor correctly identifies new cases. PMID:22163766

  20. Advanced neural network-based computational schemes for robust fault diagnosis

    CERN Document Server

    Mrugalski, Marcin

    2014-01-01

    The present book is devoted to problems of adaptation of artificial neural networks to robust fault diagnosis schemes. It presents neural networks-based modelling and estimation techniques used for designing robust fault diagnosis schemes for non-linear dynamic systems. A part of the book focuses on fundamental issues such as architectures of dynamic neural networks, methods for designing of neural networks and fault diagnosis schemes as well as the importance of robustness. The book is of a tutorial value and can be perceived as a good starting point for the new-comers to this field. The book is also devoted to advanced schemes of description of neural model uncertainty. In particular, the methods of computation of neural networks uncertainty with robust parameter estimation are presented. Moreover, a novel approach for system identification with the state-space GMDH neural network is delivered. All the concepts described in this book are illustrated by both simple academic illustrative examples and practica...

  1. Fault-Tolerant Robot Programming through Simulation with Realistic Sensor Models

    Directory of Open Access Journals (Sweden)

    Axel Waggershauser

    2008-11-01

    Full Text Available We introduce a simulation system for mobile robots that allows a realistic interaction of multiple robots in a common environment. The simulated robots are closely modeled after robots from the EyeBot family and have an identical application programmer interface. The simulation supports driving commands at two levels of abstraction as well as numerous sensors such as shaft encoders, infrared distance sensors, and compass. Simulation of on-board digital cameras via synthetic images allows the use of image processing routines for robot control within the simulation. Specific error models for actuators, distance sensors, camera sensor, and wireless communication have been implemented. Progressively increasing error levels for an application program allows for testing and improving its robustness and fault-tolerance.

  2. A New Acoustic Emission Sensor Based Gear Fault Detection Approach

    Directory of Open Access Journals (Sweden)

    Junda Zhu

    2013-01-01

    Full Text Available In order to reduce wind energy costs, prognostics and health management (PHM of wind turbine is needed to ensure the reliability and availability of wind turbines. A gearbox is an important component of a wind turbine. Therefore, developing effective gearbox fault detection tools is important to the PHM of wind turbine. In this paper, a new acoustic emission (AE sensor based gear fault detection approach is presented. This approach combines a heterodyne based frequency reduction technique with time synchronous average (TSA and spectrum kurtosis (SK to process AE sensor signals and extract features as condition indictors for gear fault detection. Heterodyne technique commonly used in communication is first employed to preprocess the AE signals before sampling. By heterodyning, the AE signal frequency is down shifted from several hundred kHz to below 50 kHz. This reduced AE signal sampling rate is comparable to that of vibration signals. The presented approach is validated using seeded gear tooth crack fault tests on a notational split torque gearbox. The approach presented in this paper is physics based and the validation results have showed that it could effectively detect the gear faults.

  3. Robust Forecasting for Energy Efficiency of Wireless Multimedia Sensor Networks.

    Science.gov (United States)

    Wang, Xue; Ma, Jun-Jie; Ding, Liang; Bi, Dao-Wei

    2007-11-15

    An important criterion of wireless sensor network is the energy efficiency inspecified applications. In this wireless multimedia sensor network, the observations arederived from acoustic sensors. Focused on the energy problem of target tracking, this paperproposes a robust forecasting method to enhance the energy efficiency of wirelessmultimedia sensor networks. Target motion information is acquired by acoustic sensornodes while a distributed network with honeycomb configuration is constructed. Thereby,target localization is performed by multiple sensor nodes collaboratively through acousticsignal processing. A novel method, combining autoregressive moving average (ARMA)model and radial basis function networks (RBFNs), is exploited to perform robust targetposition forecasting during target tracking. Then sensor nodes around the target areawakened according to the forecasted target position. With committee decision of sensornodes, target localization is performed in a distributed manner and the uncertainty ofdetection is reduced. Moreover, a sensor-to-observer routing approach of the honeycombmesh network is investigated to solve the data reporting considering the residual energy ofsensor nodes. Target localization and forecasting are implemented in experiments.Meanwhile, sensor node awakening and dynamic routing are evaluated. Experimentalresults verify that energy efficiency of wireless multimedia sensor network is enhanced bythe proposed target tracking method.

  4. Robust fault-sensitive synchronization of a class of nonlinear systems

    International Nuclear Information System (INIS)

    Xu Shi-Yun; Tang Yong; Sun Hua-Dong; Yang Ying; Liu Xian

    2011-01-01

    Aiming at enhancing the quality as well as the reliability of synchronization, this paper is concerned with the fault detection issue within the synchronization process for a class of nonlinear systems in the existence of external disturbances. To handle such problems, the concept of robust fault-sensitive (RFS) synchronization is proposed, and a method of determining such a kind of synchronization is developed. Under the framework of RFS synchronization, the master and the slave systems are robustly synchronized, and at the same time, sensitive to possible faults based on a mixed H − /H ∞ performance. The design of desired output feedback controller is realized by solving a linear matrix inequality, and the fault sensitivity H − index can be optimized via a convex optimization algorithm. A master-slave configuration composed of identical Chua's circuits is adopted as a numerical example to demonstrate the effectiveness and applicability of the analytical results. (general)

  5. A Fault Tolerance Mechanism for On-Road Sensor Networks

    Directory of Open Access Journals (Sweden)

    Lei Feng

    2016-12-01

    Full Text Available On-Road Sensor Networks (ORSNs play an important role in capturing traffic flow data for predicting short-term traffic patterns, driving assistance and self-driving vehicles. However, this kind of network is prone to large-scale communication failure if a few sensors physically fail. In this paper, to ensure that the network works normally, an effective fault-tolerance mechanism for ORSNs which mainly consists of backup on-road sensor deployment, redundant cluster head deployment and an adaptive failure detection and recovery method is proposed. Firstly, based on the N − x principle and the sensors’ failure rate, this paper formulates the backup sensor deployment problem in the form of a two-objective optimization, which explains the trade-off between the cost and fault resumption. In consideration of improving the network resilience further, this paper introduces a redundant cluster head deployment model according to the coverage constraint. Then a common solving method combining integer-continuing and sequential quadratic programming is explored to determine the optimal location of these two deployment problems. Moreover, an Adaptive Detection and Resume (ADR protocol is deigned to recover the system communication through route and cluster adjustment if there is a backup on-road sensor mismatch. The final experiments show that our proposed mechanism can achieve an average 90% recovery rate and reduce the average number of failed sensors at most by 35.7%.

  6. A hybrid robust fault tolerant control based on adaptive joint unscented Kalman filter.

    Science.gov (United States)

    Shabbouei Hagh, Yashar; Mohammadi Asl, Reza; Cocquempot, Vincent

    2017-01-01

    In this paper, a new hybrid robust fault tolerant control scheme is proposed. A robust H ∞ control law is used in non-faulty situation, while a Non-Singular Terminal Sliding Mode (NTSM) controller is activated as soon as an actuator fault is detected. Since a linear robust controller is designed, the system is first linearized through the feedback linearization method. To switch from one controller to the other, a fuzzy based switching system is used. An Adaptive Joint Unscented Kalman Filter (AJUKF) is used for fault detection and diagnosis. The proposed method is based on the simultaneous estimation of the system states and parameters. In order to show the efficiency of the proposed scheme, a simulated 3-DOF robotic manipulator is used. Copyright © 2016 ISA. Published by Elsevier Ltd. All rights reserved.

  7. A Fault Diagnostic Method for Position Sensor of Switched Reluctance Wind Generator

    DEFF Research Database (Denmark)

    Wang, Chao; Liu, Xiao; Liu, Hui

    2016-01-01

    Fast and accurate fault diagnosis of the position sensor is of great significance to ensure the reliability as well as sensor fault tolerant operation of the Switched Reluctance Wind Generator (SRWG). This paper presents a fault diagnostic scheme for a SRWG based on the residual between the estim...

  8. Robust fault detection in bond graph framework using interval analysis and Fourier-Motzkin elimination technique

    Science.gov (United States)

    Jha, Mayank Shekhar; Chatti, Nizar; Declerck, Philippe

    2017-09-01

    This paper addresses the fault diagnosis problem of uncertain systems in the context of Bond Graph modelling technique. The main objective is to enhance the fault detection step based on Interval valued Analytical Redundancy Relations (named I-ARR) in order to overcome the problems related to false alarms, missed alarms and robustness issues. These I-ARRs are a set of fault indicators that generate the interval bounds called thresholds. A fault is detected once the nominal residuals (point valued part of I-ARRs) exceed the thresholds. However, the existing fault detection method is limited to parametric faults and it presents various limitations with regards to estimation of measurement signal derivatives, to which I-ARRs are sensitive. The novelties and scientific interest of the proposed methodology are: (1) to improve the accuracy of the measurements derivatives estimation by using a dedicated sliding mode differentiator proposed in this work, (2) to suitably integrate the Fourier-Motzkin Elimination (FME) technique within the I-ARRs based diagnosis so that measurements faults can be detected successfully. The latter provides interval bounds over the derivatives which are included in the thresholds. The proposed methodology is studied under various scenarios (parametric and measurement faults) via simulations over a mechatronic torsion bar system.

  9. Fault diagnostics for turbo-shaft engine sensors based on a simplified on-board model.

    Science.gov (United States)

    Lu, Feng; Huang, Jinquan; Xing, Yaodong

    2012-01-01

    Combining a simplified on-board turbo-shaft model with sensor fault diagnostic logic, a model-based sensor fault diagnosis method is proposed. The existing fault diagnosis method for turbo-shaft engine key sensors is mainly based on a double redundancies technique, and this can't be satisfied in some occasions as lack of judgment. The simplified on-board model provides the analytical third channel against which the dual channel measurements are compared, while the hardware redundancy will increase the structure complexity and weight. The simplified turbo-shaft model contains the gas generator model and the power turbine model with loads, this is built up via dynamic parameters method. Sensor fault detection, diagnosis (FDD) logic is designed, and two types of sensor failures, such as the step faults and the drift faults, are simulated. When the discrepancy among the triplex channels exceeds a tolerance level, the fault diagnosis logic determines the cause of the difference. Through this approach, the sensor fault diagnosis system achieves the objectives of anomaly detection, sensor fault diagnosis and redundancy recovery. Finally, experiments on this method are carried out on a turbo-shaft engine, and two types of faults under different channel combinations are presented. The experimental results show that the proposed method for sensor fault diagnostics is efficient.

  10. Fault Diagnostics for Turbo-Shaft Engine Sensors Based on a Simplified On-Board Model

    Directory of Open Access Journals (Sweden)

    Yaodong Xing

    2012-08-01

    Full Text Available Combining a simplified on-board turbo-shaft model with sensor fault diagnostic logic, a model-based sensor fault diagnosis method is proposed. The existing fault diagnosis method for turbo-shaft engine key sensors is mainly based on a double redundancies technique, and this can’t be satisfied in some occasions as lack of judgment. The simplified on-board model provides the analytical third channel against which the dual channel measurements are compared, while the hardware redundancy will increase the structure complexity and weight. The simplified turbo-shaft model contains the gas generator model and the power turbine model with loads, this is built up via dynamic parameters method. Sensor fault detection, diagnosis (FDD logic is designed, and two types of sensor failures, such as the step faults and the drift faults, are simulated. When the discrepancy among the triplex channels exceeds a tolerance level, the fault diagnosis logic determines the cause of the difference. Through this approach, the sensor fault diagnosis system achieves the objectives of anomaly detection, sensor fault diagnosis and redundancy recovery. Finally, experiments on this method are carried out on a turbo-shaft engine, and two types of faults under different channel combinations are presented. The experimental results show that the proposed method for sensor fault diagnostics is efficient.

  11. Robust Fault Tolerant Control for a Class of Time-Delay Systems with Multiple Disturbances

    Directory of Open Access Journals (Sweden)

    Songyin Cao

    2013-01-01

    Full Text Available A robust fault tolerant control (FTC approach is addressed for a class of nonlinear systems with time delay, actuator faults, and multiple disturbances. The first part of the multiple disturbances is supposed to be an uncertain modeled disturbance and the second one represents a norm-bounded variable. First, a composite observer is designed to estimate the uncertain modeled disturbance and actuator fault simultaneously. Then, an FTC strategy consisting of disturbance observer based control (DOBC, fault accommodation, and a mixed H2/H∞ controller is constructed to reconfigure the considered systems with disturbance rejection and attenuation performance. Finally, simulations for a flight control system are given to show the efficiency of the proposed approach.

  12. Robust recurrent neural network modeling for software fault detection and correction prediction

    International Nuclear Information System (INIS)

    Hu, Q.P.; Xie, M.; Ng, S.H.; Levitin, G.

    2007-01-01

    Software fault detection and correction processes are related although different, and they should be studied together. A practical approach is to apply software reliability growth models to model fault detection, and fault correction process is assumed to be a delayed process. On the other hand, the artificial neural networks model, as a data-driven approach, tries to model these two processes together with no assumptions. Specifically, feedforward backpropagation networks have shown their advantages over analytical models in fault number predictions. In this paper, the following approach is explored. First, recurrent neural networks are applied to model these two processes together. Within this framework, a systematic networks configuration approach is developed with genetic algorithm according to the prediction performance. In order to provide robust predictions, an extra factor characterizing the dispersion of prediction repetitions is incorporated into the performance function. Comparisons with feedforward neural networks and analytical models are developed with respect to a real data set

  13. Multiple incipient sensor faults diagnosis with application to high-speed railway traction devices.

    Science.gov (United States)

    Wu, Yunkai; Jiang, Bin; Lu, Ningyun; Yang, Hao; Zhou, Yang

    2017-03-01

    This paper deals with the problem of incipient fault diagnosis for a class of Lipschitz nonlinear systems with sensor biases and explores further results of total measurable fault information residual (ToMFIR). Firstly, state and output transformations are introduced to transform the original system into two subsystems. The first subsystem is subject to system disturbances and free from sensor faults, while the second subsystem contains sensor faults but without any system disturbances. Sensor faults in the second subsystem are then formed as actuator faults by using a pseudo-actuator based approach. Since the effects of system disturbances on the residual are completely decoupled, multiple incipient sensor faults can be detected by constructing ToMFIR, and the fault detectability condition is then derived for discriminating the detectable incipient sensor faults. Further, a sliding-mode observers (SMOs) based fault isolation scheme is designed to guarantee accurate isolation of multiple sensor faults. Finally, simulation results conducted on a CRH2 high-speed railway traction device are given to demonstrate the effectiveness of the proposed approach. Copyright © 2016 ISA. Published by Elsevier Ltd. All rights reserved.

  14. Fault Tolerance in ZigBee Wireless Sensor Networks

    Science.gov (United States)

    Alena, Richard; Gilstrap, Ray; Baldwin, Jarren; Stone, Thom; Wilson, Pete

    2011-01-01

    Wireless sensor networks (WSN) based on the IEEE 802.15.4 Personal Area Network standard are finding increasing use in the home automation and emerging smart energy markets. The network and application layers, based on the ZigBee 2007 PRO Standard, provide a convenient framework for component-based software that supports customer solutions from multiple vendors. This technology is supported by System-on-a-Chip solutions, resulting in extremely small and low-power nodes. The Wireless Connections in Space Project addresses the aerospace flight domain for both flight-critical and non-critical avionics. WSNs provide the inherent fault tolerance required for aerospace applications utilizing such technology. The team from Ames Research Center has developed techniques for assessing the fault tolerance of ZigBee WSNs challenged by radio frequency (RF) interference or WSN node failure.

  15. An evidential sensor fusion method in fault diagnosis

    Directory of Open Access Journals (Sweden)

    Wen Jiang

    2016-03-01

    Full Text Available Dempster–Shafer evidence theory is widely used in information fusion. However, it may lead to an unreasonable result when dealing with high conflict evidence. In order to solve this problem, we put forward a new method based on the credibility of evidence. First, a novel belief entropy, Deng entropy, is applied to measure the information volume of the evidence and then the discounting coefficients of each evidence are obtained. Finally, weighted averaging the evidence in the system, the Dempster combination rule was used to realize information fusion. A weighted averaging combination role is presented for multi-sensor data fusion in fault diagnosis. It seems more reasonable than before using the new belief function to determine the weight. A numerical example is given to illustrate that the proposed rule is more effective to perform fault diagnosis than classical evidence theory in fusing multi-symptom domains.

  16. Robust fault detection of linear systems using a computationally efficient set-membership method

    DEFF Research Database (Denmark)

    Tabatabaeipour, Mojtaba; Bak, Thomas

    2014-01-01

    In this paper, a computationally efficient set-membership method for robust fault detection of linear systems is proposed. The method computes an interval outer-approximation of the output of the system that is consistent with the model, the bounds on noise and disturbance, and the past measureme...... is trivially parallelizable. The method is demonstrated for fault detection of a hydraulic pitch actuator of a wind turbine. We show the effectiveness of the proposed method by comparing our results with two zonotope-based set-membership methods....

  17. Robust fault detection of turbofan engines subject to adaptive controllers via a Total Measurable Fault Information Residual (ToMFIR) technique.

    Science.gov (United States)

    Chen, Wen; Chowdhury, Fahmida N; Djuric, Ana; Yeh, Chih-Ping

    2014-09-01

    This paper provides a new design of robust fault detection for turbofan engines with adaptive controllers. The critical issue is that the adaptive controllers can depress the faulty effects such that the actual system outputs remain the pre-specified values, making it difficult to detect faults/failures. To solve this problem, a Total Measurable Fault Information Residual (ToMFIR) technique with the aid of system transformation is adopted to detect faults in turbofan engines with adaptive controllers. This design is a ToMFIR-redundancy-based robust fault detection. The ToMFIR is first introduced and existing results are also summarized. The Detailed design process of the ToMFIRs is presented and a turbofan engine model is simulated to verify the effectiveness of the proposed ToMFIR-based fault-detection strategy. Copyright © 2013 ISA. Published by Elsevier Ltd. All rights reserved.

  18. Developing a robust wireless sensor network structure for environmental sensing

    Science.gov (United States)

    Zhang, Z.; Oroza, C.; Glaser, S. D.; Bales, R. C.; Conklin, M. H.

    2013-12-01

    The American River Hydrologic Observatory is being strategically deployed as a real-time ground-based measurement network that delivers accurate and timely information on snow conditions and other hydrologic attributes with a previously unheard of granularity of time and space. The basin-scale network involves 18 sub-networks set out at physiographically representative locations spanning the seasonally snow-covered half of the 5000 km2 American river basin. Each sub-network, covering about a 1-km2 area, consists of 10 wirelessly networked sensing nodes that continuously measure and telemeter temperature, and snow depth; plus selected locations are equipped with sensors for relative humidity, solar radiation, and soil moisture at several depths. The sensor locations were chosen to maximize the variance sampled for snow depth within the basin. Network design and deployment involves an iterative but efficient process. After sensor-station locations are determined, a robust network of interlinking sensor stations and signal repeaters must be constructed to route sensor data to a central base station with a two-way communicable data uplink. Data can then be uploaded from site to remote servers in real time through satellite and cell modems. Signal repeaters are placed for robustness of a self-healing network with redundant signal paths to the base station. Manual, trial-and-error heuristic approaches for node placement are inefficient and labor intensive. In that approach field personnel must restructure the network in real time and wait for new network statistics to be calculated at the base station before finalizing a placement, acting without knowledge of the global topography or overall network structure. We show how digital elevation plus high-definition aerial photographs to give foliage coverage can optimize planning of signal repeater placements and guarantee a robust network structure prior to the physical deployment. We can also 'stress test' the final network

  19. Monitoring and diagnosis for sensor fault detection using GMDH methodology

    International Nuclear Information System (INIS)

    Goncalves, Iraci Martinez Pereira

    2006-01-01

    The fault detection and diagnosis system is an Operator Support System dedicated to specific functions that alerts operators to sensors and actuators fault problems, and guide them in the diagnosis before the normal alarm limits are reached. Operator Support Systems appears to reduce panels complexity caused by the increase of the available information in nuclear power plants control room. In this work a Monitoring and Diagnosis System was developed based on the GMDH (Group Method of Data Handling) methodology. The methodology was applied to the IPEN research reactor IEA-R1. The system performs the monitoring, comparing GMDH model calculated values with measured values. The methodology developed was firstly applied in theoretical models: a heat exchanger model and an IPEN reactor theoretical model. The results obtained with theoretical models gave a base to methodology application to the actual reactor operation data. Three GMDH models were developed for actual operation data monitoring: the first one using just the thermal process variables, the second one was developed considering also some nuclear variables, and the third GMDH model considered all the reactor variables. The three models presented excellent results, showing the methodology utilization viability in monitoring the operation data. The comparison between the three developed models results also shows the methodology capacity to choose by itself the best set of input variables for the model optimization. For the system diagnosis implementation, faults were simulated in the actual temperature variable values by adding a step change. The fault values correspond to a typical temperature descalibration and the result of monitoring faulty data was then used to build a simple diagnosis system based on fuzzy logic. (author)

  20. Robust reconfigurable control for parametric and additive faults with FDI uncertainties

    DEFF Research Database (Denmark)

    Stoustrup, Jakob; Yang, Zhenyu

    2000-01-01

    From the system recoverable point of view, this paper discusses robust reconfigurable control synthesis for LTI systems and a class of nonlinear control systems with parametric and additive faults as well as derivations generated by FDI algorithms. By following the model-matching strategy......, an augmented optimal control problem is constructed based on the considered faulty and fictitious nominal systems, such that the robust control design techniques, such as H-infinity control and mu synthesis, can be employed for the reconfigurable control design....

  1. Discrete Kalman Filter based Sensor Fusion for Robust Accessibility Interfaces

    International Nuclear Information System (INIS)

    Ghersi, I; Miralles, M T; Mariño, M

    2016-01-01

    Human-machine interfaces have evolved, benefiting from the growing access to devices with superior, embedded signal-processing capabilities, as well as through new sensors that allow the estimation of movements and gestures, resulting in increasingly intuitive interfaces. In this context, sensor fusion for the estimation of the spatial orientation of body segments allows to achieve more robust solutions, overcoming specific disadvantages derived from the use of isolated sensors, such as the sensitivity of magnetic-field sensors to external influences, when used in uncontrolled environments. In this work, a method for the combination of image-processing data and angular-velocity registers from a 3D MEMS gyroscope, through a Discrete-time Kalman Filter, is proposed and deployed as an alternate user interface for mobile devices, in which an on-screen pointer is controlled with head movements. Results concerning general performance of the method are presented, as well as a comparative analysis, under a dedicated test application, with results from a previous version of this system, in which the relative-orientation information was acquired directly from MEMS sensors (3D magnetometer-accelerometer). These results show an improved response for this new version of the pointer, both in terms of precision and response time, while keeping many of the benefits that were highlighted for its predecessor, giving place to a complementary method for signal acquisition that can be used as an alternative-input device, as well as for accessibility solutions. (paper)

  2. Sensor and Actuator Fault-Hiding Reconfigurable Control Design for a Four-Tank System Benchmark

    DEFF Research Database (Denmark)

    Hameed, Ibrahim; El-Madbouly, Esam I; Abdo, Mohamed I

    2015-01-01

    Invariant (LTI) system where virtual sensors and virtual actuators are used to correct faulty performance through the use of a pre-fault performance. Simulation results showed that the developed approach can handle different types of faults and able to completely and instantly recover the original system......Fault detection and compensation plays a key role to fulfill high demands for performance and security in today's technological systems. In this paper, a fault-hiding (i.e., tolerant) control scheme that detects and compensates for actuator and sensor faults in a four-tank system benchmark...

  3. Study of the intelligent control robustness with respect to radiations induced faults

    International Nuclear Information System (INIS)

    Cheynet, Ph.

    1999-01-01

    The so-called intelligent control techniques, such as Artificial Neural Networks and Fuzzy Logic, are considered as being potentially robust. Their digital implementation gives compact and powerful solutions to some problems difficult to be tackled by classical techniques. Such approaches might be used for applications working in harsh environment (nuclear and space). The aim of this thesis is to study the robustness of artificial neural networks and fuzzy logic against Single Event Upset faults, in order to evaluate their viability and their efficiency for onboard spacecraft processes. A set of experiments have been performed on a neural network and a fuzzy controller, both implementing real space applications: texture analysis from satellite images and wheel control of a martian rover. An original method, allowing to increase the recognition rate of any artificial neural network has been developed and used on the studied network. Digital architectures implementing the two studied techniques in this thesis, have been boarded on two scientific satellites. One is in flight since one year, the other will be launched in the end of 1999. Obtained results, both from software simulations, hardware fault injections or particle accelerator tests, show that intelligent control techniques have a significant robustness against Single Event Upset faults. Data issued from the flight experiment confirm these properties, showing that some onboard spacecraft processes can be reliably executed by digital artificial neural networks. (author)

  4. Computation of a Reference Model for Robust Fault Detection and Isolation Residual Generation

    Directory of Open Access Journals (Sweden)

    Emmanuel Mazars

    2008-01-01

    Full Text Available This paper considers matrix inequality procedures to address the robust fault detection and isolation (FDI problem for linear time-invariant systems subject to disturbances, faults, and polytopic or norm-bounded uncertainties. We propose a design procedure for an FDI filter that aims to minimize a weighted combination of the sensitivity of the residual signal to disturbances and modeling errors, and the deviation of the faults to residual dynamics from a fault to residual reference model, using the ℋ∞-norm as a measure. A key step in our procedure is the design of an optimal fault reference model. We show that the optimal design requires the solution of a quadratic matrix inequality (QMI optimization problem. Since the solution of the optimal problem is intractable, we propose a linearization technique to derive a numerically tractable suboptimal design procedure that requires the solution of a linear matrix inequality (LMI optimization. A jet engine example is employed to demonstrate the effectiveness of the proposed approach.

  5. A robust trust establishment scheme for wireless sensor networks.

    Science.gov (United States)

    Ishmanov, Farruh; Kim, Sung Won; Nam, Seung Yeob

    2015-03-23

    Security techniques like cryptography and authentication can fail to protect a network once a node is compromised. Hence, trust establishment continuously monitors and evaluates node behavior to detect malicious and compromised nodes. However, just like other security schemes, trust establishment is also vulnerable to attack. Moreover, malicious nodes might misbehave intelligently to trick trust establishment schemes. Unfortunately, attack-resistance and robustness issues with trust establishment schemes have not received much attention from the research community. Considering the vulnerability of trust establishment to different attacks and the unique features of sensor nodes in wireless sensor networks, we propose a lightweight and robust trust establishment scheme. The proposed trust scheme is lightweight thanks to a simple trust estimation method. The comprehensiveness and flexibility of the proposed trust estimation scheme make it robust against different types of attack and misbehavior. Performance evaluation under different types of misbehavior and on-off attacks shows that the detection rate of the proposed trust mechanism is higher and more stable compared to other trust mechanisms.

  6. A Robust Trust Establishment Scheme for Wireless Sensor Networks

    Directory of Open Access Journals (Sweden)

    Farruh Ishmanov

    2015-03-01

    Full Text Available Security techniques like cryptography and authentication can fail to protect a network once a node is compromised. Hence, trust establishment continuously monitors and evaluates node behavior to detect malicious and compromised nodes. However, just like other security schemes, trust establishment is also vulnerable to attack. Moreover, malicious nodes might misbehave intelligently to trick trust establishment schemes. Unfortunately, attack-resistance and robustness issues with trust establishment schemes have not received much attention from the research community. Considering the vulnerability of trust establishment to different attacks and the unique features of sensor nodes in wireless sensor networks, we propose a lightweight and robust trust establishment scheme. The proposed trust scheme is lightweight thanks to a simple trust estimation method. The comprehensiveness and flexibility of the proposed trust estimation scheme make it robust against different types of attack and misbehavior. Performance evaluation under different types of misbehavior and on-off attacks shows that the detection rate of the proposed trust mechanism is higher and more stable compared to other trust mechanisms.

  7. An Ensemble of HMMs for Cognitive Fault Detection in Distributed Sensor Networks

    OpenAIRE

    Roveri , Manuel; Trovò , Francesco

    2014-01-01

    Part 3: Social Media and Mobile Applications of AI; International audience; Distributed sensor networks working in harsh environmental conditions can suffer from permanent or transient faults affecting the embedded electronics or the sensors. Fault Diagnosis Systems (FDSs) have been widely studied in the literature to detect, isolate, identify, and possibly accommodate faults. Recently introduced cognitive FDSs, which represent a novel generation of FDSs, are characterized by the capability t...

  8. Multi-Level Wavelet Shannon Entropy-Based Method for Single-Sensor Fault Location

    Directory of Open Access Journals (Sweden)

    Qiaoning Yang

    2015-10-01

    Full Text Available In actual application, sensors are prone to failure because of harsh environments, battery drain, and sensor aging. Sensor fault location is an important step for follow-up sensor fault detection. In this paper, two new multi-level wavelet Shannon entropies (multi-level wavelet time Shannon entropy and multi-level wavelet time-energy Shannon entropy are defined. They take full advantage of sensor fault frequency distribution and energy distribution across multi-subband in wavelet domain. Based on the multi-level wavelet Shannon entropy, a method is proposed for single sensor fault location. The method firstly uses a criterion of maximum energy-to-Shannon entropy ratio to select the appropriate wavelet base for signal analysis. Then multi-level wavelet time Shannon entropy and multi-level wavelet time-energy Shannon entropy are used to locate the fault. The method is validated using practical chemical gas concentration data from a gas sensor array. Compared with wavelet time Shannon entropy and wavelet energy Shannon entropy, the experimental results demonstrate that the proposed method can achieve accurate location of a single sensor fault and has good anti-noise ability. The proposed method is feasible and effective for single-sensor fault location.

  9. Mathematical modeling of a steam generator for sensor fault detection

    International Nuclear Information System (INIS)

    Prock, J.

    1988-01-01

    A dynamic model for a nuclear power plant steam generator (vertical, preheated, U-tube recirculation-type) is formulated as a sixth-order nonlinear system. The model integrates nodal mass and energy balances for the primary water, the U-tube metal and the secondary water and steam. The downcomer flow is determined by a static balance of momentum. The mathematical system is solved using transient input data from the Philippsburg 2 (FRG) nuclear power plant. The results of the calculation are compared with actual measured values. The proposed model provides a low-cost tool for the automatic control and simulation of the steam generating process. The ''parity-space'' algorithm is used to demonstrate the applicability of the mathematical model for sensor fault detection and identification purposes. This technique provides a powerful means of generating temporal analytical redundancy between sensor signals. It demonstrates good detection rates of sensor errors using relatively few steps of scanning time and allows the reconfiguration of faulty signals. (author)

  10. A Novel Online Data-Driven Algorithm for Detecting UAV Navigation Sensor Faults

    OpenAIRE

    Rui Sun; Qi Cheng; Guanyu Wang; Washington Yotto Ochieng

    2017-01-01

    The use of Unmanned Aerial Vehicles (UAVs) has increased significantly in recent years. On-board integrated navigation sensors are a key component of UAVs’ flight control systems and are essential for flight safety. In order to ensure flight safety, timely and effective navigation sensor fault detection capability is required. In this paper, a novel data-driven Adaptive Neuron Fuzzy Inference System (ANFIS)-based approach is presented for the detection of on-board navigation sensor faults in ...

  11. Fault Detection for Large-Scale Railway Maintenance Equipment Base on Wireless Sensor Networks

    Directory of Open Access Journals (Sweden)

    Junfu Yu

    2014-04-01

    Full Text Available Focusing on the fault detection application for large-scale railway maintenance equipment with the specialties of low-cost, energy efficiency, collecting data of the function units. This paper proposed energy efficiency, convenient installation fault detection application using Sigsbee wireless sensor networks, which Sigsbee is the most widely used protocol based on IEEE 802.15.4. This paper proposed a systematic application from hardware design using STM32F103 chips as processer, to software system. Fault detection application is the basic part of the fault diagnose system, wireless sensor nodes of the fault detection application with different kinds of sensors for verities function units communication by Sigsbee to collecting and sending basic working status data to the home gateway, then data will be sent to the fault diagnose system.

  12. A novel approach for fault detection and classification of the thermocouple sensor in Nuclear Power Plant using Singular Value Decomposition and Symbolic Dynamic Filter

    International Nuclear Information System (INIS)

    Mandal, Shyamapada; Santhi, B.; Sridhar, S.; Vinolia, K.; Swaminathan, P.

    2017-01-01

    Highlights: • A novel approach to classify the fault pattern using data-driven methods. • Application of robust reconstruction method (SVD) to identify the faulty sensor. • Analysing fault pattern for plenty of sensors using SDF with less time complexity. • An efficient data-driven model is designed to the false and missed alarms. - Abstract: A mathematical model with two layers is developed using data-driven methods for thermocouple sensor fault detection and classification in Nuclear Power Plants (NPP). The Singular Value Decomposition (SVD) based method is applied to detect the faulty sensor from a data set of all sensors, at the first layer. In the second layer, the Symbolic Dynamic Filter (SDF) is employed to classify the fault pattern. If SVD detects any false fault, it is also re-evaluated by the SDF, i.e., the model has two layers of checking to balance the false alarms. The proposed fault detection and classification method is compared with the Principal Component Analysis. Two case studies are taken from Fast Breeder Test Reactor (FBTR) to prove the efficiency of the proposed method.

  13. A Design of Finite Memory Residual Generation Filter for Sensor Fault Detection

    Directory of Open Access Journals (Sweden)

    Kim Pyung Soo

    2017-04-01

    Full Text Available In the current paper, a residual generation filter with finite memory structure is proposed for sensor fault detection. The proposed finite memory residual generation filter provides the residual by real-time filtering of fault vector using only the most recent finite measurements and inputs on the window. It is shown that the residual given by the proposed residual generation filter provides the exact fault for noisefree systems. The proposed residual generation filter is specified to the digital filter structure for the amenability to hardware implementation. Finally, to illustrate the capability of the proposed residual generation filter, extensive simulations are performed for the discretized DC motor system with two types of sensor faults, incipient soft bias-type fault and abrupt bias-type fault. In particular, according to diverse noise levels and windows lengths, meaningful simulation results are given for the abrupt bias-type fault.

  14. A Survey on Distributed Filtering and Fault Detection for Sensor Networks

    Directory of Open Access Journals (Sweden)

    Hongli Dong

    2014-01-01

    Full Text Available In recent years, theoretical and practical research on large-scale networked systems has gained an increasing attention from multiple disciplines including engineering, computer science, and mathematics. Lying in the core part of the area are the distributed estimation and fault detection problems that have recently been attracting growing research interests. In particular, an urgent need has arisen to understand the effects of distributed information structures on filtering and fault detection in sensor networks. In this paper, a bibliographical review is provided on distributed filtering and fault detection problems over sensor networks. The algorithms employed to study the distributed filtering and detection problems are categorised and then discussed. In addition, some recent advances on distributed detection problems for faulty sensors and fault events are also summarized in great detail. Finally, we conclude the paper by outlining future research challenges for distributed filtering and fault detection for sensor networks.

  15. A Hybrid Fault-Tolerant Strategy for Severe Sensor Failure Scenarios in Late-Stage Offshore DFIG-WT

    Directory of Open Access Journals (Sweden)

    Wei Li

    2017-12-01

    Full Text Available As the phase current sensors and rotor speed/position sensor are prone to fail in the late stage of an offshore doubly-fed induction generator based wind turbine (DFIG-WT, this paper investigates a hybrid fault-tolerant strategy for a severe sensor failure scenario. The phase current sensors in the back-to-back (BTB converter and the speed/position sensor are in the faulty states simultaneously. Based on the 7th-order doubly-fed induction generator (DFIG dynamic state space model, the extended Kalman filter (EKF algorithm is applied for rotor speed and position estimation. In addition, good robustness of this sensorless control algorithm to system uncertainties and measurement disturbances is presented. Besides, a single DC-link current sensor based phase current reconstruction scheme is utilized for deriving the phase current information according to the switching states. A duty ratio adjustment strategy is proposed to avoid missing the sampling points in a switching period, which is simple to implement. Furthermore, the additional active time of the targeted nonzero switching states is complemented so that the reference voltage vector remains in the same position as that before duty ratio adjustment. The validity of the proposed hybrid fault-tolerant sensorless control strategy is demonstrated by simulation results in Matlab/Simulink2017a by considering harsh operating environments.

  16. Application of D-S Evidence Fusion Method in the Fault Detection of Temperature Sensor

    Directory of Open Access Journals (Sweden)

    Zheng Dou

    2014-01-01

    Full Text Available Due to the complexity and dangerousness of drying process, the fault detection of temperature sensor is very difficult and dangerous in actual working practice and the detection effectiveness is not satisfying. For this problem, in this paper, based on the idea of information fusion and the requirements of D-S evidence method, a D-S evidence fusion structure with two layers was introduced to detect the temperature sensor fault in drying process. The first layer was data layer to establish the basic belief assignment function of evidence which could be realized by BP Neural Network. The second layer was decision layer to detect and locate the sensor fault which could be realized by D-S evidence fusion method. According to the numerical simulation results, the working conditions of sensors could be described effectively and accurately by this method, so that it could be used to detect and locate the sensor fault.

  17. Sensor Selection for Aircraft Engine Performance Estimation and Gas Path Fault Diagnostics

    Science.gov (United States)

    Simon, Donald L.; Rinehart, Aidan W.

    2016-01-01

    This paper presents analytical techniques for aiding system designers in making aircraft engine health management sensor selection decisions. The presented techniques, which are based on linear estimation and probability theory, are tailored for gas turbine engine performance estimation and gas path fault diagnostics applications. They enable quantification of the performance estimation and diagnostic accuracy offered by different candidate sensor suites. For performance estimation, sensor selection metrics are presented for two types of estimators including a Kalman filter and a maximum a posteriori estimator. For each type of performance estimator, sensor selection is based on minimizing the theoretical sum of squared estimation errors in health parameters representing performance deterioration in the major rotating modules of the engine. For gas path fault diagnostics, the sensor selection metric is set up to maximize correct classification rate for a diagnostic strategy that performs fault classification by identifying the fault type that most closely matches the observed measurement signature in a weighted least squares sense. Results from the application of the sensor selection metrics to a linear engine model are presented and discussed. Given a baseline sensor suite and a candidate list of optional sensors, an exhaustive search is performed to determine the optimal sensor suites for performance estimation and fault diagnostics. For any given sensor suite, Monte Carlo simulation results are found to exhibit good agreement with theoretical predictions of estimation and diagnostic accuracies.

  18. Neural network-based robust actuator fault diagnosis for a non-linear multi-tank system.

    Science.gov (United States)

    Mrugalski, Marcin; Luzar, Marcel; Pazera, Marcin; Witczak, Marcin; Aubrun, Christophe

    2016-03-01

    The paper is devoted to the problem of the robust actuator fault diagnosis of the dynamic non-linear systems. In the proposed method, it is assumed that the diagnosed system can be modelled by the recurrent neural network, which can be transformed into the linear parameter varying form. Such a system description allows developing the designing scheme of the robust unknown input observer within H∞ framework for a class of non-linear systems. The proposed approach is designed in such a way that a prescribed disturbance attenuation level is achieved with respect to the actuator fault estimation error, while guaranteeing the convergence of the observer. The application of the robust unknown input observer enables actuator fault estimation, which allows applying the developed approach to the fault tolerant control tasks. Copyright © 2016 ISA. Published by Elsevier Ltd. All rights reserved.

  19. Improving Electronic Sensor Reliability by Robust Outlier Screening

    Directory of Open Access Journals (Sweden)

    Federico Cuesta

    2013-10-01

    Full Text Available Electronic sensors are widely used in different application areas, and in some of them, such as automotive or medical equipment, they must perform with an extremely low defect rate. Increasing reliability is paramount. Outlier detection algorithms are a key component in screening latent defects and decreasing the number of customer quality incidents (CQIs. This paper focuses on new spatial algorithms (Good Die in a Bad Cluster with Statistical Bins (GDBC SB and Bad Bin in a Bad Cluster (BBBC and an advanced outlier screening method, called Robust Dynamic Part Averaging Testing (RDPAT, as well as two practical improvements, which significantly enhance existing algorithms. Those methods have been used in production in Freescale® Semiconductor probe factories around the world for several years. Moreover, a study was conducted with production data of 289,080 dice with 26 CQIs to determine and compare the efficiency and effectiveness of all these algorithms in identifying CQIs.

  20. A mixture model for robust registration in Kinect sensor

    Science.gov (United States)

    Peng, Li; Zhou, Huabing; Zhu, Shengguo

    2018-03-01

    The Microsoft Kinect sensor has been widely used in many applications, but it suffers from the drawback of low registration precision between color image and depth image. In this paper, we present a robust method to improve the registration precision by a mixture model that can handle multiply images with the nonparametric model. We impose non-parametric geometrical constraints on the correspondence, as a prior distribution, in a reproducing kernel Hilbert space (RKHS).The estimation is performed by the EM algorithm which by also estimating the variance of the prior model is able to obtain good estimates. We illustrate the proposed method on the public available dataset. The experimental results show that our approach outperforms the baseline methods.

  1. Virtual Sensor of Surface Electromyography in a New Extensive Fault-Tolerant Classification System.

    Science.gov (United States)

    de Moura, Karina de O A; Balbinot, Alexandre

    2018-05-01

    A few prosthetic control systems in the scientific literature obtain pattern recognition algorithms adapted to changes that occur in the myoelectric signal over time and, frequently, such systems are not natural and intuitive. These are some of the several challenges for myoelectric prostheses for everyday use. The concept of the virtual sensor, which has as its fundamental objective to estimate unavailable measures based on other available measures, is being used in other fields of research. The virtual sensor technique applied to surface electromyography can help to minimize these problems, typically related to the degradation of the myoelectric signal that usually leads to a decrease in the classification accuracy of the movements characterized by computational intelligent systems. This paper presents a virtual sensor in a new extensive fault-tolerant classification system to maintain the classification accuracy after the occurrence of the following contaminants: ECG interference, electrode displacement, movement artifacts, power line interference, and saturation. The Time-Varying Autoregressive Moving Average (TVARMA) and Time-Varying Kalman filter (TVK) models are compared to define the most robust model for the virtual sensor. Results of movement classification were presented comparing the usual classification techniques with the method of the degraded signal replacement and classifier retraining. The experimental results were evaluated for these five noise types in 16 surface electromyography (sEMG) channel degradation case studies. The proposed system without using classifier retraining techniques recovered of mean classification accuracy was of 4% to 38% for electrode displacement, movement artifacts, and saturation noise. The best mean classification considering all signal contaminants and channel combinations evaluated was the classification using the retraining method, replacing the degraded channel by the virtual sensor TVARMA model. This method

  2. Model based Fault Detection and Isolation for Driving Motors of a Ground Vehicle

    Directory of Open Access Journals (Sweden)

    Young-Joon Kim

    2016-04-01

    Full Text Available This paper proposes model based current sensor and position sensor fault detection and isolation algorithm for driving motor of In-wheel independent drive electric vehicle. From low level perspective, fault diagnosis conducted and analyzed to enhance robustness and stability. Composing state equation of interior permanent magnet synchronous motor (IPMSM, current sensor fault and position sensor fault diagnosed with parity equation. Validation and usefulness of algorithm confirmed based on IPMSM fault occurrence simulation data.

  3. RUASN: A Robust User Authentication Framework for Wireless Sensor Networks

    Directory of Open Access Journals (Sweden)

    Hoon-Jae Lee

    2011-05-01

    Full Text Available In recent years, wireless sensor networks (WSNs have been considered as a potential solution for real-time monitoring applications and these WSNs have potential practical impact on next generation technology too. However, WSNs could become a threat if suitable security is not considered before the deployment and if there are any loopholes in their security, which might open the door for an attacker and hence, endanger the application. User authentication is one of the most important security services to protect WSN data access from unauthorized users; it should provide both mutual authentication and session key establishment services. This paper proposes a robust user authentication framework for wireless sensor networks, based on a two-factor (password and smart card concept. This scheme facilitates many services to the users such as user anonymity, mutual authentication, secure session key establishment and it allows users to choose/update their password regularly, whenever needed. Furthermore, we have provided the formal verification using Rubin logic and compare RUASN with many existing schemes. As a result, we found that the proposed scheme possesses many advantages against popular attacks, and achieves better efficiency at low computation cost.

  4. Sensor fault diagnosis of aero-engine based on divided flight status.

    Science.gov (United States)

    Zhao, Zhen; Zhang, Jun; Sun, Yigang; Liu, Zhexu

    2017-11-01

    Fault diagnosis and safety analysis of an aero-engine have attracted more and more attention in modern society, whose safety directly affects the flight safety of an aircraft. In this paper, the problem concerning sensor fault diagnosis is investigated for an aero-engine during the whole flight process. Considering that the aero-engine is always working in different status through the whole flight process, a flight status division-based sensor fault diagnosis method is presented to improve fault diagnosis precision for the aero-engine. First, aero-engine status is partitioned according to normal sensor data during the whole flight process through the clustering algorithm. Based on that, a diagnosis model is built for each status using the principal component analysis algorithm. Finally, the sensors are monitored using the built diagnosis models by identifying the aero-engine status. The simulation result illustrates the effectiveness of the proposed method.

  5. Sensor fault diagnosis of aero-engine based on divided flight status

    Science.gov (United States)

    Zhao, Zhen; Zhang, Jun; Sun, Yigang; Liu, Zhexu

    2017-11-01

    Fault diagnosis and safety analysis of an aero-engine have attracted more and more attention in modern society, whose safety directly affects the flight safety of an aircraft. In this paper, the problem concerning sensor fault diagnosis is investigated for an aero-engine during the whole flight process. Considering that the aero-engine is always working in different status through the whole flight process, a flight status division-based sensor fault diagnosis method is presented to improve fault diagnosis precision for the aero-engine. First, aero-engine status is partitioned according to normal sensor data during the whole flight process through the clustering algorithm. Based on that, a diagnosis model is built for each status using the principal component analysis algorithm. Finally, the sensors are monitored using the built diagnosis models by identifying the aero-engine status. The simulation result illustrates the effectiveness of the proposed method.

  6. Fault Diagnosis for Satellite Sensors and Actuators using Nonlinear Geometric Approach and Adaptive Observers

    DEFF Research Database (Denmark)

    Baldi, P.; Blanke, Mogens; Castaldi, P.

    2018-01-01

    This paper presents a novel scheme for diagnosis of faults affecting sensors that measure the satellite attitude, body angular velocity, flywheel spin rates, and defects in control torques from reaction wheel motors. The proposed methodology uses adaptive observers to provide fault estimates that...

  7. Fault Diagnosis from Raw Sensor Data Using Deep Neural Networks Considering Temporal Coherence.

    Science.gov (United States)

    Zhang, Ran; Peng, Zhen; Wu, Lifeng; Yao, Beibei; Guan, Yong

    2017-03-09

    Intelligent condition monitoring and fault diagnosis by analyzing the sensor data can assure the safety of machinery. Conventional fault diagnosis and classification methods usually implement pretreatments to decrease noise and extract some time domain or frequency domain features from raw time series sensor data. Then, some classifiers are utilized to make diagnosis. However, these conventional fault diagnosis approaches suffer from the expertise of feature selection and they do not consider the temporal coherence of time series data. This paper proposes a fault diagnosis model based on Deep Neural Networks (DNN). The model can directly recognize raw time series sensor data without feature selection and signal processing. It also takes advantage of the temporal coherence of the data. Firstly, raw time series training data collected by sensors are used to train the DNN until the cost function of DNN gets the minimal value; Secondly, test data are used to test the classification accuracy of the DNN on local time series data. Finally, fault diagnosis considering temporal coherence with former time series data is implemented. Experimental results show that the classification accuracy of bearing faults can get 100%. The proposed fault diagnosis approach is effective in recognizing the type of bearing faults.

  8. Identifiability of Additive, Time-Varying Actuator and Sensor Faults by State Augmentation

    Science.gov (United States)

    Upchurch, Jason M.; Gonzalez, Oscar R.; Joshi, Suresh M.

    2014-01-01

    Recent work has provided a set of necessary and sucient conditions for identifiability of additive step faults (e.g., lock-in-place actuator faults, constant bias in the sensors) using state augmentation. This paper extends these results to an important class of faults which may affect linear, time-invariant systems. In particular, the faults under consideration are those which vary with time and affect the system dynamics additively. Such faults may manifest themselves in aircraft as, for example, control surface oscillations, control surface runaway, and sensor drift. The set of necessary and sucient conditions presented in this paper are general, and apply when a class of time-varying faults affects arbitrary combinations of actuators and sensors. The results in the main theorems are illustrated by two case studies, which provide some insight into how the conditions may be used to check the theoretical identifiability of fault configurations of interest for a given system. It is shown that while state augmentation can be used to identify certain fault configurations, other fault configurations are theoretically impossible to identify using state augmentation, giving practitioners valuable insight into such situations. That is, the limitations of state augmentation for a given system and configuration of faults are made explicit. Another limitation of model-based methods is that there can be large numbers of fault configurations, thus making identification of all possible configurations impractical. However, the theoretical identifiability of known, credible fault configurations can be tested using the theorems presented in this paper, which can then assist the efforts of fault identification practitioners.

  9. Sensor fault-tolerant control for gear-shifting engaging process of automated manual transmission

    Science.gov (United States)

    Li, Liang; He, Kai; Wang, Xiangyu; Liu, Yahui

    2018-01-01

    Angular displacement sensor on the actuator of automated manual transmission (AMT) is sensitive to fault, and the sensor fault will disturb its normal control, which affects the entire gear-shifting process of AMT and results in awful riding comfort. In order to solve this problem, this paper proposes a method of fault-tolerant control for AMT gear-shifting engaging process. By using the measured current of actuator motor and angular displacement of actuator, the gear-shifting engaging load torque table is built and updated before the occurrence of the sensor fault. Meanwhile, residual between estimated and measured angular displacements is used to detect the sensor fault. Once the residual exceeds a determined fault threshold, the sensor fault is detected. Then, switch control is triggered, and the current observer and load torque table estimates an actual gear-shifting position to replace the measured one to continue controlling the gear-shifting process. Numerical and experiment tests are carried out to evaluate the reliability and feasibility of proposed methods, and the results show that the performance of estimation and control is satisfactory.

  10. Robust Fault-Tolerant Control for Satellite Attitude Stabilization Based on Active Disturbance Rejection Approach with Artificial Bee Colony Algorithm

    Directory of Open Access Journals (Sweden)

    Fei Song

    2014-01-01

    Full Text Available This paper proposed a robust fault-tolerant control algorithm for satellite stabilization based on active disturbance rejection approach with artificial bee colony algorithm. The actuating mechanism of attitude control system consists of three working reaction flywheels and one spare reaction flywheel. The speed measurement of reaction flywheel is adopted for fault detection. If any reaction flywheel fault is detected, the corresponding fault flywheel is isolated and the spare reaction flywheel is activated to counteract the fault effect and ensure that the satellite is working safely and reliably. The active disturbance rejection approach is employed to design the controller, which handles input information with tracking differentiator, estimates system uncertainties with extended state observer, and generates control variables by state feedback and compensation. The designed active disturbance rejection controller is robust to both internal dynamics and external disturbances. The bandwidth parameter of extended state observer is optimized by the artificial bee colony algorithm so as to improve the performance of attitude control system. A series of simulation experiment results demonstrate the performance superiorities of the proposed robust fault-tolerant control algorithm.

  11. Robust adaptive fault-tolerant control for leader-follower flocking of uncertain multi-agent systems with actuator failure.

    Science.gov (United States)

    Yazdani, Sahar; Haeri, Mohammad

    2017-11-01

    In this work, we study the flocking problem of multi-agent systems with uncertain dynamics subject to actuator failure and external disturbances. By considering some standard assumptions, we propose a robust adaptive fault tolerant protocol for compensating of the actuator bias fault, the partial loss of actuator effectiveness fault, the model uncertainties, and external disturbances. Under the designed protocol, velocity convergence of agents to that of virtual leader is guaranteed while the connectivity preservation of network and collision avoidance among agents are ensured as well. Copyright © 2017 ISA. Published by Elsevier Ltd. All rights reserved.

  12. A Novel Online Data-Driven Algorithm for Detecting UAV Navigation Sensor Faults.

    Science.gov (United States)

    Sun, Rui; Cheng, Qi; Wang, Guanyu; Ochieng, Washington Yotto

    2017-09-29

    The use of Unmanned Aerial Vehicles (UAVs) has increased significantly in recent years. On-board integrated navigation sensors are a key component of UAVs' flight control systems and are essential for flight safety. In order to ensure flight safety, timely and effective navigation sensor fault detection capability is required. In this paper, a novel data-driven Adaptive Neuron Fuzzy Inference System (ANFIS)-based approach is presented for the detection of on-board navigation sensor faults in UAVs. Contrary to the classic UAV sensor fault detection algorithms, based on predefined or modelled faults, the proposed algorithm combines an online data training mechanism with the ANFIS-based decision system. The main advantages of this algorithm are that it allows real-time model-free residual analysis from Kalman Filter (KF) estimates and the ANFIS to build a reliable fault detection system. In addition, it allows fast and accurate detection of faults, which makes it suitable for real-time applications. Experimental results have demonstrated the effectiveness of the proposed fault detection method in terms of accuracy and misdetection rate.

  13. A Novel Online Data-Driven Algorithm for Detecting UAV Navigation Sensor Faults

    Directory of Open Access Journals (Sweden)

    Rui Sun

    2017-09-01

    Full Text Available The use of Unmanned Aerial Vehicles (UAVs has increased significantly in recent years. On-board integrated navigation sensors are a key component of UAVs’ flight control systems and are essential for flight safety. In order to ensure flight safety, timely and effective navigation sensor fault detection capability is required. In this paper, a novel data-driven Adaptive Neuron Fuzzy Inference System (ANFIS-based approach is presented for the detection of on-board navigation sensor faults in UAVs. Contrary to the classic UAV sensor fault detection algorithms, based on predefined or modelled faults, the proposed algorithm combines an online data training mechanism with the ANFIS-based decision system. The main advantages of this algorithm are that it allows real-time model-free residual analysis from Kalman Filter (KF estimates and the ANFIS to build a reliable fault detection system. In addition, it allows fast and accurate detection of faults, which makes it suitable for real-time applications. Experimental results have demonstrated the effectiveness of the proposed fault detection method in terms of accuracy and misdetection rate.

  14. Using Spread Spectrum Transform for Fast and Robust Simultaneous Measurement in Active Sensors with Multiple Emitters

    DEFF Research Database (Denmark)

    Harbo, Anders La-Cour; Stoustrup, Jakob

    2002-01-01

    We present a signal processing algorithm for making robust and simultaneous measurements in an active sensor, which has one or more emitters and a receiver, and which employs some sort of signal processing hardware. Robustness means low sensitivity to time and frequency localized disturbances......-cost active sensors....

  15. Clustering and fault tolerance for target tracking using wireless sensor networks

    International Nuclear Information System (INIS)

    Bhatti, S.; Khanzada, S.; Memon, S.

    2012-01-01

    Over the last few years, the deployment of WSNs (Wireless Sensor Networks) has been fostered in diverse applications. WSN has great potential for a variety of domains ranging from scientific experiments to commercial applications. Due to the deployment of WSNs in dynamic and unpredictable environments. They have potential to cope with variety of faults. This paper proposes an energy-aware fault-tolerant clustering protocol for target tracking applications termed as the FITf (Fault Tolerant Target Tracking) protocol The identification of RNs (Redundant Nodes) makes SN (Sensor Node) fault tolerance plausible and the clustering endorsed recovery of sensors supervised by a faulty CH (Cluster Head). The FfTT protocol intends two steps of reducing energy consumption: first, by identifying RNs in the network; secondly, by restricting the numbers of SNs sending data to the CH. Simulations validate the scalability and low power consumption of the FITf protocol in comparison with LEACH protocol. (author)

  16. Optimisation in the Design of Environmental Sensor Networks with Robustness Consideration

    Science.gov (United States)

    Budi, Setia; de Souza, Paulo; Timms, Greg; Malhotra, Vishv; Turner, Paul

    2015-01-01

    This work proposes the design of Environmental Sensor Networks (ESN) through balancing robustness and redundancy. An Evolutionary Algorithm (EA) is employed to find the optimal placement of sensor nodes in the Region of Interest (RoI). Data quality issues are introduced to simulate their impact on the performance of the ESN. Spatial Regression Test (SRT) is also utilised to promote robustness in data quality of the designed ESN. The proposed method provides high network representativeness (fit for purpose) with minimum sensor redundancy (cost), and ensures robustness by enabling the network to continue to achieve its objectives when some sensors fail. PMID:26633392

  17. Distributed sensor and actuator reconfiguration for fault-tolerant networked control systems

    NARCIS (Netherlands)

    Herdeiro Teixeira, A.M.; Araujo, Jose; Sandberg, Henrik; Johansson, Karl H.

    2017-01-01

    In this paper, we address the problem of distributed reconfiguration of networked control systems upon the removal of misbehaving sensors and actuators. In particular, we consider systems with redundant sensors and actuators cooperating to recover from faults. Reconfiguration is performed while

  18. A Machine Learning Approach for Identifying and Classifying Faults in Wireless Sensor Networks

    NARCIS (Netherlands)

    Warriach, Ehsan Ullah; Aiello, Marco; Tei, Kenji

    2012-01-01

    Wireless Sensor Network (WSN) deployment experiences show that collected data is prone to be faulty. Faults are due to internal and external influences, such as calibration, low battery, environmental interference and sensor aging. However, only few solutions exist to deal with faulty sensory data

  19. Fault detection and diagnosis for refrigerator from compressor sensor

    Science.gov (United States)

    Keres, Stephen L.; Gomes, Alberto Regio; Litch, Andrew D.

    2016-12-06

    A refrigerator, a sealed refrigerant system, and method are provided where the refrigerator includes at least a refrigerated compartment and a sealed refrigerant system including an evaporator, a compressor, a condenser, a controller, an evaporator fan, and a condenser fan. The method includes monitoring a frequency of the compressor, and identifying a fault condition in the at least one component of the refrigerant sealed system in response to the compressor frequency. The method may further comprise calculating a compressor frequency rate based upon the rate of change of the compressor frequency, wherein a fault in the condenser fan is identified if the compressor frequency rate is positive and exceeds a condenser fan fault threshold rate, and wherein a fault in the evaporator fan is identified if the compressor frequency rate is negative and exceeds an evaporator fan fault threshold rate.

  20. A Novel Dual Separate Paths (DSP) Algorithm Providing Fault-Tolerant Communication for Wireless Sensor Networks.

    Science.gov (United States)

    Tien, Nguyen Xuan; Kim, Semog; Rhee, Jong Myung; Park, Sang Yoon

    2017-07-25

    Fault tolerance has long been a major concern for sensor communications in fault-tolerant cyber physical systems (CPSs). Network failure problems often occur in wireless sensor networks (WSNs) due to various factors such as the insufficient power of sensor nodes, the dislocation of sensor nodes, the unstable state of wireless links, and unpredictable environmental interference. Fault tolerance is thus one of the key requirements for data communications in WSN applications. This paper proposes a novel path redundancy-based algorithm, called dual separate paths (DSP), that provides fault-tolerant communication with the improvement of the network traffic performance for WSN applications, such as fault-tolerant CPSs. The proposed DSP algorithm establishes two separate paths between a source and a destination in a network based on the network topology information. These paths are node-disjoint paths and have optimal path distances. Unicast frames are delivered from the source to the destination in the network through the dual paths, providing fault-tolerant communication and reducing redundant unicast traffic for the network. The DSP algorithm can be applied to wired and wireless networks, such as WSNs, to provide seamless fault-tolerant communication for mission-critical and life-critical applications such as fault-tolerant CPSs. The analyzed and simulated results show that the DSP-based approach not only provides fault-tolerant communication, but also improves network traffic performance. For the case study in this paper, when the DSP algorithm was applied to high-availability seamless redundancy (HSR) networks, the proposed DSP-based approach reduced the network traffic by 80% to 88% compared with the standard HSR protocol, thus improving network traffic performance.

  1. Fault diagnosis of locomotive electro-pneumatic brake through uncertain bond graph modeling and robust online monitoring

    Science.gov (United States)

    Niu, Gang; Zhao, Yajun; Defoort, Michael; Pecht, Michael

    2015-01-01

    To improve reliability, safety and efficiency, advanced methods of fault detection and diagnosis become increasingly important for many technical fields, especially for safety related complex systems like aircraft, trains, automobiles, power plants and chemical plants. This paper presents a robust fault detection and diagnostic scheme for a multi-energy domain system that integrates a model-based strategy for system fault modeling and a data-driven approach for online anomaly monitoring. The developed scheme uses LFT (linear fractional transformations)-based bond graph for physical parameter uncertainty modeling and fault simulation, and employs AAKR (auto-associative kernel regression)-based empirical estimation followed by SPRT (sequential probability ratio test)-based threshold monitoring to improve the accuracy of fault detection. Moreover, pre- and post-denoising processes are applied to eliminate the cumulative influence of parameter uncertainty and measurement uncertainty. The scheme is demonstrated on the main unit of a locomotive electro-pneumatic brake in a simulated experiment. The results show robust fault detection and diagnostic performance.

  2. GMDH and neural networks applied in monitoring and fault detection in sensors in nuclear power plants

    Energy Technology Data Exchange (ETDEWEB)

    Bueno, Elaine Inacio [Instituto Federal de Educacao, Ciencia e Tecnologia, Guarulhos, SP (Brazil); Pereira, Iraci Martinez; Silva, Antonio Teixeira e, E-mail: martinez@ipen.b, E-mail: teixeira@ipen.b [Instituto de Pesquisas Energeticas e Nucleares (IPEN/CNEN-SP), Sao Paulo, SP (Brazil)

    2011-07-01

    In this work a new monitoring and fault detection methodology was developed using GMDH (Group Method of Data Handling) algorithm and artificial neural networks (ANNs) which was applied in the IEA-R1 research reactor at IPEN. The monitoring and fault detection system was developed in two parts: the first was dedicated to preprocess information, using GMDH algorithm; and the second to the process information using ANNs. The preprocess information was divided in two parts. In the first part, the GMDH algorithm was used to generate a better database estimate, called matrix z, which was used to train the ANNs. In the second part the GMDH was used to study the best set of variables to be used to train the ANNs, resulting in a best monitoring variable estimative. The methodology was developed and tested using five different models: one theoretical model and for models using different sets of reactor variables. After an exhausting study dedicated to the sensors monitoring, the fault detection in sensors was developed by simulating faults in the sensors database using values of +5%, +10%, +15% and +20% in these sensors database. The good results obtained through the present methodology shows the viability of using GMDH algorithm in the study of the best input variables to the ANNs, thus making possible the use of these methods in the implementation of a new monitoring and fault detection methodology applied in sensors. (author)

  3. GMDH and neural networks applied in monitoring and fault detection in sensors in nuclear power plants

    International Nuclear Information System (INIS)

    Bueno, Elaine Inacio; Pereira, Iraci Martinez; Silva, Antonio Teixeira e

    2011-01-01

    In this work a new monitoring and fault detection methodology was developed using GMDH (Group Method of Data Handling) algorithm and artificial neural networks (ANNs) which was applied in the IEA-R1 research reactor at IPEN. The monitoring and fault detection system was developed in two parts: the first was dedicated to preprocess information, using GMDH algorithm; and the second to the process information using ANNs. The preprocess information was divided in two parts. In the first part, the GMDH algorithm was used to generate a better database estimate, called matrix z, which was used to train the ANNs. In the second part the GMDH was used to study the best set of variables to be used to train the ANNs, resulting in a best monitoring variable estimative. The methodology was developed and tested using five different models: one theoretical model and for models using different sets of reactor variables. After an exhausting study dedicated to the sensors monitoring, the fault detection in sensors was developed by simulating faults in the sensors database using values of +5%, +10%, +15% and +20% in these sensors database. The good results obtained through the present methodology shows the viability of using GMDH algorithm in the study of the best input variables to the ANNs, thus making possible the use of these methods in the implementation of a new monitoring and fault detection methodology applied in sensors. (author)

  4. Smart Sensor for Online Detection of Multiple-Combined Faults in VSD-Fed Induction Motors

    Science.gov (United States)

    Garcia-Ramirez, Armando G.; Osornio-Rios, Roque A.; Granados-Lieberman, David; Garcia-Perez, Arturo; Romero-Troncoso, Rene J.

    2012-01-01

    Induction motors fed through variable speed drives (VSD) are widely used in different industrial processes. Nowadays, the industry demands the integration of smart sensors to improve the fault detection in order to reduce cost, maintenance and power consumption. Induction motors can develop one or more faults at the same time that can be produce severe damages. The combined fault identification in induction motors is a demanding task, but it has been rarely considered in spite of being a common situation, because it is difficult to identify two or more faults simultaneously. This work presents a smart sensor for online detection of simple and multiple-combined faults in induction motors fed through a VSD in a wide frequency range covering low frequencies from 3 Hz and high frequencies up to 60 Hz based on a primary sensor being a commercially available current clamp or a hall-effect sensor. The proposed smart sensor implements a methodology based on the fast Fourier transform (FFT), RMS calculation and artificial neural networks (ANN), which are processed online using digital hardware signal processing based on field programmable gate array (FPGA).

  5. Nuclear Power Plant Thermocouple Sensor-Fault Detection and Classification Using Deep Learning and Generalized Likelihood Ratio Test

    Science.gov (United States)

    Mandal, Shyamapada; Santhi, B.; Sridhar, S.; Vinolia, K.; Swaminathan, P.

    2017-06-01

    In this paper, an online fault detection and classification method is proposed for thermocouples used in nuclear power plants. In the proposed method, the fault data are detected by the classification method, which classifies the fault data from the normal data. Deep belief network (DBN), a technique for deep learning, is applied to classify the fault data. The DBN has a multilayer feature extraction scheme, which is highly sensitive to a small variation of data. Since the classification method is unable to detect the faulty sensor; therefore, a technique is proposed to identify the faulty sensor from the fault data. Finally, the composite statistical hypothesis test, namely generalized likelihood ratio test, is applied to compute the fault pattern of the faulty sensor signal based on the magnitude of the fault. The performance of the proposed method is validated by field data obtained from thermocouple sensors of the fast breeder test reactor.

  6. Small and Robust Antennas for Concrete Embedded Sensors

    DEFF Research Database (Denmark)

    Rindorf, Lars; Jakobsen, Kaj Bjarne

    2009-01-01

    We study a small antenna for a structural health monitoring sensor. The sensor is cast into concrete. The sensor monitors the humidity and temperature of the concrete, which is a harsh environment. From measured material parameters of the concrete we estimate the signal loss and the antenna detun...

  7. A Gossip-based Energy Efficient Protocol for Robust In-network Aggregation in Wireless Sensor Networks

    Science.gov (United States)

    Fauji, Shantanu

    We consider the problem of energy efficient and fault tolerant in--network aggregation for wireless sensor networks (WSNs). In-network aggregation is the process of aggregation while collecting data from sensors to the base station. This process should be energy efficient due to the limited energy at the sensors and tolerant to the high failure rates common in sensor networks. Tree based in--network aggregation protocols, although energy efficient, are not robust to network failures. Multipath routing protocols are robust to failures to a certain degree but are not energy efficient due to the overhead in the maintenance of multiple paths. We propose a new protocol for in-network aggregation in WSNs, which is energy efficient, achieves high lifetime, and is robust to the changes in the network topology. Our protocol, gossip--based protocol for in-network aggregation (GPIA) is based on the spreading of information via gossip. GPIA is not only adaptive to failures and changes in the network topology, but is also energy efficient. Energy efficiency of GPIA comes from all the nodes being capable of selective message reception and detecting convergence of the aggregation early. We experimentally show that GPIA provides significant improvement over some other competitors like the Ridesharing, Synopsis Diffusion and the pure version of gossip. GPIA shows ten fold, five fold and two fold improvement over the pure gossip, the synopsis diffusion and Ridesharing protocols in terms of network lifetime, respectively. Further, GPIA retains gossip's robustness to failures and improves upon the accuracy of synopsis diffusion and Ridesharing.

  8. Nuclear power plant sensor fault detection using singular value ...

    Indian Academy of Sciences (India)

    Shyamapada Mandal

    2017-07-27

    Jul 27, 2017 ... A nuclear power plant (NPP) has a large number of sensors to monitor different ... mathematical and systematic characteristics of the pro- cess. In these ..... avoid many problems associated with manual calibration of sensors.

  9. Nuclear power plant sensor fault detection using singular value

    Indian Academy of Sciences (India)

    In a nuclear power plant, periodic sensor calibration is necessary to ensure the correctness of measurements. Those sensors which have gone out of calibration can lead to malfunction of the plant, possibly causing a loss in revenue or damage to equipment. Continuous sensor status monitoring is desirable to assure ...

  10. Robust Distributed Kalman Filter for Wireless Sensor Networks with Uncertain Communication Channels

    Directory of Open Access Journals (Sweden)

    Du Yong Kim

    2012-01-01

    Full Text Available We address a state estimation problem over a large-scale sensor network with uncertain communication channel. Consensus protocol is usually used to adapt a large-scale sensor network. However, when certain parts of communication channels are broken down, the accuracy performance is seriously degraded. Specifically, outliers in the channel or temporal disconnection are avoided via proposed method for the practical implementation of the distributed estimation over large-scale sensor networks. We handle this practical challenge by using adaptive channel status estimator and robust L1-norm Kalman filter in design of the processor of the individual sensor node. Then, they are incorporated into the consensus algorithm in order to achieve the robust distributed state estimation. The robust property of the proposed algorithm enables the sensor network to selectively weight sensors of normal conditions so that the filter can be practically useful.

  11. Two-level Robust Measurement Fusion Kalman Filter for Clustering Sensor Networks

    Institute of Scientific and Technical Information of China (English)

    ZHANG Peng; QI Wen-Juan; DENG Zi-Li

    2014-01-01

    This paper investigates the distributed fusion Kalman filtering over clustering sensor networks. The sensor network is partitioned as clusters by the nearest neighbor rule and each cluster consists of sensing nodes and cluster-head. Using the minimax robust estimation principle, based on the worst-case conservative system with the conservative upper bounds of noise variances, two-level robust measurement fusion Kalman filter is presented for the clustering sensor network systems with uncertain noise variances. It can significantly reduce the communication load and save energy when the number of sensors is very large. A Lyapunov equation approach for the robustness analysis is presented, by which the robustness of the local and fused Kalman filters is proved. The concept of the robust accuracy is presented, and the robust accuracy relations among the local and fused robust Kalman filters are proved. It is proved that the robust accuracy of the two-level weighted measurement fuser is equal to that of the global centralized robust fuser and is higher than those of each local robust filter and each local weighted measurement fuser. A simulation example shows the correctness and effectiveness of the proposed results.

  12. A Self-Learning Sensor Fault Detection Framework for Industry Monitoring IoT

    Directory of Open Access Journals (Sweden)

    Yu Liu

    2013-01-01

    Full Text Available Many applications based on Internet of Things (IoT technology have recently founded in industry monitoring area. Thousands of sensors with different types work together in an industry monitoring system. Sensors at different locations can generate streaming data, which can be analyzed in the data center. In this paper, we propose a framework for online sensor fault detection. We motivate our technique in the context of the problem of the data value fault detection and event detection. We use the Statistics Sliding Windows (SSW to contain the recent sensor data and regress each window by Gaussian distribution. The regression result can be used to detect the data value fault. Devices on a production line may work in different workloads and the associate sensors will have different status. We divide the sensors into several status groups according to different part of production flow chat. In this way, the status of a sensor is associated with others in the same group. We fit the values in the Status Transform Window (STW to get the slope and generate a group trend vector. By comparing the current trend vector with history ones, we can detect a rational or irrational event. In order to determine parameters for each status group we build a self-learning worker thread in our framework which can edit the corresponding parameter according to the user feedback. Group-based fault detection (GbFD algorithm is proposed in this paper. We test the framework with a simulation dataset extracted from real data of an oil field. Test result shows that GbFD detects 95% sensor fault successfully.

  13. Robust, Self-Contained and Bio-Inspired Shear Sensor Array, Phase I

    Data.gov (United States)

    National Aeronautics and Space Administration — The proposed innovation is a robust, bio-inspired, and self-contained sensor array for the measurement of shear stress. The proposed system uses commercially...

  14. Detection of Sensor Faults in Small Helicopter UAVs Using Observer/Kalman Filter Identification

    Directory of Open Access Journals (Sweden)

    Guillermo Heredia

    2011-01-01

    Full Text Available Reliability is a critical issue in navigation of unmanned aerial vehicles (UAVs since there is no human pilot that can react to any abnormal situation. Due to size and cost limitations, redundant sensor schemes and aeronautical-grade navigation sensors used in large aircrafts cannot be installed in small UAVs. Therefore, other approaches like analytical redundancy should be used to detect faults in navigation sensors and increase reliability. This paper presents a sensor fault detection and diagnosis system for small autonomous helicopters based on analytical redundancy. Fault detection is accomplished by evaluating any significant change in the behaviour of the vehicle with respect to the fault-free behaviour, which is estimated by using an observer. The observer is obtained from input-output experimental data with the Observer/Kalman Filter Identification (OKID method. The OKID method is able to identify the system and an observer with properties similar to a Kalman filter, directly from input-output experimental data. Results are similar to the Kalman filter, but, with the proposed method, there is no need to estimate neither system matrices nor sensor and process noise covariance matrices. The system has been tested with real helicopter flight data, and the results compared with other methods.

  15. A Method to Simultaneously Detect the Current Sensor Fault and Estimate the State of Energy for Batteries in Electric Vehicles.

    Science.gov (United States)

    Xu, Jun; Wang, Jing; Li, Shiying; Cao, Binggang

    2016-08-19

    Recently, State of energy (SOE) has become one of the most fundamental parameters for battery management systems in electric vehicles. However, current information is critical in SOE estimation and current sensor is usually utilized to obtain the latest current information. However, if the current sensor fails, the SOE estimation may be confronted with large error. Therefore, this paper attempts to make the following contributions: Current sensor fault detection and SOE estimation method is realized simultaneously. Through using the proportional integral observer (PIO) based method, the current sensor fault could be accurately estimated. By taking advantage of the accurate estimated current sensor fault, the influence caused by the current sensor fault can be eliminated and compensated. As a result, the results of the SOE estimation will be influenced little by the fault. In addition, the simulation and experimental workbench is established to verify the proposed method. The results indicate that the current sensor fault can be estimated accurately. Simultaneously, the SOE can also be estimated accurately and the estimation error is influenced little by the fault. The maximum SOE estimation error is less than 2%, even though the large current error caused by the current sensor fault still exists.

  16. Fiber Bragg Grating Sensor for Fault Detection in Radial and Network Transmission Lines

    Directory of Open Access Journals (Sweden)

    Mehdi Shadaram

    2010-10-01

    Full Text Available In this paper, a fiber optic based sensor capable of fault detection in both radial and network overhead transmission power line systems is investigated. Bragg wavelength shift is used to measure the fault current and detect fault in power systems. Magnetic fields generated by currents in the overhead transmission lines cause a strain in magnetostrictive material which is then detected by Fiber Bragg Grating (FBG. The Fiber Bragg interrogator senses the reflected FBG signals, and the Bragg wavelength shift is calculated and the signals are processed. A broadband light source in the control room scans the shift in the reflected signal. Any surge in the magnetic field relates to an increased fault current at a certain location. Also, fault location can be precisely defined with an artificial neural network (ANN algorithm. This algorithm can be easily coordinated with other protective devices. It is shown that the faults in the overhead transmission line cause a detectable wavelength shift on the reflected signal of FBG and can be used to detect and classify different kind of faults. The proposed method has been extensively tested by simulation and results confirm that the proposed scheme is able to detect different kinds of fault in both radial and network system.

  17. Sensor Fault Masking of a Ship Propulsion System

    DEFF Research Database (Denmark)

    Wu, N. Eva; Thavamani, Shuda; Zhang, Youmin

    2005-01-01

    This paper presents the results of a study on fault-tolerant control of a ship propulsion benchmark (Izadi-Zamanabadi and Blanke, 999), which uses estimated or virtual measurements as feedback variables. The estimator operates on a self-adjustable design model so that its outputs can be made immu...

  18. Sensor Fault Masking of a Ship Propulsion System

    DEFF Research Database (Denmark)

    Wu, N.E.; Thavamani, A.; Zhang, Y.

    2003-01-01

    This paper presents the results of a study on fault-tolerant control of a ship propulsion benchmark (Izadi-Zamanabadi and Blanke, 1999), which uses estimated or virtual measurements as feedback variables. The estimator operates on a selfadjustable design model so that its outputs can be made immu...

  19. A Nonlinear Adaptive Approach to Isolation of Sensor Faults and Component Faults, Phase I

    Data.gov (United States)

    National Aeronautics and Space Administration — Impact Technologies, LLC in collaboration with Wright State University and Pratt & Whitney, propose to develop innovative methods to differentiate sensor failure...

  20. Fault Detection And Diagnosis For Air Conditioners And Heat Pumps Based On Virtual Sensors

    OpenAIRE

    Kim, Woohyun

    2013-01-01

    The primary goal of this research is to develop and demonstrate an integrated, on-line performance monitoring and diagnostic system with low cost sensors for air conditioning and heat pump equipment. Automated fault detection and diagnostics (FDD) has the potential for improving energy efficiency along with reducing service costs and comfort complaints. To achieve this goal, virtual sensors with low cost measurements and simple models were developed to estimate quantities that would be expens...

  1. Underwater Sensor Networks: A New Energy Efficient and Robust Architecture

    NARCIS (Netherlands)

    Climent, Salvador; Capella, Juan Vincente; Meratnia, Nirvana; Serrano, Juan José

    2012-01-01

    The specific characteristics of underwater environments introduce new challenges for networking protocols. In this paper, a specialized architecture for underwater sensor networks (UWSNs) is proposed and evaluated. Experiments are conducted in order to analyze the suitability of this protocol for

  2. Robust Agent Control of an Autonomous Robot with Many Sensors and Actuators

    Science.gov (United States)

    1993-05-01

    and suggests areas of future work. 13 Chapter 2 Hannibal This chapter presents the physical, sensing, and computing aspects of Hanni - bal. Hannibal, and...of sensor faults on the left front leg (the same pro- cesses run on the rest of the legs). The tests were performed while Hanni - bal walked over flat

  3. Fault Detection Using the Clustering-kNN Rule for Gas Sensor Arrays

    Directory of Open Access Journals (Sweden)

    Jingli Yang

    2016-12-01

    Full Text Available The k-nearest neighbour (kNN rule, which naturally handles the possible non-linearity of data, is introduced to solve the fault detection problem of gas sensor arrays. In traditional fault detection methods based on the kNN rule, the detection process of each new test sample involves all samples in the entire training sample set. Therefore, these methods can be computation intensive in monitoring processes with a large volume of variables and training samples and may be impossible for real-time monitoring. To address this problem, a novel clustering-kNN rule is presented. The landmark-based spectral clustering (LSC algorithm, which has low computational complexity, is employed to divide the entire training sample set into several clusters. Further, the kNN rule is only conducted in the cluster that is nearest to the test sample; thus, the efficiency of the fault detection methods can be enhanced by reducing the number of training samples involved in the detection process of each test sample. The performance of the proposed clustering-kNN rule is fully verified in numerical simulations with both linear and non-linear models and a real gas sensor array experimental system with different kinds of faults. The results of simulations and experiments demonstrate that the clustering-kNN rule can greatly enhance both the accuracy and efficiency of fault detection methods and provide an excellent solution to reliable and real-time monitoring of gas sensor arrays.

  4. Vibration sensor data denoising using a time-frequency manifold for machinery fault diagnosis.

    Science.gov (United States)

    He, Qingbo; Wang, Xiangxiang; Zhou, Qiang

    2013-12-27

    Vibration sensor data from a mechanical system are often associated with important measurement information useful for machinery fault diagnosis. However, in practice the existence of background noise makes it difficult to identify the fault signature from the sensing data. This paper introduces the time-frequency manifold (TFM) concept into sensor data denoising and proposes a novel denoising method for reliable machinery fault diagnosis. The TFM signature reflects the intrinsic time-frequency structure of a non-stationary signal. The proposed method intends to realize data denoising by synthesizing the TFM using time-frequency synthesis and phase space reconstruction (PSR) synthesis. Due to the merits of the TFM in noise suppression and resolution enhancement, the denoised signal would have satisfactory denoising effects, as well as inherent time-frequency structure keeping. Moreover, this paper presents a clustering-based statistical parameter to evaluate the proposed method, and also presents a new diagnostic approach, called frequency probability time series (FPTS) spectral analysis, to show its effectiveness in fault diagnosis. The proposed TFM-based data denoising method has been employed to deal with a set of vibration sensor data from defective bearings, and the results verify that for machinery fault diagnosis the method is superior to two traditional denoising methods.

  5. Fault Detection Using the Clustering-kNN Rule for Gas Sensor Arrays

    Science.gov (United States)

    Yang, Jingli; Sun, Zhen; Chen, Yinsheng

    2016-01-01

    The k-nearest neighbour (kNN) rule, which naturally handles the possible non-linearity of data, is introduced to solve the fault detection problem of gas sensor arrays. In traditional fault detection methods based on the kNN rule, the detection process of each new test sample involves all samples in the entire training sample set. Therefore, these methods can be computation intensive in monitoring processes with a large volume of variables and training samples and may be impossible for real-time monitoring. To address this problem, a novel clustering-kNN rule is presented. The landmark-based spectral clustering (LSC) algorithm, which has low computational complexity, is employed to divide the entire training sample set into several clusters. Further, the kNN rule is only conducted in the cluster that is nearest to the test sample; thus, the efficiency of the fault detection methods can be enhanced by reducing the number of training samples involved in the detection process of each test sample. The performance of the proposed clustering-kNN rule is fully verified in numerical simulations with both linear and non-linear models and a real gas sensor array experimental system with different kinds of faults. The results of simulations and experiments demonstrate that the clustering-kNN rule can greatly enhance both the accuracy and efficiency of fault detection methods and provide an excellent solution to reliable and real-time monitoring of gas sensor arrays. PMID:27929412

  6. Aircraft Engine Sensor/Actuator/Component Fault Diagnosis Using a Bank of Kalman Filters

    Science.gov (United States)

    Kobayashi, Takahisa; Simon, Donald L. (Technical Monitor)

    2003-01-01

    In this report, a fault detection and isolation (FDI) system which utilizes a bank of Kalman filters is developed for aircraft engine sensor and actuator FDI in conjunction with the detection of component faults. This FDI approach uses multiple Kalman filters, each of which is designed based on a specific hypothesis for detecting a specific sensor or actuator fault. In the event that a fault does occur, all filters except the one using the correct hypothesis will produce large estimation errors, from which a specific fault is isolated. In the meantime, a set of parameters that indicate engine component performance is estimated for the detection of abrupt degradation. The performance of the FDI system is evaluated against a nonlinear engine simulation for various engine faults at cruise operating conditions. In order to mimic the real engine environment, the nonlinear simulation is executed not only at the nominal, or healthy, condition but also at aged conditions. When the FDI system designed at the healthy condition is applied to an aged engine, the effectiveness of the FDI system is impacted by the mismatch in the engine health condition. Depending on its severity, this mismatch can cause the FDI system to generate incorrect diagnostic results, such as false alarms and missed detections. To partially recover the nominal performance, two approaches, which incorporate information regarding the engine s aging condition in the FDI system, will be discussed and evaluated. The results indicate that the proposed FDI system is promising for reliable diagnostics of aircraft engines.

  7. Evaluation of MEMS-Based Wireless Accelerometer Sensors in Detecting Gear Tooth Faults in Helicopter Transmissions

    Science.gov (United States)

    Lewicki, David George; Lambert, Nicholas A.; Wagoner, Robert S.

    2015-01-01

    The diagnostics capability of micro-electro-mechanical systems (MEMS) based rotating accelerometer sensors in detecting gear tooth crack failures in helicopter main-rotor transmissions was evaluated. MEMS sensors were installed on a pre-notched OH-58C spiral-bevel pinion gear. Endurance tests were performed and the gear was run to tooth fracture failure. Results from the MEMS sensor were compared to conventional accelerometers mounted on the transmission housing. Most of the four stationary accelerometers mounted on the gear box housing and most of the CI's used gave indications of failure at the end of the test. The MEMS system performed well and lasted the entire test. All MEMS accelerometers gave an indication of failure at the end of the test. The MEMS systems performed as well, if not better, than the stationary accelerometers mounted on the gear box housing with regards to gear tooth fault detection. For both the MEMS sensors and stationary sensors, the fault detection time was not much sooner than the actual tooth fracture time. The MEMS sensor spectrum data showed large first order shaft frequency sidebands due to the measurement rotating frame of reference. The method of constructing a pseudo tach signal from periodic characteristics of the vibration data was successful in deriving a TSA signal without an actual tach and proved as an effective way to improve fault detection for the MEMS.

  8. Actuator and sensor selection for an active vehicle suspension aimed at robust performance

    NARCIS (Netherlands)

    Wal, van de M.M.J.; Philips, P.P.H.H.; Jager, de A.G.

    1998-01-01

    A recently presented method for actuator and sensor selection for linear control systems is applied and evaluated for an active vehicle suspension control problem. The aim is to eliminate the actuator/sensor combinations for which no controller exists that achieves a specified level of robust

  9. Sliding Mode Observer-Based Current Sensor Fault Reconstruction and Unknown Load Disturbance Estimation for PMSM Driven System.

    Science.gov (United States)

    Zhao, Kaihui; Li, Peng; Zhang, Changfan; Li, Xiangfei; He, Jing; Lin, Yuliang

    2017-12-06

    This paper proposes a new scheme of reconstructing current sensor faults and estimating unknown load disturbance for a permanent magnet synchronous motor (PMSM)-driven system. First, the original PMSM system is transformed into two subsystems; the first subsystem has unknown system load disturbances, which are unrelated to sensor faults, and the second subsystem has sensor faults, but is free from unknown load disturbances. Introducing a new state variable, the augmented subsystem that has sensor faults can be transformed into having actuator faults. Second, two sliding mode observers (SMOs) are designed: the unknown load disturbance is estimated by the first SMO in the subsystem, which has unknown load disturbance, and the sensor faults can be reconstructed using the second SMO in the augmented subsystem, which has sensor faults. The gains of the proposed SMOs and their stability analysis are developed via the solution of linear matrix inequality (LMI). Finally, the effectiveness of the proposed scheme was verified by simulations and experiments. The results demonstrate that the proposed scheme can reconstruct current sensor faults and estimate unknown load disturbance for the PMSM-driven system.

  10. Adaptive robust fault-tolerant control for linear MIMO systems with unmatched uncertainties

    Science.gov (United States)

    Zhang, Kangkang; Jiang, Bin; Yan, Xing-Gang; Mao, Zehui

    2017-10-01

    In this paper, two novel fault-tolerant control design approaches are proposed for linear MIMO systems with actuator additive faults, multiplicative faults and unmatched uncertainties. For time-varying multiplicative and additive faults, new adaptive laws and additive compensation functions are proposed. A set of conditions is developed such that the unmatched uncertainties are compensated by actuators in control. On the other hand, for unmatched uncertainties with their projection in unmatched space being not zero, based on a (vector) relative degree condition, additive functions are designed to compensate for the uncertainties from output channels in the presence of actuator faults. The developed fault-tolerant control schemes are applied to two aircraft systems to demonstrate the efficiency of the proposed approaches.

  11. Vehicle Lateral Control Under Fault in Front and/or Rear Sensors: Final Report

    OpenAIRE

    Lu, Guang; Huang, Jihua; Tomizuka, Masayoshi

    2004-01-01

    This report presents the research results of Task Order 4204(TO4204), "Vehicle Lateral Control under Fault in Front and/or Rear Sensors". This project is a continuing effort of the Partners for Advanced Transit and Highways (PATH) on the research of passenger vehicles for Automated Highway Systems (AHS).

  12. A Method Based on Multi-Sensor Data Fusion for Fault Detection of Planetary Gearboxes

    Directory of Open Access Journals (Sweden)

    Detong Kong

    2012-02-01

    Full Text Available Studies on fault detection and diagnosis of planetary gearboxes are quite limited compared with those of fixed-axis gearboxes. Different from fixed-axis gearboxes, planetary gearboxes exhibit unique behaviors, which invalidate fault diagnosis methods that work well for fixed-axis gearboxes. It is a fact that for systems as complex as planetary gearboxes, multiple sensors mounted on different locations provide complementary information on the health condition of the systems. On this basis, a fault detection method based on multi-sensor data fusion is introduced in this paper. In this method, two features developed for planetary gearboxes are used to characterize the gear health conditions, and an adaptive neuro-fuzzy inference system (ANFIS is utilized to fuse all features from different sensors. In order to demonstrate the effectiveness of the proposed method, experiments are carried out on a planetary gearbox test rig, on which multiple accelerometers are mounted for data collection. The comparisons between the proposed method and the methods based on individual sensors show that the former achieves much higher accuracies in detecting planetary gearbox faults.

  13. Combined Geometric and Neural Network Approach to Generic Fault Diagnosis in Satellite Actuators and Sensors

    DEFF Research Database (Denmark)

    Baldi, P.; Blanke, Mogens; Castaldi, P.

    2016-01-01

    This paper presents a novel scheme for diagnosis of faults affecting the sensors measuring the satellite attitude, body angular velocity and flywheel spin rates as well as defects related to the control torques provided by satellite reaction wheels. A nonlinear geometric design is used to avoid t...

  14. Model-Based Sensor Placement for Component Condition Monitoring and Fault Diagnosis in Fossil Energy Systems

    Energy Technology Data Exchange (ETDEWEB)

    Mobed, Parham [Texas Tech Univ., Lubbock, TX (United States); Pednekar, Pratik [West Virginia Univ., Morgantown, WV (United States); Bhattacharyya, Debangsu [West Virginia Univ., Morgantown, WV (United States); Turton, Richard [West Virginia Univ., Morgantown, WV (United States); Rengaswamy, Raghunathan [Texas Tech Univ., Lubbock, TX (United States)

    2016-01-29

    Design and operation of energy producing, near “zero-emission” coal plants has become a national imperative. This report on model-based sensor placement describes a transformative two-tier approach to identify the optimum placement, number, and type of sensors for condition monitoring and fault diagnosis in fossil energy system operations. The algorithms are tested on a high fidelity model of the integrated gasification combined cycle (IGCC) plant. For a condition monitoring network, whether equipment should be considered at a unit level or a systems level depends upon the criticality of the process equipment, its likeliness to fail, and the level of resolution desired for any specific failure. Because of the presence of a high fidelity model at the unit level, a sensor network can be designed to monitor the spatial profile of the states and estimate fault severity levels. In an IGCC plant, besides the gasifier, the sour water gas shift (WGS) reactor plays an important role. In view of this, condition monitoring of the sour WGS reactor is considered at the unit level, while a detailed plant-wide model of gasification island, including sour WGS reactor and the Selexol process, is considered for fault diagnosis at the system-level. Finally, the developed algorithms unify the two levels and identifies an optimal sensor network that maximizes the effectiveness of the overall system-level fault diagnosis and component-level condition monitoring. This work could have a major impact on the design and operation of future fossil energy plants, particularly at the grassroots level where the sensor network is yet to be identified. In addition, the same algorithms developed in this report can be further enhanced to be used in retrofits, where the objectives could be upgrade (addition of more sensors) and relocation of existing sensors.

  15. Fault-tolerant topology in the wireless sensor networks for energy depletion and random failure

    International Nuclear Information System (INIS)

    Liu Bin; Dong Ming-Ru; Yin Rong-Rong; Yin Wen-Xiao

    2014-01-01

    Nodes in the wireless sensor networks (WSNs) are prone to failure due to energy depletion and poor environment, which could have a negative impact on the normal operation of the network. In order to solve this problem, in this paper, we build a fault-tolerant topology which can effectively tolerate energy depletion and random failure. Firstly, a comprehensive failure model about energy depletion and random failure is established. Then an improved evolution model is presented to generate a fault-tolerant topology, and the degree distribution of the topology can be adjusted. Finally, the relation between the degree distribution and the topological fault tolerance is analyzed, and the optimal value of evolution model parameter is obtained. Then the target fault-tolerant topology which can effectively tolerate energy depletion and random failure is obtained. The performances of the new fault tolerant topology are verified by simulation experiments. The results show that the new fault tolerant topology effectively prolongs the network lifetime and has strong fault tolerance. (general)

  16. Feedback Robust Cubature Kalman Filter for Target Tracking Using an Angle Sensor.

    Science.gov (United States)

    Wu, Hao; Chen, Shuxin; Yang, Binfeng; Chen, Kun

    2016-05-09

    The direction of arrival (DOA) tracking problem based on an angle sensor is an important topic in many fields. In this paper, a nonlinear filter named the feedback M-estimation based robust cubature Kalman filter (FMR-CKF) is proposed to deal with measurement outliers from the angle sensor. The filter designs a new equivalent weight function with the Mahalanobis distance to combine the cubature Kalman filter (CKF) with the M-estimation method. Moreover, by embedding a feedback strategy which consists of a splitting and merging procedure, the proper sub-filter (the standard CKF or the robust CKF) can be chosen in each time index. Hence, the probability of the outliers' misjudgment can be reduced. Numerical experiments show that the FMR-CKF performs better than the CKF and conventional robust filters in terms of accuracy and robustness with good computational efficiency. Additionally, the filter can be extended to the nonlinear applications using other types of sensors.

  17. Robust Design of SAW Gas Sensors by Taguchi Dynamic Method

    Directory of Open Access Journals (Sweden)

    Hsun-Heng Tsai

    2009-02-01

    Full Text Available This paper adopts Taguchi’s signal-to-noise ratio analysis to optimize the dynamic characteristics of a SAW gas sensor system whose output response is linearly related to the input signal. The goal of the present dynamic characteristics study is to increase the sensitivity of the measurement system while simultaneously reducing its variability. A time- and cost-efficient finite element analysis method is utilized to investigate the effects of the deposited mass upon the resonant frequency output of the SAW biosensor. The results show that the proposed methodology not only reduces the design cost but also promotes the performance of the sensors.

  18. An Adaptive Fault-Tolerant Communication Scheme for Body Sensor Networks

    Directory of Open Access Journals (Sweden)

    Zichuan Xu

    2010-10-01

    Full Text Available A high degree of reliability for critical data transmission is required in body sensor networks (BSNs. However, BSNs are usually vulnerable to channel impairments due to body fading effect and RF interference, which may potentially cause data transmission to be unreliable. In this paper, an adaptive and flexible fault-tolerant communication scheme for BSNs, namely AFTCS, is proposed. AFTCS adopts a channel bandwidth reservation strategy to provide reliable data transmission when channel impairments occur. In order to fulfill the reliability requirements of critical sensors, fault-tolerant priority and queue are employed to adaptively adjust the channel bandwidth allocation. Simulation results show that AFTCS can alleviate the effect of channel impairments, while yielding lower packet loss rate and latency for critical sensors at runtime.

  19. Simultaneous Fault Detection and Sensor Selection for Condition Monitoring of Wind Turbines

    Directory of Open Access Journals (Sweden)

    Wenna Zhang

    2016-04-01

    Full Text Available Data collected from the supervisory control and data acquisition (SCADA system are used widely in wind farms to obtain operation and performance information about wind turbines. The paper presents a three-way model by means of parallel factor analysis (PARAFAC for wind turbine fault detection and sensor selection, and evaluates the method with SCADA data obtained from an operational farm. The main characteristic of this new approach is that it can be used to simultaneously explore measurement sample profiles and sensors profiles to avoid discarding potentially relevant information for feature extraction. With K-means clustering method, the measurement data indicating normal, fault and alarm conditions of the wind turbines can be identified, and the sensor array can be optimised for effective condition monitoring.

  20. Fault Diagnosis Based on Chemical Sensor Data with an Active Deep Neural Network.

    Science.gov (United States)

    Jiang, Peng; Hu, Zhixin; Liu, Jun; Yu, Shanen; Wu, Feng

    2016-10-13

    Big sensor data provide significant potential for chemical fault diagnosis, which involves the baseline values of security, stability and reliability in chemical processes. A deep neural network (DNN) with novel active learning for inducing chemical fault diagnosis is presented in this study. It is a method using large amount of chemical sensor data, which is a combination of deep learning and active learning criterion to target the difficulty of consecutive fault diagnosis. DNN with deep architectures, instead of shallow ones, could be developed through deep learning to learn a suitable feature representation from raw sensor data in an unsupervised manner using stacked denoising auto-encoder (SDAE) and work through a layer-by-layer successive learning process. The features are added to the top Softmax regression layer to construct the discriminative fault characteristics for diagnosis in a supervised manner. Considering the expensive and time consuming labeling of sensor data in chemical applications, in contrast to the available methods, we employ a novel active learning criterion for the particularity of chemical processes, which is a combination of Best vs. Second Best criterion (BvSB) and a Lowest False Positive criterion (LFP), for further fine-tuning of diagnosis model in an active manner rather than passive manner. That is, we allow models to rank the most informative sensor data to be labeled for updating the DNN parameters during the interaction phase. The effectiveness of the proposed method is validated in two well-known industrial datasets. Results indicate that the proposed method can obtain superior diagnosis accuracy and provide significant performance improvement in accuracy and false positive rate with less labeled chemical sensor data by further active learning compared with existing methods.

  1. Fault Diagnosis Based on Chemical Sensor Data with an Active Deep Neural Network

    Science.gov (United States)

    Jiang, Peng; Hu, Zhixin; Liu, Jun; Yu, Shanen; Wu, Feng

    2016-01-01

    Big sensor data provide significant potential for chemical fault diagnosis, which involves the baseline values of security, stability and reliability in chemical processes. A deep neural network (DNN) with novel active learning for inducing chemical fault diagnosis is presented in this study. It is a method using large amount of chemical sensor data, which is a combination of deep learning and active learning criterion to target the difficulty of consecutive fault diagnosis. DNN with deep architectures, instead of shallow ones, could be developed through deep learning to learn a suitable feature representation from raw sensor data in an unsupervised manner using stacked denoising auto-encoder (SDAE) and work through a layer-by-layer successive learning process. The features are added to the top Softmax regression layer to construct the discriminative fault characteristics for diagnosis in a supervised manner. Considering the expensive and time consuming labeling of sensor data in chemical applications, in contrast to the available methods, we employ a novel active learning criterion for the particularity of chemical processes, which is a combination of Best vs. Second Best criterion (BvSB) and a Lowest False Positive criterion (LFP), for further fine-tuning of diagnosis model in an active manner rather than passive manner. That is, we allow models to rank the most informative sensor data to be labeled for updating the DNN parameters during the interaction phase. The effectiveness of the proposed method is validated in two well-known industrial datasets. Results indicate that the proposed method can obtain superior diagnosis accuracy and provide significant performance improvement in accuracy and false positive rate with less labeled chemical sensor data by further active learning compared with existing methods. PMID:27754386

  2. Fault Diagnosis Based on Chemical Sensor Data with an Active Deep Neural Network

    Directory of Open Access Journals (Sweden)

    Peng Jiang

    2016-10-01

    Full Text Available Big sensor data provide significant potential for chemical fault diagnosis, which involves the baseline values of security, stability and reliability in chemical processes. A deep neural network (DNN with novel active learning for inducing chemical fault diagnosis is presented in this study. It is a method using large amount of chemical sensor data, which is a combination of deep learning and active learning criterion to target the difficulty of consecutive fault diagnosis. DNN with deep architectures, instead of shallow ones, could be developed through deep learning to learn a suitable feature representation from raw sensor data in an unsupervised manner using stacked denoising auto-encoder (SDAE and work through a layer-by-layer successive learning process. The features are added to the top Softmax regression layer to construct the discriminative fault characteristics for diagnosis in a supervised manner. Considering the expensive and time consuming labeling of sensor data in chemical applications, in contrast to the available methods, we employ a novel active learning criterion for the particularity of chemical processes, which is a combination of Best vs. Second Best criterion (BvSB and a Lowest False Positive criterion (LFP, for further fine-tuning of diagnosis model in an active manner rather than passive manner. That is, we allow models to rank the most informative sensor data to be labeled for updating the DNN parameters during the interaction phase. The effectiveness of the proposed method is validated in two well-known industrial datasets. Results indicate that the proposed method can obtain superior diagnosis accuracy and provide significant performance improvement in accuracy and false positive rate with less labeled chemical sensor data by further active learning compared with existing methods.

  3. Robust Fault Estimation Design for Discrete-Time Nonlinear Systems via A Modified Fuzzy Fault Estimation Observer.

    Science.gov (United States)

    Xie, Xiang-Peng; Yue, Dong; Park, Ju H

    2018-02-01

    The paper provides relaxed designs of fault estimation observer for nonlinear dynamical plants in the Takagi-Sugeno form. Compared with previous theoretical achievements, a modified version of fuzzy fault estimation observer is implemented with the aid of the so-called maximum-priority-based switching law. Given each activated switching status, the appropriate group of designed matrices can be provided so as to explore certain key properties of the considered plants by means of introducing a set of matrix-valued variables. Owing to the reason that more abundant information of the considered plants can be updated in due course and effectively exploited for each time instant, the conservatism of the obtained result is less than previous theoretical achievements and thus the main defect of those existing methods can be overcome to some extent in practice. Finally, comparative simulation studies on the classical nonlinear truck-trailer model are given to certify the benefits of the theoretic achievement which is obtained in our study. Copyright © 2017 ISA. Published by Elsevier Ltd. All rights reserved.

  4. Study of robust thin film PT-1000 temperature sensors for cryogenic process control applications

    Science.gov (United States)

    Ramalingam, R.; Boguhn, D.; Fillinger, H.; Schlachter, S. I.; Süßer, M.

    2014-01-01

    In some cryogenic process measurement applications, for example, in hydrogen technology and in high temperature superconductor based generators, there is a need of robust temperature sensors. These sensors should be able to measure the large temperature range of 20 - 500 K with reasonable resolution and accuracy. Thin film PT 1000 sensors could be a choice to cover this large temperature range. Twenty one sensors selected from the same production batch were tested for their temperature sensitivity which was then compared with different batch sensors. Furthermore, the sensor's stability was studied by subjecting the sensors to repeated temperature cycles of 78-525 K. Deviations in the resistance were investigated using ice point calibration and water triple point calibration methods. Also the study of directional oriented intense static magnetic field effects up to 8 Oersted (Oe) were conducted to understand its magneto resistance behaviour in the cryogenic temperature range from 77 K - 15 K. This paper reports all investigation results in detail.

  5. Fault Activity Aware Service Delivery in Wireless Sensor Networks for Smart Cities

    Directory of Open Access Journals (Sweden)

    Xiaomei Zhang

    2017-01-01

    Full Text Available Wireless sensor networks (WSNs are increasingly used in smart cities which involve multiple city services having quality of service (QoS requirements. When misbehaving devices exist, the performance of current delivery protocols degrades significantly. Nonetheless, the majority of existing schemes either ignore the faulty behaviors’ variability and time-variance in city environments or focus on homogeneous traffic for traditional data services (simple text messages rather than city services (health care units, traffic monitors, and video surveillance. We consider the problem of fault-aware multiservice delivery, in which the network performs secure routing and rate control in terms of fault activity dynamic metric. To this end, we first design a distributed framework to estimate the fault activity information based on the effects of nondeterministic faulty behaviors and to incorporate these estimates into the service delivery. Then we present a fault activity geographic opportunistic routing (FAGOR algorithm addressing a wide range of misbehaviors. We develop a leaky-hop model and design a fault activity rate-control algorithm for heterogeneous traffic to allocate resources, while guaranteeing utility fairness among multiple city services. Finally, we demonstrate the significant performance of our scheme in routing performance, effective utility, and utility fairness in the presence of misbehaving sensors through extensive simulations.

  6. SeRLoc: Robust Localization for Wireless Sensor Networks

    National Research Council Canada - National Science Library

    Lazos, Loukas; Poovendran, Radha

    2005-01-01

    .... We show that SeRLoc is robust against known attacks on WSNs such as the wormhole attack, the Sybil attack, and compromise of network entities and analytically compute the probability of success for each attack. We also compare the performance of SeRLoc with state-of-the-art range-independent localization schemes and show that SeRLoc has better performance.

  7. Robust Dehaze Algorithm for Degraded Image of CMOS Image Sensors

    Directory of Open Access Journals (Sweden)

    Chen Qu

    2017-09-01

    Full Text Available The CMOS (Complementary Metal-Oxide-Semiconductor is a new type of solid image sensor device widely used in object tracking, object recognition, intelligent navigation fields, and so on. However, images captured by outdoor CMOS sensor devices are usually affected by suspended atmospheric particles (such as haze, causing a reduction in image contrast, color distortion problems, and so on. In view of this, we propose a novel dehazing approach based on a local consistent Markov random field (MRF framework. The neighboring clique in traditional MRF is extended to the non-neighboring clique, which is defined on local consistent blocks based on two clues, where both the atmospheric light and transmission map satisfy the character of local consistency. In this framework, our model can strengthen the restriction of the whole image while incorporating more sophisticated statistical priors, resulting in more expressive power of modeling, thus, solving inadequate detail recovery effectively and alleviating color distortion. Moreover, the local consistent MRF framework can obtain details while maintaining better results for dehazing, which effectively improves the image quality captured by the CMOS image sensor. Experimental results verified that the method proposed has the combined advantages of detail recovery and color preservation.

  8. Real-time fault-tolerant moving horizon air data estimation for the RECONFIGURE benchmark

    NARCIS (Netherlands)

    Wan, Y.; Keviczky, T.

    2018-01-01

    This paper proposes a real-time fault-tolerant estimation approach for combined sensor fault diagnosis and air data reconstruction. Due to simultaneous influence of winds and latent faults on monitored sensors, it is challenging to address the tradeoff between robustness to wind disturbances and

  9. Intelligent Fault Diagnosis of Delta 3D Printers Using Attitude Sensors Based on Support Vector Machines

    Directory of Open Access Journals (Sweden)

    Kun He

    2018-04-01

    Full Text Available Health condition is a vital factor affecting printing quality for a 3D printer. In this work, an attitude monitoring approach is proposed to diagnose the fault of the delta 3D printer using support vector machines (SVM. An attitude sensor was mounted on the moving platform of the printer to monitor its 3-axial attitude angle, angular velocity, vibratory acceleration and magnetic field intensity. The attitude data of the working printer were collected under different conditions involving 12 fault types and a normal condition. The collected data were analyzed for diagnosing the health condition. To this end, the combination of binary classification, one-against-one with least-square SVM, was adopted for fault diagnosis modelling by using all channels of attitude monitoring data in the experiment. For comparison, each one channel of the attitude monitoring data was employed for model training and testing. On the other hand, a back propagation neural network (BPNN was also applied to diagnose fault using the same data. The best fault diagnosis accuracy (94.44% was obtained when all channels of the attitude monitoring data were used with SVM modelling. The results indicate that the attitude monitoring with SVM is an effective method for the fault diagnosis of delta 3D printers.

  10. Intelligent Fault Diagnosis of Delta 3D Printers Using Attitude Sensors Based on Support Vector Machines

    Science.gov (United States)

    He, Kun; Yang, Zhijun; Bai, Yun; Long, Jianyu; Li, Chuan

    2018-01-01

    Health condition is a vital factor affecting printing quality for a 3D printer. In this work, an attitude monitoring approach is proposed to diagnose the fault of the delta 3D printer using support vector machines (SVM). An attitude sensor was mounted on the moving platform of the printer to monitor its 3-axial attitude angle, angular velocity, vibratory acceleration and magnetic field intensity. The attitude data of the working printer were collected under different conditions involving 12 fault types and a normal condition. The collected data were analyzed for diagnosing the health condition. To this end, the combination of binary classification, one-against-one with least-square SVM, was adopted for fault diagnosis modelling by using all channels of attitude monitoring data in the experiment. For comparison, each one channel of the attitude monitoring data was employed for model training and testing. On the other hand, a back propagation neural network (BPNN) was also applied to diagnose fault using the same data. The best fault diagnosis accuracy (94.44%) was obtained when all channels of the attitude monitoring data were used with SVM modelling. The results indicate that the attitude monitoring with SVM is an effective method for the fault diagnosis of delta 3D printers. PMID:29690641

  11. Intelligent Fault Diagnosis of Delta 3D Printers Using Attitude Sensors Based on Support Vector Machines.

    Science.gov (United States)

    He, Kun; Yang, Zhijun; Bai, Yun; Long, Jianyu; Li, Chuan

    2018-04-23

    Health condition is a vital factor affecting printing quality for a 3D printer. In this work, an attitude monitoring approach is proposed to diagnose the fault of the delta 3D printer using support vector machines (SVM). An attitude sensor was mounted on the moving platform of the printer to monitor its 3-axial attitude angle, angular velocity, vibratory acceleration and magnetic field intensity. The attitude data of the working printer were collected under different conditions involving 12 fault types and a normal condition. The collected data were analyzed for diagnosing the health condition. To this end, the combination of binary classification, one-against-one with least-square SVM, was adopted for fault diagnosis modelling by using all channels of attitude monitoring data in the experiment. For comparison, each one channel of the attitude monitoring data was employed for model training and testing. On the other hand, a back propagation neural network (BPNN) was also applied to diagnose fault using the same data. The best fault diagnosis accuracy (94.44%) was obtained when all channels of the attitude monitoring data were used with SVM modelling. The results indicate that the attitude monitoring with SVM is an effective method for the fault diagnosis of delta 3D printers.

  12. Fault diagnosis for temperature, flow rate and pressure sensors in VAV systems using wavelet neural network

    Energy Technology Data Exchange (ETDEWEB)

    Du, Zhimin; Jin, Xinqiao; Yang, Yunyu [School of Mechanical Engineering, Shanghai Jiao Tong University, 800, Dongchuan Road, Shanghai (China)

    2009-09-15

    Wavelet neural network, the integration of wavelet analysis and neural network, is presented to diagnose the faults of sensors including temperature, flow rate and pressure in variable air volume (VAV) systems to ensure well capacity of energy conservation. Wavelet analysis is used to process the original data collected from the building automation first. With three-level wavelet decomposition, the series of characteristic information representing various operation conditions of the system are obtained. In addition, neural network is developed to diagnose the source of the fault. To improve the diagnosis efficiency, three data groups based on several physical models or balances are classified and constructed. Using the data decomposed by three-level wavelet, the neural network can be well trained and series of convergent networks are obtained. Finally, the new measurements to diagnose are similarly processed by wavelet. And the well-trained convergent neural networks are used to identify the operation condition and isolate the source of the fault. (author)

  13. Sensor Fault Diagnosis Observer for an Electric Vehicle Modeled as a Takagi-Sugeno System

    Directory of Open Access Journals (Sweden)

    S. Gómez-Peñate

    2018-01-01

    Full Text Available A sensor fault diagnosis of an electric vehicle (EV modeled as a Takagi-Sugeno (TS system is proposed. The proposed TS model considers the nonlinearity of the longitudinal velocity of the vehicle and parametric variation induced by the slope of the road; these considerations allow to obtain a mathematical model that represents the vehicle for a wide range of speeds and different terrain conditions. First, a virtual sensor represented by a TS state observer is developed. Sufficient conditions are given by a set of linear matrix inequalities (LMIs that guarantee asymptotic convergence of the TS observer. Second, the work is extended to perform fault detection and isolation based on a generalized observer scheme (GOS. Numerical simulations are presented to show the performance and applicability of the proposed method.

  14. An Improved Evidential-IOWA Sensor Data Fusion Approach in Fault Diagnosis.

    Science.gov (United States)

    Tang, Yongchuan; Zhou, Deyun; Zhuang, Miaoyan; Fang, Xueyi; Xie, Chunhe

    2017-09-18

    As an important tool of information fusion, Dempster-Shafer evidence theory is widely applied in handling the uncertain information in fault diagnosis. However, an incorrect result may be obtained if the combined evidence is highly conflicting, which may leads to failure in locating the fault. To deal with the problem, an improved evidential-Induced Ordered Weighted Averaging (IOWA) sensor data fusion approach is proposed in the frame of Dempster-Shafer evidence theory. In the new method, the IOWA operator is used to determine the weight of different sensor data source, while determining the parameter of the IOWA, both the distance of evidence and the belief entropy are taken into consideration. First, based on the global distance of evidence and the global belief entropy, the α value of IOWA is obtained. Simultaneously, a weight vector is given based on the maximum entropy method model. Then, according to IOWA operator, the evidence are modified before applying the Dempster's combination rule. The proposed method has a better performance in conflict management and fault diagnosis due to the fact that the information volume of each evidence is taken into consideration. A numerical example and a case study in fault diagnosis are presented to show the rationality and efficiency of the proposed method.

  15. Vehicle Lateral Control under Fault in Front and/or Rear Sensors

    OpenAIRE

    Huang, Jihua; Lu, Guang; Tomizuka, Masayoshi

    2000-01-01

    This report documents the findings of research performed under TO4204, "Vehicle Lateral Control under Fault in Front and/or Rear Sensors" during the year 2000- 2001. The research goal of TO4204 is to develop vehicle lateral control strategies under faulty operation of the magnetometers. The main objectives of the project are: (1) to design controllers that use the output from only one set of magnetometers, and (2) to develop an autonomous lateral control scheme that uses no magnetometers. New...

  16. Turbine engine rotor blade fault diagnostics through casing pressure and vibration sensors

    International Nuclear Information System (INIS)

    Cox, J; Anusonti-Inthra, P

    2014-01-01

    In this study, an exact solution is provided for a previously indeterminate equation used for rotor blade fault diagnostics. The method estimates rotor blade natural frequency through turbine engine casing pressure and vibration sensors. The equation requires accurate measurements of low-amplitude sideband signals in the frequency domain. With this in mind, statistical evaluation was also completed with the goal of determining the effect of sampling time and frequency on sideband resolution in the frequency domain

  17. Active Disturbance Rejection Approach for Robust Fault-Tolerant Control via Observer Assisted Sliding Mode Control

    Directory of Open Access Journals (Sweden)

    John Cortés-Romero

    2013-01-01

    Full Text Available This work proposes an active disturbance rejection approach for the establishment of a sliding mode control strategy in fault-tolerant operations. The core of the proposed active disturbance rejection assistance is a Generalized Proportional Integral (GPI observer which is in charge of the active estimation of lumped nonlinear endogenous and exogenous disturbance inputs related to the creation of local sliding regimes with limited control authority. Possibilities are explored for the GPI observer assisted sliding mode control in fault-tolerant schemes. Convincing improvements are presented with respect to classical sliding mode control strategies. As a collateral advantage, the observer-based control architecture offers the possibility of chattering reduction given that a significant part of the control signal is of the continuous type. The case study considers a classical DC motor control affected by actuator faults, parametric failures, and perturbations. Experimental results and comparisons with other established sliding mode controller design methodologies, which validate the proposed approach, are provided.

  18. Interference-Robust Transmission in Wireless Sensor Networks.

    Science.gov (United States)

    Han, Jin-Seok; Lee, Yong-Hwan

    2016-11-14

    Low-power wireless sensor networks (WSNs) operating in unlicensed spectrum bands may seriously suffer from interference from other coexisting radio systems, such as IEEE 802.11 wireless local area networks. In this paper, we consider the improvement of the transmission performance of low-power WSNs by adjusting the transmission rate and the payload size in response to the change of co-channel interference. We estimate the probability of transmission failure and the data throughput and then determine the payload size to maximize the throughput performance. We investigate that the transmission time maximizing the normalized throughput is not much affected by the transmission rate, but rather by the interference condition. We adjust the transmission rate and the transmission time in response to the change of the channel and interference condition, respectively. Finally, we verify the performance of the proposed scheme by computer simulation. The simulation results show that the proposed scheme significantly improves data throughput compared with conventional schemes while preserving energy efficiency even in the presence of interference.

  19. Multi-sensor information fusion method for vibration fault diagnosis of rolling bearing

    Science.gov (United States)

    Jiao, Jing; Yue, Jianhai; Pei, Di

    2017-10-01

    Bearing is a key element in high-speed electric multiple unit (EMU) and any defect of it can cause huge malfunctioning of EMU under high operation speed. This paper presents a new method for bearing fault diagnosis based on least square support vector machine (LS-SVM) in feature-level fusion and Dempster-Shafer (D-S) evidence theory in decision-level fusion which were used to solve the problems about low detection accuracy, difficulty in extracting sensitive characteristics and unstable diagnosis system of single-sensor in rolling bearing fault diagnosis. Wavelet de-nosing technique was used for removing the signal noises. LS-SVM was used to make pattern recognition of the bearing vibration signal, and then fusion process was made according to the D-S evidence theory, so as to realize recognition of bearing fault. The results indicated that the data fusion method improved the performance of the intelligent approach in rolling bearing fault detection significantly. Moreover, the results showed that this method can efficiently improve the accuracy of fault diagnosis.

  20. Robust Sensor-Orientation-Independent Feature Selection for Animal Activity Recognition on Collar Tags

    NARCIS (Netherlands)

    Kamminga, Jacob Wilhelm; Le Viet Duc, Duc Viet; Meijers, Jan Pieter; Bisby, Helena C.; Meratnia, Nirvana; Havinga, Paul J.M.

    2018-01-01

    Fundamental challenges faced by real-time animal activity recognition include variation in motion data due to changing sensor orientations, numerous features, and energy and processing constraints of animal tags. This paper aims at finding small optimal feature sets that are lightweight and robust

  1. Data-Driven Method for Wind Turbine Yaw Angle Sensor Zero-Point Shifting Fault Detection

    Directory of Open Access Journals (Sweden)

    Yan Pei

    2018-03-01

    Full Text Available Wind turbine yaw control plays an important role in increasing the wind turbine production and also in protecting the wind turbine. Accurate measurement of yaw angle is the basis of an effective wind turbine yaw controller. The accuracy of yaw angle measurement is affected significantly by the problem of zero-point shifting. Hence, it is essential to evaluate the zero-point shifting error on wind turbines on-line in order to improve the reliability of yaw angle measurement in real time. Particularly, qualitative evaluation of the zero-point shifting error could be useful for wind farm operators to realize prompt and cost-effective maintenance on yaw angle sensors. In the aim of qualitatively evaluating the zero-point shifting error, the yaw angle sensor zero-point shifting fault is firstly defined in this paper. A data-driven method is then proposed to detect the zero-point shifting fault based on Supervisory Control and Data Acquisition (SCADA data. The zero-point shifting fault is detected in the proposed method by analyzing the power performance under different yaw angles. The SCADA data are partitioned into different bins according to both wind speed and yaw angle in order to deeply evaluate the power performance. An indicator is proposed in this method for power performance evaluation under each yaw angle. The yaw angle with the largest indicator is considered as the yaw angle measurement error in our work. A zero-point shifting fault would trigger an alarm if the error is larger than a predefined threshold. Case studies from several actual wind farms proved the effectiveness of the proposed method in detecting zero-point shifting fault and also in improving the wind turbine performance. Results of the proposed method could be useful for wind farm operators to realize prompt adjustment if there exists a large error of yaw angle measurement.

  2. Observability analysis for model-based fault detection and sensor selection in induction motors

    International Nuclear Information System (INIS)

    Nakhaeinejad, Mohsen; Bryant, Michael D

    2011-01-01

    Sensors in different types and configurations provide information on the dynamics of a system. For a specific task, the question is whether measurements have enough information or whether the sensor configuration can be changed to improve the performance or to reduce costs. Observability analysis may answer the questions. This paper presents a general algorithm of nonlinear observability analysis with application to model-based diagnostics and sensor selection in three-phase induction motors. A bond graph model of the motor is developed and verified with experiments. A nonlinear observability matrix based on Lie derivatives is obtained from state equations. An observability index based on the singular value decomposition of the observability matrix is obtained. Singular values and singular vectors are used to identify the most and least observable configurations of sensors and parameters. A complex step derivative technique is used in the calculation of Jacobians to improve the computational performance of the observability analysis. The proposed algorithm of observability analysis can be applied to any nonlinear system to select the best configuration of sensors for applications of model-based diagnostics, observer-based controller, or to determine the level of sensor redundancy. Observability analysis on induction motors provides various sensor configurations with corresponding observability indices. Results show the redundancy levels for different sensors, and provide a sensor selection guideline for model-based diagnostics, and for observer-based controllers. The results can also be used for sensor fault detection and to improve the reliability of the system by increasing the redundancy level in measurements

  3. Analytical Redundancy Design for Aeroengine Sensor Fault Diagnostics Based on SROS-ELM

    Directory of Open Access Journals (Sweden)

    Jun Zhou

    2016-01-01

    Full Text Available Analytical redundancy technique is of great importance to guarantee the reliability and safety of aircraft engine system. In this paper, a machine learning based aeroengine sensor analytical redundancy technique is developed and verified through hardware-in-the-loop (HIL simulation. The modified online sequential extreme learning machine, selective updating regularized online sequential extreme learning machine (SROS-ELM, is employed to train the model online and estimate sensor measurements. It selectively updates the output weights of neural networks according to the prediction accuracy and the norm of output weight vector, tackles the problems of singularity and ill-posedness by regularization, and adopts a dual activation function in the hidden nodes combing neural and wavelet theory to enhance prediction capability. The experimental results verify the good generalization performance of SROS-ELM and show that the developed analytical redundancy technique for aeroengine sensor fault diagnosis based on SROS-ELM is effective and feasible.

  4. Gearbox Fault Diagnosis in a Wind Turbine Using Single Sensor Based Blind Source Separation

    Directory of Open Access Journals (Sweden)

    Yuning Qian

    2016-01-01

    Full Text Available This paper presents a single sensor based blind source separation approach, namely, the wavelet-assisted stationary subspace analysis (WSSA, for gearbox fault diagnosis in a wind turbine. Continuous wavelet transform (CWT is used as a preprocessing tool to decompose a single sensor measurement data into a set of wavelet coefficients to meet the multidimensional requirement of the stationary subspace analysis (SSA. The SSA is a blind source separation technique that can separate the multidimensional signals into stationary and nonstationary source components without the need for independency and prior information of the source signals. After that, the separated nonstationary source component with the maximum kurtosis value is analyzed by the enveloping spectral analysis to identify potential fault-related characteristic frequencies. Case studies performed on a wind turbine gearbox test system verify the effectiveness of the WSSA approach and indicate that it outperforms independent component analysis (ICA and empirical mode decomposition (EMD, as well as the spectral-kurtosis-based enveloping, for wind turbine gearbox fault diagnosis.

  5. Fault detection of sensors in nuclear reactors using self-organizing maps

    Energy Technology Data Exchange (ETDEWEB)

    Barbosa, Paulo Roberto; Tiago, Graziela Marchi [Instituto Federal de Educacao, Ciencia e Tecnologia de Sao Paulo (IFSP), Sao Paulo, SP (Brazil); Bueno, Elaine Inacio [Instituto Federal de Educacao, Ciencia e Tecnologia de Sao Paulo (IFSP), Guarulhos, SP (Brazil); Pereira, Iraci Martinez, E-mail: martinez@ipen.b [Instituto de Pesquisas Energeticas e Nucleares (IPEN/CNEN-SP), Sao Paulo, SP (Brazil)

    2011-07-01

    In this work a Fault Detection System was developed based on the self-organizing maps methodology. This method was applied to the IEA-R1 research reactor at IPEN using a database generated by a theoretical model of the reactor. The IEA-R1 research reactor is a pool type reactor of 5 MW, cooled and moderated by light water, and uses graphite and beryllium as reflector. The theoretical model was developed using the Matlab Guide toolbox. The equations are based in the IEA-R1 mass and energy inventory balance and physical as well as operational aspects are taken into consideration. In order to test the model ability for fault detection, faults were artificially produced. As the value of the maximum calibration error for special thermocouples is +- 0.5 deg C, it had been inserted faults in the sensor signals with the purpose to produce the database considered in this work. The results show a high percentage of correct classification, encouraging the use of the technique for this type of industrial application. (author)

  6. Gearbox Tooth Cut Fault Diagnostics Using Acoustic Emission and Vibration Sensors — A Comparative Study

    Directory of Open Access Journals (Sweden)

    Yongzhi Qu

    2014-01-01

    Full Text Available In recent years, acoustic emission (AE sensors and AE-based techniques have been developed and tested for gearbox fault diagnosis. In general, AE-based techniques require much higher sampling rates than vibration analysis-based techniques for gearbox fault diagnosis. Therefore, it is questionable whether an AE-based technique would give a better or at least the same performance as the vibration analysis-based techniques using the same sampling rate. To answer the question, this paper presents a comparative study for gearbox tooth damage level diagnostics using AE and vibration measurements, the first known attempt to compare the gearbox fault diagnostic performance of AE- and vibration analysis-based approaches using the same sampling rate. Partial tooth cut faults are seeded in a gearbox test rig and experimentally tested in a laboratory. Results have shown that the AE-based approach has the potential to differentiate gear tooth damage levels in comparison with the vibration-based approach. While vibration signals are easily affected by mechanical resonance, the AE signals show more stable performance.

  7. Fault detection of sensors in nuclear reactors using self-organizing maps

    International Nuclear Information System (INIS)

    Barbosa, Paulo Roberto; Tiago, Graziela Marchi; Bueno, Elaine Inacio; Pereira, Iraci Martinez

    2011-01-01

    In this work a Fault Detection System was developed based on the self-organizing maps methodology. This method was applied to the IEA-R1 research reactor at IPEN using a database generated by a theoretical model of the reactor. The IEA-R1 research reactor is a pool type reactor of 5 MW, cooled and moderated by light water, and uses graphite and beryllium as reflector. The theoretical model was developed using the Matlab Guide toolbox. The equations are based in the IEA-R1 mass and energy inventory balance and physical as well as operational aspects are taken into consideration. In order to test the model ability for fault detection, faults were artificially produced. As the value of the maximum calibration error for special thermocouples is +- 0.5 deg C, it had been inserted faults in the sensor signals with the purpose to produce the database considered in this work. The results show a high percentage of correct classification, encouraging the use of the technique for this type of industrial application. (author)

  8. Gearbox tooth cut fault diagnostics using acoustic emission and vibration sensors--a comparative study.

    Science.gov (United States)

    Qu, Yongzhi; He, David; Yoon, Jae; Van Hecke, Brandon; Bechhoefer, Eric; Zhu, Junda

    2014-01-14

    In recent years, acoustic emission (AE) sensors and AE-based techniques have been developed and tested for gearbox fault diagnosis. In general, AE-based techniques require much higher sampling rates than vibration analysis-based techniques for gearbox fault diagnosis. Therefore, it is questionable whether an AE-based technique would give a better or at least the same performance as the vibration analysis-based techniques using the same sampling rate. To answer the question, this paper presents a comparative study for gearbox tooth damage level diagnostics using AE and vibration measurements, the first known attempt to compare the gearbox fault diagnostic performance of AE- and vibration analysis-based approaches using the same sampling rate. Partial tooth cut faults are seeded in a gearbox test rig and experimentally tested in a laboratory. Results have shown that the AE-based approach has the potential to differentiate gear tooth damage levels in comparison with the vibration-based approach. While vibration signals are easily affected by mechanical resonance, the AE signals show more stable performance.

  9. Gearbox Tooth Cut Fault Diagnostics Using Acoustic Emission and Vibration Sensors — A Comparative Study

    Science.gov (United States)

    Qu, Yongzhi; He, David; Yoon, Jae; Van Hecke, Brandon; Bechhoefer, Eric; Zhu, Junda

    2014-01-01

    In recent years, acoustic emission (AE) sensors and AE-based techniques have been developed and tested for gearbox fault diagnosis. In general, AE-based techniques require much higher sampling rates than vibration analysis-based techniques for gearbox fault diagnosis. Therefore, it is questionable whether an AE-based technique would give a better or at least the same performance as the vibration analysis-based techniques using the same sampling rate. To answer the question, this paper presents a comparative study for gearbox tooth damage level diagnostics using AE and vibration measurements, the first known attempt to compare the gearbox fault diagnostic performance of AE- and vibration analysis-based approaches using the same sampling rate. Partial tooth cut faults are seeded in a gearbox test rig and experimentally tested in a laboratory. Results have shown that the AE-based approach has the potential to differentiate gear tooth damage levels in comparison with the vibration-based approach. While vibration signals are easily affected by mechanical resonance, the AE signals show more stable performance. PMID:24424467

  10. Virtual sensors for active noise control in acoustic-structural coupled enclosures using structural sensing: robust virtual sensor design.

    Science.gov (United States)

    Halim, Dunant; Cheng, Li; Su, Zhongqing

    2011-03-01

    The work was aimed to develop a robust virtual sensing design methodology for sensing and active control applications of vibro-acoustic systems. The proposed virtual sensor was designed to estimate a broadband acoustic interior sound pressure using structural sensors, with robustness against certain dynamic uncertainties occurring in an acoustic-structural coupled enclosure. A convex combination of Kalman sub-filters was used during the design, accommodating different sets of perturbed dynamic model of the vibro-acoustic enclosure. A minimax optimization problem was set up to determine an optimal convex combination of Kalman sub-filters, ensuring an optimal worst-case virtual sensing performance. The virtual sensing and active noise control performance was numerically investigated on a rectangular panel-cavity system. It was demonstrated that the proposed virtual sensor could accurately estimate the interior sound pressure, particularly the one dominated by cavity-controlled modes, by using a structural sensor. With such a virtual sensing technique, effective active noise control performance was also obtained even for the worst-case dynamics. © 2011 Acoustical Society of America

  11. Fault tolerant attitude control for small unmanned aircraft systems equipped with an airflow sensor array.

    Science.gov (United States)

    Shen, H; Xu, Y; Dickinson, B T

    2014-11-18

    Inspired by sensing strategies observed in birds and bats, a new attitude control concept of directly using real-time pressure and shear stresses has recently been studied. It was shown that with an array of onboard airflow sensors, small unmanned aircraft systems can promptly respond to airflow changes and improve flight performances. In this paper, a mapping function is proposed to compute aerodynamic moments from the real-time pressure and shear data in a practical and computationally tractable formulation. Since many microscale airflow sensors are embedded on the small unmanned aircraft system surface, it is highly possible that certain sensors may fail. Here, an adaptive control system is developed that is robust to sensor failure as well as other numerical mismatches in calculating real-time aerodynamic moments. The advantages of the proposed method are shown in the following simulation cases: (i) feedback pressure and wall shear data from a distributed array of 45 airflow sensors; (ii) 50% failure of the symmetrically distributed airflow sensor array; and (iii) failure of all the airflow sensors on one wing. It is shown that even if 50% of the airflow sensors have failures, the aircraft is still stable and able to track the attitude commands.

  12. Intelligent microchip networks: an agent-on-chip synthesis framework for the design of smart and robust sensor networks

    Science.gov (United States)

    Bosse, Stefan

    2013-05-01

    Sensorial materials consisting of high-density, miniaturized, and embedded sensor networks require new robust and reliable data processing and communication approaches. Structural health monitoring is one major field of application for sensorial materials. Each sensor node provides some kind of sensor, electronics, data processing, and communication with a strong focus on microchip-level implementation to meet the goals of miniaturization and low-power energy environments, a prerequisite for autonomous behaviour and operation. Reliability requires robustness of the entire system in the presence of node, link, data processing, and communication failures. Interaction between nodes is required to manage and distribute information. One common interaction model is the mobile agent. An agent approach provides stronger autonomy than a traditional object or remote-procedure-call based approach. Agents can decide for themselves, which actions are performed, and they are capable of flexible behaviour, reacting on the environment and other agents, providing some degree of robustness. Traditionally multi-agent systems are abstract programming models which are implemented in software and executed on program controlled computer architectures. This approach does not well scale to micro-chip level and requires full equipped computers and communication structures, and the hardware architecture does not consider and reflect the requirements for agent processing and interaction. We propose and demonstrate a novel design paradigm for reliable distributed data processing systems and a synthesis methodology and framework for multi-agent systems implementable entirely on microchip-level with resource and power constrained digital logic supporting Agent-On-Chip architectures (AoC). The agent behaviour and mobility is fully integrated on the micro-chip using pipelined communicating processes implemented with finite-state machines and register-transfer logic. The agent behaviour

  13. Reliability of Measured Data for pH Sensor Arrays with Fault Diagnosis and Data Fusion Based on LabVIEW

    OpenAIRE

    Liao, Yi-Hung; Chou, Jung-Chuan; Lin, Chin-Yi

    2013-01-01

    Fault diagnosis (FD) and data fusion (DF) technologies implemented in the LabVIEW program were used for a ruthenium dioxide pH sensor array. The purpose of the fault diagnosis and data fusion technologies is to increase the reliability of measured data. Data fusion is a very useful statistical method used for sensor arrays in many fields. Fault diagnosis is used to avoid sensor faults and to measure errors in the electrochemical measurement system, therefore, in this study, we use fault diagn...

  14. Analysis of field-oriented controlled induction motor drives under sensor faults and an overview of sensorless schemes.

    Science.gov (United States)

    Arun Dominic, D; Chelliah, Thanga Raj

    2014-09-01

    To obtain high dynamic performance on induction motor drives (IMD), variable voltage and variable frequency operation has to be performed by measuring speed of rotation and stator currents through sensors and fed back them to the controllers. When the sensors are undergone a fault, the stability of control system, may be designed for an industrial process, is disturbed. This paper studies the negative effects on a 12.5 hp induction motor drives when the field oriented control system is subjected to sensor faults. To illustrate the importance of this study mine hoist load diagram is considered as shaft load of the tested machine. The methods to recover the system from sensor faults are discussed. In addition, the various speed sensorless schemes are reviewed comprehensively. Copyright © 2014 ISA. Published by Elsevier Ltd. All rights reserved.

  15. Robust

    DEFF Research Database (Denmark)

    2017-01-01

    Robust – Reflections on Resilient Architecture’, is a scientific publication following the conference of the same name in November of 2017. Researches and PhD-Fellows, associated with the Masters programme: Cultural Heritage, Transformation and Restoration (Transformation), at The Royal Danish...

  16. A Self-Reconstructing Algorithm for Single and Multiple-Sensor Fault Isolation Based on Auto-Associative Neural Networks

    Directory of Open Access Journals (Sweden)

    Hamidreza Mousavi

    2017-01-01

    Full Text Available Recently different approaches have been developed in the field of sensor fault diagnostics based on Auto-Associative Neural Network (AANN. In this paper we present a novel algorithm called Self reconstructing Auto-Associative Neural Network (S-AANN which is able to detect and isolate single faulty sensor via reconstruction. We have also extended the algorithm to be applicable in multiple fault conditions. The algorithm uses a calibration model based on AANN. AANN can reconstruct the faulty sensor using non-faulty sensors due to correlation between the process variables, and mean of the difference between reconstructed and original data determines which sensors are faulty. The algorithms are tested on a Dimerization process. The simulation results show that the S-AANN can isolate multiple faulty sensors with low computational time that make the algorithm appropriate candidate for online applications.

  17. An Autonomous Self-Aware and Adaptive Fault Tolerant Routing Technique for Wireless Sensor Networks.

    Science.gov (United States)

    Abba, Sani; Lee, Jeong-A

    2015-08-18

    We propose an autonomous self-aware and adaptive fault-tolerant routing technique (ASAART) for wireless sensor networks. We address the limitations of self-healing routing (SHR) and self-selective routing (SSR) techniques for routing sensor data. We also examine the integration of autonomic self-aware and adaptive fault detection and resiliency techniques for route formation and route repair to provide resilience to errors and failures. We achieved this by using a combined continuous and slotted prioritized transmission back-off delay to obtain local and global network state information, as well as multiple random functions for attaining faster routing convergence and reliable route repair despite transient and permanent node failure rates and efficient adaptation to instantaneous network topology changes. The results of simulations based on a comparison of the ASAART with the SHR and SSR protocols for five different simulated scenarios in the presence of transient and permanent node failure rates exhibit a greater resiliency to errors and failure and better routing performance in terms of the number of successfully delivered network packets, end-to-end delay, delivered MAC layer packets, packet error rate, as well as efficient energy conservation in a highly congested, faulty, and scalable sensor network.

  18. An Autonomous Self-Aware and Adaptive Fault Tolerant Routing Technique for Wireless Sensor Networks

    Science.gov (United States)

    Abba, Sani; Lee, Jeong-A

    2015-01-01

    We propose an autonomous self-aware and adaptive fault-tolerant routing technique (ASAART) for wireless sensor networks. We address the limitations of self-healing routing (SHR) and self-selective routing (SSR) techniques for routing sensor data. We also examine the integration of autonomic self-aware and adaptive fault detection and resiliency techniques for route formation and route repair to provide resilience to errors and failures. We achieved this by using a combined continuous and slotted prioritized transmission back-off delay to obtain local and global network state information, as well as multiple random functions for attaining faster routing convergence and reliable route repair despite transient and permanent node failure rates and efficient adaptation to instantaneous network topology changes. The results of simulations based on a comparison of the ASAART with the SHR and SSR protocols for five different simulated scenarios in the presence of transient and permanent node failure rates exhibit a greater resiliency to errors and failure and better routing performance in terms of the number of successfully delivered network packets, end-to-end delay, delivered MAC layer packets, packet error rate, as well as efficient energy conservation in a highly congested, faulty, and scalable sensor network. PMID:26295236

  19. A Wireless Sensor System for Real-Time Monitoring and Fault Detection of Motor Arrays.

    Science.gov (United States)

    Medina-García, Jonathan; Sánchez-Rodríguez, Trinidad; Galán, Juan Antonio Gómez; Delgado, Aránzazu; Gómez-Bravo, Fernando; Jiménez, Raúl

    2017-02-25

    This paper presents a wireless fault detection system for industrial motors that combines vibration, motor current and temperature analysis, thus improving the detection of mechanical faults. The design also considers the time of detection and further possible actions, which are also important for the early detection of possible malfunctions, and thus for avoiding irreversible damage to the motor. The remote motor condition monitoring is implemented through a wireless sensor network (WSN) based on the IEEE 802.15.4 standard. The deployed network uses the beacon-enabled mode to synchronize several sensor nodes with the coordinator node, and the guaranteed time slot mechanism provides data monitoring with a predetermined latency. A graphic user interface offers remote access to motor conditions and real-time monitoring of several parameters. The developed wireless sensor node exhibits very low power consumption since it has been optimized both in terms of hardware and software. The result is a low cost, highly reliable and compact design, achieving a high degree of autonomy of more than two years with just one 3.3 V/2600 mAh battery. Laboratory and field tests confirm the feasibility of the wireless system.

  20. Designing a robust high-speed CMOS-MEMS capacitive humidity sensor

    International Nuclear Information System (INIS)

    Lazarus, N; Fedder, G K

    2012-01-01

    In our previous work (Lazarus and Fedder 2011 J. Micromech. Microeng. 21 0650281), we demonstrated a CMOS-MEMS capacitive humidity sensor with a 72% improvement in sensitivity over the highest previously integrated on a CMOS die. This paper explores a series of methods for creating a faster and more manufacturable high-sensitivity capacitive humidity sensor. These techniques include adding oxide pillars to hold the plates apart, spin coating polymer to allow sensors to be fabricated more cheaply, adding a polysilicon heater and etching away excess polymer in the release holes. In most cases a tradeoff was found between sensitivity and other factors such as response time or robustness. A robust high-speed sensor was designed with a sensitivity of 0.21% change in capacitance per per cent relative humidity, while dropping the response time constant from 70 to 4s. Although less sensitive than our design, the sensor remains 17% more sensitive than the most sensitive interdigitated designs successfully integrated with CMOS. (paper)

  1. Robust Sequential Covariance Intersection Fusion Kalman Filtering over Multi-agent Sensor Networks with Measurement Delays and Uncertain Noise Variances

    Institute of Scientific and Technical Information of China (English)

    QI Wen-Juan; ZHANG Peng; DENG Zi-Li

    2014-01-01

    This paper deals with the problem of designing robust sequential covariance intersection (SCI) fusion Kalman filter for the clustering multi-agent sensor network system with measurement delays and uncertain noise variances. The sensor network is partitioned into clusters by the nearest neighbor rule. Using the minimax robust estimation principle, based on the worst-case conservative sensor network system with conservative upper bounds of noise variances, and applying the unbiased linear minimum variance (ULMV) optimal estimation rule, we present the two-layer SCI fusion robust steady-state Kalman filter which can reduce communication and computation burdens and save energy sources, and guarantee that the actual filtering error variances have a less-conservative upper-bound. A Lyapunov equation method for robustness analysis is proposed, by which the robustness of the local and fused Kalman filters is proved. The concept of the robust accuracy is presented and the robust accuracy relations of the local and fused robust Kalman filters are proved. It is proved that the robust accuracy of the global SCI fuser is higher than those of the local SCI fusers and the robust accuracies of all SCI fusers are higher than that of each local robust Kalman filter. A simulation example for a tracking system verifies the robustness and robust accuracy relations.

  2. Fault Residuals for Compact Disc Players based on Redundant and Non-linear Sensors

    DEFF Research Database (Denmark)

    Odgaard, Peter Fogh; Stoustrup, Jakob; Andersen, Palle

    2004-01-01

    In the process of achieving good performance of Compact Disc players it is important to handle surface defects as good as possible. The first thing in handling these defects is to detect their beginnings and ends.Two servo loops are formed to keep the optical pick-up focused on and radially tracked...... at the information track on the Compact Disc. The pick-up feeds the controllers with two sensor signals for each of the loops. The difference between these two signal pairs is an approximations of focus and radial tracking distances. The sum of these signal pairs is used for fault detection. But due to optical cross...

  3. A Robust Fiber Bragg Grating Hydrogen Gas Sensor Using Platinum-Supported Silica Catalyst Film

    OpenAIRE

    Marina Kurohiji; Seiji Ichiriyama; Naoki Yamasaku; Shinji Okazaki; Naoya Kasai; Yusuke Maru; Tadahito Mizutani

    2018-01-01

    A robust fiber Bragg grating (FBG) hydrogen gas sensor for reliable multipoint-leakage monitoring has been developed. The sensing mechanism is based on shifts of center wavelength of the reflection spectra due to temperature change caused by catalytic combustion heat. The sensitive film which consists of platinum-supported silica (Pt/SiO2) catalyst film was obtained using sol-gel method. The precursor solution was composed of hexachloroplatinic acid and commercially available silica precursor...

  4. Sensor-driven, fault-tolerant control of a maintenance robot

    International Nuclear Information System (INIS)

    Moy, M.M.; Davidson, W.M.

    1987-01-01

    A robot system has been designed to do routine maintenance tasks on the Sandia Pulsed Reactor (SPR). The use of this Remote Maintenance Robot (RMR) is expected to significantly reduce the occupational radiation exposure of the reactor operators. Reactor safety was a key issue in the design of the robot maintenance system. Using sensors to detect error conditions and intelligent control to recover from the errors, the RMR is capable of responding to error conditions without creating a hazard. This paper describes the design and implementation of a sensor-driven, fault-tolerant control for the RMR. Recovery from errors is not automatic; it does rely on operator assistance. However, a key feature of the error recovery procedure is that the operator is allowed to reenter the programmed operation after the error has been corrected. The recovery procedure guarantees that the moving components of the system will not collide with the reactor during recovery

  5. Robust gold nanoparticles stabilized by trithiol for application in chemiresistive sensors

    Energy Technology Data Exchange (ETDEWEB)

    Garg, Niti; Mohanty, Ashok; Jin, Rongchao [Department of Chemistry, Carnegie Mellon University, Pittsburgh, PA 15213 (United States); Lazarus, Nathan; Santhanam, Suresh; Fedder, Gary K [Department of Electrical and Computer Engineering, Carnegie Mellon University, Pittsburgh, PA 15213 (United States); Schultz, Lawrence; Weiss, Lee [Robotics Institute, Carnegie Mellon University, Pittsburgh, PA 15213 (United States); Rozzi, Tony R; Snyder, Jay L, E-mail: zpx5@cdc.gov, E-mail: fedder@ece.cmu.edu, E-mail: rongchao@andrew.cmu.edu [National Institute for Occupational Safety and Health (NIOSH), Pittsburgh, PA 15236 (United States)

    2010-10-08

    The use of gold nanoparticles coated with an organic monolayer of thiol for application in chemiresistive sensors was initiated in the late 1990s; since then, such types of sensors have been widely pursued due to their high sensitivities and reversible responses to volatile organic compounds (VOCs). However, a major issue for chemical sensors based on thiol-capped gold nanoparticles is their poor long-term stability as a result of slow degradation of the monothiol-to-gold bonds. We have devised a strategy to overcome this limitation by synthesizing a more robust system using Au nanoparticles capped by trithiol ligands. Compared to its monothiol counterpart, the new system is significantly more stable and also shows improved sensitivity towards different types of polar or non-polar VOCs. Thus, the trithiol-Au nanosensor shows great promise for use in real world applications.

  6. Robust gold nanoparticles stabilized by trithiol for application in chemiresistive sensors

    International Nuclear Information System (INIS)

    Garg, Niti; Mohanty, Ashok; Jin, Rongchao; Lazarus, Nathan; Santhanam, Suresh; Fedder, Gary K; Schultz, Lawrence; Weiss, Lee; Rozzi, Tony R; Snyder, Jay L

    2010-01-01

    The use of gold nanoparticles coated with an organic monolayer of thiol for application in chemiresistive sensors was initiated in the late 1990s; since then, such types of sensors have been widely pursued due to their high sensitivities and reversible responses to volatile organic compounds (VOCs). However, a major issue for chemical sensors based on thiol-capped gold nanoparticles is their poor long-term stability as a result of slow degradation of the monothiol-to-gold bonds. We have devised a strategy to overcome this limitation by synthesizing a more robust system using Au nanoparticles capped by trithiol ligands. Compared to its monothiol counterpart, the new system is significantly more stable and also shows improved sensitivity towards different types of polar or non-polar VOCs. Thus, the trithiol-Au nanosensor shows great promise for use in real world applications.

  7. Data-driven fault detection, isolation and estimation of aircraft gas turbine engine actuator and sensors

    Science.gov (United States)

    Naderi, E.; Khorasani, K.

    2018-02-01

    In this work, a data-driven fault detection, isolation, and estimation (FDI&E) methodology is proposed and developed specifically for monitoring the aircraft gas turbine engine actuator and sensors. The proposed FDI&E filters are directly constructed by using only the available system I/O data at each operating point of the engine. The healthy gas turbine engine is stimulated by a sinusoidal input containing a limited number of frequencies. First, the associated system Markov parameters are estimated by using the FFT of the input and output signals to obtain the frequency response of the gas turbine engine. These data are then used for direct design and realization of the fault detection, isolation and estimation filters. Our proposed scheme therefore does not require any a priori knowledge of the system linear model or its number of poles and zeros at each operating point. We have investigated the effects of the size of the frequency response data on the performance of our proposed schemes. We have shown through comprehensive case studies simulations that desirable fault detection, isolation and estimation performance metrics defined in terms of the confusion matrix criterion can be achieved by having access to only the frequency response of the system at only a limited number of frequencies.

  8. Aircraft engine sensor fault diagnostics using an on-line OBEM update method.

    Directory of Open Access Journals (Sweden)

    Xiaofeng Liu

    Full Text Available This paper proposed a method to update the on-line health reference baseline of the On-Board Engine Model (OBEM to maintain the effectiveness of an in-flight aircraft sensor Fault Detection and Isolation (FDI system, in which a Hybrid Kalman Filter (HKF was incorporated. Generated from a rapid in-flight engine degradation, a large health condition mismatch between the engine and the OBEM can corrupt the performance of the FDI. Therefore, it is necessary to update the OBEM online when a rapid degradation occurs, but the FDI system will lose estimation accuracy if the estimation and update are running simultaneously. To solve this problem, the health reference baseline for a nonlinear OBEM was updated using the proposed channel controller method. Simulations based on the turbojet engine Linear-Parameter Varying (LPV model demonstrated the effectiveness of the proposed FDI system in the presence of substantial degradation, and the channel controller can ensure that the update process finishes without interference from a single sensor fault.

  9. Research of diagnosis sensors fault based on correlation analysis of the bridge structural health monitoring system

    Science.gov (United States)

    Hu, Shunren; Chen, Weimin; Liu, Lin; Gao, Xiaoxia

    2010-03-01

    Bridge structural health monitoring system is a typical multi-sensor measurement system due to the multi-parameters of bridge structure collected from the monitoring sites on the river-spanning bridges. Bridge structure monitored by multi-sensors is an entity, when subjected to external action; there will be different performances to different bridge structure parameters. Therefore, the data acquired by each sensor should exist countless correlation relation. However, complexity of the correlation relation is decided by complexity of bridge structure. Traditionally correlation analysis among monitoring sites is mainly considered from physical locations. unfortunately, this method is so simple that it cannot describe the correlation in detail. The paper analyzes the correlation among the bridge monitoring sites according to the bridge structural data, defines the correlation of bridge monitoring sites and describes its several forms, then integrating the correlative theory of data mining and signal system to establish the correlation model to describe the correlation among the bridge monitoring sites quantificationally. Finally, The Chongqing Mashangxi Yangtze river bridge health measurement system is regards as research object to diagnosis sensors fault, and simulation results verify the effectiveness of the designed method and theoretical discussions.

  10. Robust observer-based fault diagnosis for nonlinear systems using Matlab

    CERN Document Server

    Zhang, Jian; Nguang, Sing Kiong

    2016-01-01

    This book introduces several observer-based methods, including: • the sliding-mode observer • the adaptive observer • the unknown-input observer and • the descriptor observer method for the problem of fault detection, isolation and estimation, allowing readers to compare and contrast the different approaches. The authors present basic material on Lyapunov stability theory, H¥ control theory, sliding-mode control theory and linear matrix inequality problems in a self-contained and step-by-step manner. Detailed and rigorous mathematical proofs are provided for all the results developed in the text so that readers can quickly gain a good understanding of the material. MATLAB® and Simulink® codes for all the examples, which can be downloaded from http://extras.springer.com, enable students to follow the methods and illustrative examples easily. The systems used in the examples make the book highly relevant to real-world problems in industrial control engineering and include a seventh-order aircraft mod...

  11. Miniature robust five-dimensional fingertip force/torque sensor with high performance

    International Nuclear Information System (INIS)

    Liang, Qiaokang; Huang, Xiuxiang; Li, Zhongyang; Zhang, Dan; Ge, Yunjian

    2011-01-01

    This paper proposes an innovative design and investigation for a five-dimensional fingertip force/torque sensor with a dual annular diaphragm. This sensor can be applied to a robot hand to measure forces along the X-, Y- and Z-axes (F x , F y and F z ) and moments about the X- and Y-axes (M x and M y ) simultaneously. Particularly, the details of the sensing principle, the structural design and the overload protection mechanism are presented. Afterward, based on the design of experiments approach provided by the software ANSYS®, a finite element analysis and an optimization design are performed. These are performed with the objective of achieving both high sensitivity and stiffness of the sensor. Furthermore, static and dynamic calibrations based on the neural network method are carried out. Finally, an application of the developed sensor on a dexterous robot hand is demonstrated. The results of calibration experiments and the application show that the developed sensor possesses high performance and robustness

  12. Robust Adaptive Beamforming with Sensor Position Errors Using Weighted Subspace Fitting-Based Covariance Matrix Reconstruction.

    Science.gov (United States)

    Chen, Peng; Yang, Yixin; Wang, Yong; Ma, Yuanliang

    2018-05-08

    When sensor position errors exist, the performance of recently proposed interference-plus-noise covariance matrix (INCM)-based adaptive beamformers may be severely degraded. In this paper, we propose a weighted subspace fitting-based INCM reconstruction algorithm to overcome sensor displacement for linear arrays. By estimating the rough signal directions, we construct a novel possible mismatched steering vector (SV) set. We analyze the proximity of the signal subspace from the sample covariance matrix (SCM) and the space spanned by the possible mismatched SV set. After solving an iterative optimization problem, we reconstruct the INCM using the estimated sensor position errors. Then we estimate the SV of the desired signal by solving an optimization problem with the reconstructed INCM. The main advantage of the proposed algorithm is its robustness against SV mismatches dominated by unknown sensor position errors. Numerical examples show that even if the position errors are up to half of the assumed sensor spacing, the output signal-to-interference-plus-noise ratio is only reduced by 4 dB. Beam patterns plotted using experiment data show that the interference suppression capability of the proposed beamformer outperforms other tested beamformers.

  13. Robust and Agile System against Fault and Anomaly Traffic in Software Defined Networks

    Directory of Open Access Journals (Sweden)

    Mihui Kim

    2017-03-01

    Full Text Available The main advantage of software defined networking (SDN is that it allows intelligent control and management of networking though programmability in real time. It enables efficient utilization of network resources through traffic engineering, and offers potential attack defense methods when abnormalities arise. However, previous studies have only identified individual solutions for respective problems, instead of finding a more global solution in real time that is capable of addressing multiple situations in network status. To cover diverse network conditions, this paper presents a comprehensive reactive system for simultaneously monitoring failures, anomalies, and attacks for high availability and reliability. We design three main modules in the SDN controller for a robust and agile defense (RAD system against network anomalies: a traffic analyzer, a traffic engineer, and a rule manager. RAD provides reactive flow rule generation to control traffic while detecting network failures, anomalies, high traffic volume (elephant flows, and attacks. The traffic analyzer identifies elephant flows, traffic anomalies, and attacks based on attack signatures and network monitoring. The traffic engineer module measures network utilization and delay in order to determine the best path for multi-dimensional routing and load balancing under any circumstances. Finally, the rule manager generates and installs a flow rule for the selected best path to control traffic. We implement the proposed RAD system based on Floodlight, an open source project for the SDN controller. We evaluate our system using simulation with and without the aforementioned RAD modules. Experimental results show that our approach is both practical and feasible, and can successfully augment an existing SDN controller in terms of agility, robustness, and efficiency, even in the face of link failures, attacks, and elephant flows.

  14. A Robust Fiber Bragg Grating Hydrogen Gas Sensor Using Platinum-Supported Silica Catalyst Film

    Directory of Open Access Journals (Sweden)

    Marina Kurohiji

    2018-01-01

    Full Text Available A robust fiber Bragg grating (FBG hydrogen gas sensor for reliable multipoint-leakage monitoring has been developed. The sensing mechanism is based on shifts of center wavelength of the reflection spectra due to temperature change caused by catalytic combustion heat. The sensitive film which consists of platinum-supported silica (Pt/SiO2 catalyst film was obtained using sol-gel method. The precursor solution was composed of hexachloroplatinic acid and commercially available silica precursor solution. The atom ratio of Si : Pt was fixed at 13 : 1. A small amount of this solution was dropped on the substrate and dried at room temperature. After that, the film was calcined at 500°C in air. These procedures were repeated and therefore thick hydrogen-sensitive films were obtained. The catalytic film obtained by 20-time coating on quartz glass substrate showed a temperature change 75 K upon exposure to 3 vol.% H2. For realizing robust sensor device, this catalytic film was deposited and FBG portion was directly fixed on titanium substrate. The sensor device showed good performances enough to detect hydrogen gas in the concentration range below lower explosion limit at room temperature. The enhancement of the sensitivity was attributed to not only catalytic combustion heat but also related thermal strain.

  15. Group method of data handling and neral networks applied in monitoring and fault detection in sensors in nuclear power plants

    International Nuclear Information System (INIS)

    Bueno, Elaine Inacio

    2011-01-01

    The increasing demand in the complexity, efficiency and reliability in modern industrial systems stimulated studies on control theory applied to the development of Monitoring and Fault Detection system. In this work a new Monitoring and Fault Detection methodology was developed using GMDH (Group Method of Data Handling) algorithm and Artificial Neural Networks (ANNs) which was applied to the IEA-R1 research reactor at IPEN. The Monitoring and Fault Detection system was developed in two parts: the first was dedicated to preprocess information, using GMDH algorithm; and the second part to the process information using ANNs. The GMDH algorithm was used in two different ways: firstly, the GMDH algorithm was used to generate a better database estimated, called matrix z , which was used to train the ANNs. After that, the GMDH was used to study the best set of variables to be used to train the ANNs, resulting in a best monitoring variable estimative. The methodology was developed and tested using five different models: one Theoretical Model and four Models using different sets of reactor variables. After an exhausting study dedicated to the sensors Monitoring, the Fault Detection in sensors was developed by simulating faults in the sensors database using values of 5%, 10%, 15% and 20% in these sensors database. The results obtained using GMDH algorithm in the choice of the best input variables to the ANNs were better than that using only ANNs, thus making possible the use of these methods in the implementation of a new Monitoring and Fault Detection methodology applied in sensors. (author)

  16. Optimal Design of the Absolute Positioning Sensor for a High-Speed Maglev Train and Research on Its Fault Diagnosis

    Directory of Open Access Journals (Sweden)

    Junge Zhang

    2012-08-01

    Full Text Available This paper studies an absolute positioning sensor for a high-speed maglev train and its fault diagnosis method. The absolute positioning sensor is an important sensor for the high-speed maglev train to accomplish its synchronous traction. It is used to calibrate the error of the relative positioning sensor which is used to provide the magnetic phase signal. On the basis of the analysis for the principle of the absolute positioning sensor, the paper describes the design of the sending and receiving coils and realizes the hardware and the software for the sensor. In order to enhance the reliability of the sensor, a support vector machine is used to recognize the fault characters, and the signal flow method is used to locate the faulty parts. The diagnosis information not only can be sent to an upper center control computer to evaluate the reliability of the sensors, but also can realize on-line diagnosis for debugging and the quick detection when the maglev train is off-line. The absolute positioning sensor we study has been used in the actual project.

  17. Optimal design of the absolute positioning sensor for a high-speed maglev train and research on its fault diagnosis.

    Science.gov (United States)

    Zhang, Dapeng; Long, Zhiqiang; Xue, Song; Zhang, Junge

    2012-01-01

    This paper studies an absolute positioning sensor for a high-speed maglev train and its fault diagnosis method. The absolute positioning sensor is an important sensor for the high-speed maglev train to accomplish its synchronous traction. It is used to calibrate the error of the relative positioning sensor which is used to provide the magnetic phase signal. On the basis of the analysis for the principle of the absolute positioning sensor, the paper describes the design of the sending and receiving coils and realizes the hardware and the software for the sensor. In order to enhance the reliability of the sensor, a support vector machine is used to recognize the fault characters, and the signal flow method is used to locate the faulty parts. The diagnosis information not only can be sent to an upper center control computer to evaluate the reliability of the sensors, but also can realize on-line diagnosis for debugging and the quick detection when the maglev train is off-line. The absolute positioning sensor we study has been used in the actual project.

  18. A Robust, Enzyme-Free Glucose Sensor Based on Lysine-Assisted CuO Nanostructures

    Directory of Open Access Journals (Sweden)

    Qurrat-ul-Ain Baloach

    2016-11-01

    Full Text Available The production of a nanomaterial with enhanced and desirable electrocatalytic properties is of prime importance, and the commercialization of devices containing these materials is a challenging task. In this study, unique cupric oxide (CuO nanostructures were synthesized using lysine as a soft template for the evolution of morphology via a rapid and boiled hydrothermal method. The morphology and structure of the synthesized CuO nanomaterial were characterized using scanning electron microscopy (SEM and X-ray diffraction (XRD, respectively. The prepared CuO nanostructures showed high potential for use in the electrocatalytic oxidation of glucose in an alkaline medium. The proposed enzyme-free glucose sensor demonstrated a robust response to glucose with a wide linear range and high sensitivity, selectivity, stability, and reproducibility. To explore its practical feasibility, the glucose content of serum samples was successfully determined using the enzyme-free sensor. An analytical recovery method was used to measure the actual glucose from the serum samples, and the results were satisfactory. Moreover, the presented glucose sensor has high chemical stability and can be reused for repetitive measurements. This study introduces an enzyme-free glucose sensor as an alternative tool for clinical glucose quantification.

  19. A Soft Sensor-Based Fault-Tolerant Control on the Air Fuel Ratio of Spark-Ignition Engines

    Directory of Open Access Journals (Sweden)

    Yu-Jia Zhai

    2017-01-01

    Full Text Available The air/fuel ratio (AFR regulation for spark-ignition (SI engines has been an essential and challenging control problem for engineers in the automotive industry. The feed-forward and feedback scheme has been investigated in both academic research and industrial application. The aging effect can often cause an AFR sensor fault in the feedback loop, and the AFR control performance will degrade consequently. In this research, a new control scheme on AFR with fault-tolerance is proposed by using an artificial neural network model based on fault detection and compensation, which can provide the satisfactory AFR regulation performance at the stoichiometric value for the combustion process, given a certain level of misreading of the AFR sensor.

  20. Sensor fault analysis using decision theory and data-driven modeling of pressurized water reactor subsystems

    International Nuclear Information System (INIS)

    Upadhyaya, B.R.; Skorska, M.

    1984-01-01

    Instrument fault detection and estimation is important for process surveillance, control, and safety functions of a power plant. The method incorporates the dual-hypotheses decision procedure and system characterization using data-driven time-domain models of signals representing the system. The multivariate models can be developed on-line and can be adapted to changing system conditions. For the method to be effective, specific subsystems of pressurized water reactors were considered, and signal selection was made such that a strong causal relationship exists among the measured variables. The technique is applied to the reactor core subsystem of the loss-of-fluid test reactor using in-core neutron detector and core-exit thermocouple signals. Thermocouple anomalies such as bias error, noise error, and slow drift in the sensor are detected and estimated using appropriate measurement models

  1. Fault-tolerant Control of Discrete-time LPV systems using Virtual Actuators and Sensors

    DEFF Research Database (Denmark)

    Tabatabaeipour, Mojtaba; Stoustrup, Jakob; Bak, Thomas

    2015-01-01

    This paper proposes a new fault-tolerant control (FTC) method for discrete-time linear parameter varying (LPV) systems using a reconfiguration block. The basic idea of the method is to achieve the FTC goal without re-designing the nominal controller by inserting a reconfiguration block between......, it transforms the output of the controller for the faulty system such that the stability and performance goals are preserved. Input-to-state stabilizing LPV gains of the virtual actuator and sensor are obtained by solving linear matrix inequalities (LMIs). We show that separate design of these gains guarantees....... Finally, the effectiveness of the method is demonstrated via a numerical example and stator current control of an induction motor....

  2. Energy-Efficient Fault-Tolerant Dynamic Event Region Detection in Wireless Sensor Networks

    DEFF Research Database (Denmark)

    Enemark, Hans-Jacob; Zhang, Yue; Dragoni, Nicola

    2015-01-01

    to a hybrid algorithm for dynamic event region detection, such as real-time tracking of chemical leakage regions. Considering the characteristics of the moving away dynamic events, we propose a return back condition for the hybrid algorithm from distributed neighborhood collaboration, in which a node makes......Fault-tolerant event detection is fundamental to wireless sensor network applications. Existing approaches usually adopt neighborhood collaboration for better detection accuracy, while need more energy consumption due to communication. Focusing on energy efficiency, this paper makes an improvement...... its detection decision based on decisions received from its spatial and temporal neighbors, to local non-communicative decision making. The simulation results demonstrate that the improved algorithm does not degrade the detection accuracy of the original algorithm, while it has better energy...

  3. Spirally Structured Conductive Composites for Highly Stretchable, Robust Conductors and Sensors.

    Science.gov (United States)

    Wu, Xiaodong; Han, Yangyang; Zhang, Xinxing; Lu, Canhui

    2017-07-12

    Flexible and stretchable electronics are highly desirable for next generation devices. However, stretchability and conductivity are fundamentally difficult to combine for conventional conductive composites, which restricts their widespread applications especially as stretchable electronics. Here, we innovatively develop a new class of highly stretchable and robust conductive composites via a simple and scalable structural approach. Briefly, carbon nanotubes are spray-coated onto a self-adhesive rubber film, followed by rolling up the film completely to create a spirally layered structure within the composites. This unique spirally layered structure breaks the typical trade-off between stretchability and conductivity of traditional conductive composites and, more importantly, restrains the generation and propagation of mechanical microcracks in the conductive layer under strain. Benefiting from such structure-induced advantages, the spirally layered composites exhibit high stretchability and flexibility, good conductive stability, and excellent robustness, enabling the composites to serve as highly stretchable conductors (up to 300% strain), versatile sensors for monitoring both subtle and large human activities, and functional threads for wearable electronics. This novel and efficient methodology provides a new design philosophy for manufacturing not only stretchable conductors and sensors but also other stretchable electronics, such as transistors, generators, artificial muscles, etc.

  4. Multi-Unmanned Aerial Vehicle (UAV) Cooperative Fault Detection Employing Differential Global Positioning (DGPS), Inertial and Vision Sensors.

    Science.gov (United States)

    Heredia, Guillermo; Caballero, Fernando; Maza, Iván; Merino, Luis; Viguria, Antidio; Ollero, Aníbal

    2009-01-01

    This paper presents a method to increase the reliability of Unmanned Aerial Vehicle (UAV) sensor Fault Detection and Identification (FDI) in a multi-UAV context. Differential Global Positioning System (DGPS) and inertial sensors are used for sensor FDI in each UAV. The method uses additional position estimations that augment individual UAV FDI system. These additional estimations are obtained using images from the same planar scene taken from two different UAVs. Since accuracy and noise level of the estimation depends on several factors, dynamic replanning of the multi-UAV team can be used to obtain a better estimation in case of faults caused by slow growing errors of absolute position estimation that cannot be detected by using local FDI in the UAVs. Experimental results with data from two real UAVs are also presented.

  5. Robust fiber optic flexure sensor exploiting mode coupling in few-mode fiber

    Science.gov (United States)

    Nelsen, Bryan; Rudek, Florian; Taudt, Christopher; Baselt, Tobias; Hartmann, Peter

    2015-05-01

    Few-mode fiber (FMF) has become very popular for use in multiplexing telecommunications data over fiber optics. The simplicity of producing FMF and the relative robustness of the optical modes, coupled with the simplicity of reading out the information make this fiber a natural choice for communications. However, little work has been done to take advantage of this type of fiber for sensors. Here, we demonstrate the feasibility of using FMF properties as a mechanism for detecting flexure by exploiting mode coupling between modes when the cylindrical symmetry of the fiber is perturbed. The theoretical calculations shown here are used to understand the coupling between the lowest order linearly polarized mode (LP01) and the next higher mode (LP11x or LP11y) under the action of bending. Twisting is also evaluated as a means to detect flexure and was determined to be the most reliable and effective method when observing the LP21 mode. Experimental results of twisted fiber and observations of the LP21 mode are presented here. These types of fiber flexure sensors are practical in high voltage, high magnetic field, or high temperature medical or industrial environments where typical electronic flexure sensors would normally fail. Other types of flexure measurement systems that utilize fiber, such as Rayleigh back-scattering [1], are complicated and expensive and often provide a higher-than necessary sensitivity for the task at hand.

  6. High Resolution Robust GPS-free Localization for Wireless Sensor Networks and its Applications

    KAUST Repository

    Mirza, Mohammed

    2011-12-12

    In this thesis we investigate the problem of robustness and scalability w.r.t. estimating the position of randomly deployed motes/nodes of a Wireless Sensor Network (WSN) without the help of Global Positioning System (GPS) devices. We propose a few applications of range independent localization algorithms that allow the sensors to actively determine their location with high resolution without increasing the complexity of the hardware or any additional device setup. In our first application we try to present a localized and centralized cooperative spectrum sensing using RF sensor networks. This scheme collaboratively sense the spectrum and localize the whole network efficiently and with less difficulty. In second application we try to focus on how efficiently we can localize the nodes, to detect underwater threats, without the use of beacons. In third application we try to focus on 3-Dimensional localization for LTE systems. Our performance evaluation shows that these schemes lead to a significant improvement in localization accuracy compared to the state-of-art range independent localization schemes, without requiring GPS support.

  7. Robust Target Tracking with Multi-Static Sensors under Insufficient TDOA Information.

    Science.gov (United States)

    Shin, Hyunhak; Ku, Bonhwa; Nelson, Jill K; Ko, Hanseok

    2018-05-08

    This paper focuses on underwater target tracking based on a multi-static sonar network composed of passive sonobuoys and an active ping. In the multi-static sonar network, the location of the target can be estimated using TDOA (Time Difference of Arrival) measurements. However, since the sensor network may obtain insufficient and inaccurate TDOA measurements due to ambient noise and other harsh underwater conditions, target tracking performance can be significantly degraded. We propose a robust target tracking algorithm designed to operate in such a scenario. First, track management with track splitting is applied to reduce performance degradation caused by insufficient measurements. Second, a target location is estimated by a fusion of multiple TDOA measurements using a Gaussian Mixture Model (GMM). In addition, the target trajectory is refined by conducting a stack-based data association method based on multiple-frames measurements in order to more accurately estimate target trajectory. The effectiveness of the proposed method is verified through simulations.

  8. A Robust Method to Detect Zero Velocity for Improved 3D Personal Navigation Using Inertial Sensors

    Science.gov (United States)

    Xu, Zhengyi; Wei, Jianming; Zhang, Bo; Yang, Weijun

    2015-01-01

    This paper proposes a robust zero velocity (ZV) detector algorithm to accurately calculate stationary periods in a gait cycle. The proposed algorithm adopts an effective gait cycle segmentation method and introduces a Bayesian network (BN) model based on the measurements of inertial sensors and kinesiology knowledge to infer the ZV period. During the detected ZV period, an Extended Kalman Filter (EKF) is used to estimate the error states and calibrate the position error. The experiments reveal that the removal rate of ZV false detections by the proposed method increases 80% compared with traditional method at high walking speed. Furthermore, based on the detected ZV, the Personal Inertial Navigation System (PINS) algorithm aided by EKF performs better, especially in the altitude aspect. PMID:25831086

  9. A Robust Vehicle Localization Approach Based on GNSS/IMU/DMI/LiDAR Sensor Fusion for Autonomous Vehicles.

    Science.gov (United States)

    Meng, Xiaoli; Wang, Heng; Liu, Bingbing

    2017-09-18

    Precise and robust localization in a large-scale outdoor environment is essential for an autonomous vehicle. In order to improve the performance of the fusion of GNSS (Global Navigation Satellite System)/IMU (Inertial Measurement Unit)/DMI (Distance-Measuring Instruments), a multi-constraint fault detection approach is proposed to smooth the vehicle locations in spite of GNSS jumps. Furthermore, the lateral localization error is compensated by the point cloud-based lateral localization method proposed in this paper. Experiment results have verified the algorithms proposed in this paper, which shows that the algorithms proposed in this paper are capable of providing precise and robust vehicle localization.

  10. A Robust Vehicle Localization Approach Based on GNSS/IMU/DMI/LiDAR Sensor Fusion for Autonomous Vehicles

    Directory of Open Access Journals (Sweden)

    Xiaoli Meng

    2017-09-01

    Full Text Available Precise and robust localization in a large-scale outdoor environment is essential for an autonomous vehicle. In order to improve the performance of the fusion of GNSS (Global Navigation Satellite System/IMU (Inertial Measurement Unit/DMI (Distance-Measuring Instruments, a multi-constraint fault detection approach is proposed to smooth the vehicle locations in spite of GNSS jumps. Furthermore, the lateral localization error is compensated by the point cloud-based lateral localization method proposed in this paper. Experiment results have verified the algorithms proposed in this paper, which shows that the algorithms proposed in this paper are capable of providing precise and robust vehicle localization.

  11. Omnidirectional regeneration (ODR) of proximity sensor signals for robust diagnosis of journal bearing systems

    Science.gov (United States)

    Jung, Joon Ha; Jeon, Byung Chul; Youn, Byeng D.; Kim, Myungyon; Kim, Donghwan; Kim, Yeonwhan

    2017-06-01

    Some anomaly states of journal bearing rotor systems are direction-oriented (e.g., rubbing, misalignment). In these situations, vibration signals vary according to the direction of the sensors and the health state. This makes diagnosis difficult with traditional diagnosis methods. This paper proposes an omnidirectional regeneration method to develop a robust diagnosis algorithm for rotor systems. The proposed method can generate vibration signals in arbitrary directions without using extra sensors. In this method, signals are generated around the entire circumference of the rotor to consider all possible directions. Then, the directionality of each state is proved by mathematically and is evaluated using a proposed metric. When a directional state is determined, the classification is carried out on all of the generated signals. When a non-directional state is found, the classification is performed on only one of the generated signals to minimize computational load without sacrificing accuracy. The proposed ODR method was validated using experimental data. The classification results show that the proposed method generally outperforms the conventional classification method. The results support the proposed concept of using ODR signals in diagnosis procedures for journal bearing systems.

  12. An Adaptive Multi-Sensor Data Fusion Method Based on Deep Convolutional Neural Networks for Fault Diagnosis of Planetary Gearbox

    Science.gov (United States)

    Jing, Luyang; Wang, Taiyong; Zhao, Ming; Wang, Peng

    2017-01-01

    A fault diagnosis approach based on multi-sensor data fusion is a promising tool to deal with complicated damage detection problems of mechanical systems. Nevertheless, this approach suffers from two challenges, which are (1) the feature extraction from various types of sensory data and (2) the selection of a suitable fusion level. It is usually difficult to choose an optimal feature or fusion level for a specific fault diagnosis task, and extensive domain expertise and human labor are also highly required during these selections. To address these two challenges, we propose an adaptive multi-sensor data fusion method based on deep convolutional neural networks (DCNN) for fault diagnosis. The proposed method can learn features from raw data and optimize a combination of different fusion levels adaptively to satisfy the requirements of any fault diagnosis task. The proposed method is tested through a planetary gearbox test rig. Handcraft features, manual-selected fusion levels, single sensory data, and two traditional intelligent models, back-propagation neural networks (BPNN) and a support vector machine (SVM), are used as comparisons in the experiment. The results demonstrate that the proposed method is able to detect the conditions of the planetary gearbox effectively with the best diagnosis accuracy among all comparative methods in the experiment. PMID:28230767

  13. Condition monitoring and fault diagnosis of motor bearings using undersampled vibration signals from a wireless sensor network

    Science.gov (United States)

    Lu, Siliang; Zhou, Peng; Wang, Xiaoxian; Liu, Yongbin; Liu, Fang; Zhao, Jiwen

    2018-02-01

    Wireless sensor networks (WSNs) which consist of miscellaneous sensors are used frequently in monitoring vital equipment. Benefiting from the development of data mining technologies, the massive data generated by sensors facilitate condition monitoring and fault diagnosis. However, too much data increase storage space, energy consumption, and computing resource, which can be considered fatal weaknesses for a WSN with limited resources. This study investigates a new method for motor bearings condition monitoring and fault diagnosis using the undersampled vibration signals acquired from a WSN. The proposed method, which is a fusion of the kurtogram, analog domain bandpass filtering, bandpass sampling, and demodulated resonance technique, can reduce the sampled data length while retaining the monitoring and diagnosis performance. A WSN prototype was designed, and simulations and experiments were conducted to evaluate the effectiveness and efficiency of the proposed method. Experimental results indicated that the sampled data length and transmission time of the proposed method result in a decrease of over 80% in comparison with that of the traditional method. Therefore, the proposed method indicates potential applications on condition monitoring and fault diagnosis of motor bearings installed in remote areas, such as wind farms and offshore platforms.

  14. Robust fault detection for the dynamics of high-speed train with multi-source finite frequency interference.

    Science.gov (United States)

    Bai, Weiqi; Dong, Hairong; Yao, Xiuming; Ning, Bin

    2018-04-01

    This paper proposes a composite fault detection scheme for the dynamics of high-speed train (HST), using an unknown input observer-like (UIO-like) fault detection filter, in the presence of wind gust and operating noises which are modeled as disturbance generated by exogenous system and unknown multi-source disturbance within finite frequency domain. Using system input and system output measurements, the fault detection filter is designed to generate the needed residual signals. In order to decouple disturbance from residual signals without truncating the influence of faults, this paper proposes a method to partition the disturbance into two parts. One subset of the disturbance does not appear in residual dynamics, and the influence of the other subset is constrained by H ∞ performance index in a finite frequency domain. A set of detection subspaces are defined, and every different fault is assigned to its own detection subspace to guarantee the residual signals are diagonally affected promptly by the faults. Simulations are conducted to demonstrate the effectiveness and merits of the proposed method. Copyright © 2018 ISA. Published by Elsevier Ltd. All rights reserved.

  15. Surface analysis and electrochemistry of a robust carbon-nanofiber-based electrode platform H_2O_2 sensor

    International Nuclear Information System (INIS)

    Suazo-Dávila, D.; Rivera-Meléndez, J.; Koehne, J.; Meyyappan, M.; Cabrera, C.R.

    2016-01-01

    Highlights: • Vertically aligned carbon nanofibers were intercalated with SiO_2 for mechanical strength and isolation of individual electrodes. • Stable and robust electrochemical hydrogen peroxide sensor is stable and robust. • Five consecutive calibration curves were done with different hydrogen peroxide concentrations over a period of 3 days without any deterioration in the electrochemical response. • The sensor was also used for the measurement of hydrogen peroxide as one of the by-products of the reaction of cholesterol oxidase with cholesterol and the sensor response exhibited linear behavior from 50 μM to 1 mM in cholesterol concentration. • In general, the electrochemical sensor is robust, stable, and reproducible, and the detection limit and sensitivity responses were among the best when compared with the literature. - Abstract: A vertically aligned carbon nanofiber-based (VACNF) electrode platform was developed for an enzymeless hydrogen peroxide sensor. Vertical nanofibers have heights on the order of 2–3 μm, and diameters that vary from 50 to 100 nm as seen by atomic force microscopy. The VACNF was grown as individual, vertically, and freestanding structures using plasma-enhanced chemical vapor deposition. The electrochemical sensor, for the hydrogen peroxide measurement in solution, showed stability and reproducibility in five consecutive calibration curves with different hydrogen peroxide concentrations over a period of 3 days. The detection limit was 66 μM. The sensitivity for hydrogen peroxide electrochemical detection was 0.0906 mA cm"−"2 mM"−"1, respectively. The sensor was also used for the measurement of hydrogen peroxide as the by-product of the reaction of cholesterol with cholesterol oxidase as a biosensor application. The sensor exhibits linear behavior in the range of 50 μM–1 mM in cholesterol concentrations. The surface analysis and electrochemistry characterization is presented.

  16. Robust, Brillouin Active Embedded Fiber-Is-The-Sensor System in Smart Composite Structures

    Science.gov (United States)

    Yu, Chung

    1996-01-01

    Extensive review of our proposed sensing scheme, based mainly on the forward Guided Acoustic Wave Brillouin Scattering (GAWBS) with backward stimulated Brillouin scattering (sBs) as an auxiliary scheme for system fault tolerance has been completed during this project period. This preliminary study is conducted for a number of reasons. The most significant reasons lie in the essential capability of the system to measure temperature and pressure. These two measurands have been proposed to be sensed by sBs in our proposal. Temperature and pressure/strain are important measurands in structural monitoring, so that the effectiveness of sensing by sBs needs to be further examined. It has been pointed out initially that sBs shift will be dependent on temperature and pressure/strain simultaneously. The shift versus temperature or strain is linear. Now, the question is how can these two measurands be separated when sBs is used to sense an environment, in which both temperature and strain are changing simultaneously. Typical sBs shift plotted versus strain and varying temperature is shown in Fig. 1. As is clear, a fiber initially stressed will relax with rising temperature. This is verified by a displacement to the right with rising temperature of the sBs shift vs strain curves in the figure. A way to circumvent this ambiguity is by employing two fibers, one pre-stressed and the other is a free fiber. The latter will measure temperature and subtracting data in the latter fiber from those of the former will give us net strain readings. This is a laborious approach, since it involves the use of two identical fibers, and this is hard to accomplish, especially when many sensors are needed. Additional multiplexing of the data stream for data subtraction becomes a necessity.

  17. On-Line Fault Detection in Wind Turbine Transmission System using Adaptive Filter and Robust Statistical Features

    Directory of Open Access Journals (Sweden)

    Mark Frogley

    2013-01-01

    Full Text Available To reduce the maintenance cost, avoid catastrophic failure, and improve the wind transmission system reliability, online condition monitoring system is critical important. In the real applications, many rotating mechanical faults, such as bearing surface defect, gear tooth crack, chipped gear tooth and so on generate impulsive signals. When there are these types of faults developing inside rotating machinery, each time the rotating components pass over the damage point, an impact force could be generated. The impact force will cause a ringing of the support structure at the structural natural frequency. By effectively detecting those periodic impulse signals, one group of rotating machine faults could be detected and diagnosed. However, in real wind turbine operations, impulsive fault signals are usually relatively weak to the background noise and vibration signals generated from other healthy components, such as shaft, blades, gears and so on. Moreover, wind turbine transmission systems work under dynamic operating conditions. This will further increase the difficulties in fault detection and diagnostics. Therefore, developing advanced signal processing methods to enhance the impulsive signals is in great needs.In this paper, an adaptive filtering technique will be applied for enhancing the fault impulse signals-to-noise ratio in wind turbine gear transmission systems. Multiple statistical features designed to quantify the impulsive signals of the processed signal are extracted for bearing fault detection. The multiple dimensional features are then transformed into one dimensional feature. A minimum error rate classifier will be designed based on the compressed feature to identify the gear transmission system with defect. Real wind turbine vibration signals will be used to demonstrate the effectiveness of the presented methodology.

  18. Human Age Estimation Method Robust to Camera Sensor and/or Face Movement

    Directory of Open Access Journals (Sweden)

    Dat Tien Nguyen

    2015-08-01

    Full Text Available Human age can be employed in many useful real-life applications, such as customer service systems, automatic vending machines, entertainment, etc. In order to obtain age information, image-based age estimation systems have been developed using information from the human face. However, limitations exist for current age estimation systems because of the various factors of camera motion and optical blurring, facial expressions, gender, etc. Motion blurring can usually be presented on face images by the movement of the camera sensor and/or the movement of the face during image acquisition. Therefore, the facial feature in captured images can be transformed according to the amount of motion, which causes performance degradation of age estimation systems. In this paper, the problem caused by motion blurring is addressed and its solution is proposed in order to make age estimation systems robust to the effects of motion blurring. Experiment results show that our method is more efficient for enhancing age estimation performance compared with systems that do not employ our method.

  19. Wind Turbine Load Mitigation based on Multivariable Robust Control and Blade Root Sensors

    Science.gov (United States)

    Díaz de Corcuera, A.; Pujana-Arrese, A.; Ezquerra, J. M.; Segurola, E.; Landaluze, J.

    2014-12-01

    This paper presents two H∞ multivariable robust controllers based on blade root sensors' information for individual pitch angle control. The wind turbine of 5 MW defined in the Upwind European project is the reference non-linear model used in this research work, which has been modelled in the GH Bladed 4.0 software package. The main objective of these controllers is load mitigation in different components of wind turbines during power production in the above rated control zone. The first proposed multi-input multi-output (MIMO) individual pitch H" controller mitigates the wind effect on the tower side-to-side acceleration and reduces the asymmetrical loads which appear in the rotor due to its misalignment. The second individual pitch H" multivariable controller mitigates the loads on the three blades reducing the wind effect on the bending flapwise and edgewise momentums in the blades. The designed H" controllers have been validated in GH Bladed and an exhaustive analysis has been carried out to calculate fatigue load reduction on wind turbine components, as well as to analyze load mitigation in some extreme cases.

  20. Wind Turbine Load Mitigation based on Multivariable Robust Control and Blade Root Sensors

    International Nuclear Information System (INIS)

    Corcuera, A Díaz de; Pujana-Arrese, A; Ezquerra, J M; Segurola, E; Landaluze, J

    2014-01-01

    This paper presents two H ∞ multivariable robust controllers based on blade root sensors' information for individual pitch angle control. The wind turbine of 5 MW defined in the Upwind European project is the reference non-linear model used in this research work, which has been modelled in the GH Bladed 4.0 software package. The main objective of these controllers is load mitigation in different components of wind turbines during power production in the above rated control zone. The first proposed multi-input multi-output (MIMO) individual pitch H'' controller mitigates the wind effect on the tower side-to-side acceleration and reduces the asymmetrical loads which appear in the rotor due to its misalignment. The second individual pitch H'' multivariable controller mitigates the loads on the three blades reducing the wind effect on the bending flapwise and edgewise momentums in the blades. The designed H'' controllers have been validated in GH Bladed and an exhaustive analysis has been carried out to calculate fatigue load reduction on wind turbine components, as well as to analyze load mitigation in some extreme cases

  1. RIDES: Robust Intrusion Detection System for IP-Based Ubiquitous Sensor Networks.

    Science.gov (United States)

    Amin, Syed Obaid; Siddiqui, Muhammad Shoaib; Hong, Choong Seon; Lee, Sungwon

    2009-01-01

    The IP-based Ubiquitous Sensor Network (IP-USN) is an effort to build the "Internet of things". By utilizing IP for low power networks, we can benefit from existing well established tools and technologies of IP networks. Along with many other unresolved issues, securing IP-USN is of great concern for researchers so that future market satisfaction and demands can be met. Without proper security measures, both reactive and proactive, it is hard to envisage an IP-USN realm. In this paper we present a design of an IDS (Intrusion Detection System) called RIDES (Robust Intrusion DEtection System) for IP-USN. RIDES is a hybrid intrusion detection system, which incorporates both Signature and Anomaly based intrusion detection components. For signature based intrusion detection this paper only discusses the implementation of distributed pattern matching algorithm with the help of signature-code, a dynamically created attack-signature identifier. Other aspects, such as creation of rules are not discussed. On the other hand, for anomaly based detection we propose a scoring classifier based on the SPC (Statistical Process Control) technique called CUSUM charts. We also investigate the settings and their effects on the performance of related parameters for both of the components.

  2. RIDES: Robust Intrusion Detection System for IP-Based Ubiquitous Sensor Networks

    Directory of Open Access Journals (Sweden)

    Sungwon Lee

    2009-05-01

    Full Text Available TheIP-based Ubiquitous Sensor Network (IP-USN is an effort to build the “Internet of things”. By utilizing IP for low power networks, we can benefit from existing well established tools and technologies of IP networks. Along with many other unresolved issues, securing IP-USN is of great concern for researchers so that future market satisfaction and demands can be met. Without proper security measures, both reactive and proactive, it is hard to envisage an IP-USN realm. In this paper we present a design of an IDS (Intrusion Detection System called RIDES (Robust Intrusion DEtection System for IP-USN. RIDES is a hybrid intrusion detection system, which incorporates both Signature and Anomaly based intrusion detection components. For signature based intrusion detection this paper only discusses the implementation of distributed pattern matching algorithm with the help of signature-code, a dynamically created attack-signature identifier. Other aspects, such as creation of rules are not discussed. On the other hand, for anomaly based detection we propose a scoring classifier based on the SPC (Statistical Process Control technique called CUSUM charts. We also investigate the settings and their effects on the performance of related parameters for both of the components.

  3. A Robust Approach for Clock Offset Estimation in Wireless Sensor Networks

    Directory of Open Access Journals (Sweden)

    Kim Jang-Sub

    2010-01-01

    Full Text Available The maximum likelihood estimators (MLEs for the clock phase offset assuming a two-way message exchange mechanism between the nodes of a wireless sensor network were recently derived assuming Gaussian and exponential network delays. However, the MLE performs poorly in the presence of non-Gaussian or nonexponential network delay distributions. Currently, there is a need to develop clock synchronization algorithms that are robust to the distribution of network delays. This paper proposes a clock offset estimator based on the composite particle filter (CPF to cope with the possible asymmetries and non-Gaussianity of the network delay distributions. Also, a variant of the CPF approach based on the bootstrap sampling (BS is shown to exhibit good performance in the presence of reduced number of observations. Computer simulations illustrate that the basic CPF and its BS-based variant present superior performance than MLE under general random network delay distributions such as asymmetric Gaussian, exponential, Gamma, Weibull as well as various mixtures.

  4. A Novel Evidence Theory and Fuzzy Preference Approach-Based Multi-Sensor Data Fusion Technique for Fault Diagnosis

    Directory of Open Access Journals (Sweden)

    Fuyuan Xiao

    2017-10-01

    Full Text Available The multi-sensor data fusion technique plays a significant role in fault diagnosis and in a variety of such applications, and the Dempster–Shafer evidence theory is employed to improve the system performance; whereas, it may generate a counter-intuitive result when the pieces of evidence highly conflict with each other. To handle this problem, a novel multi-sensor data fusion approach on the basis of the distance of evidence, belief entropy and fuzzy preference relation analysis is proposed. A function of evidence distance is first leveraged to measure the conflict degree among the pieces of evidence; thus, the support degree can be obtained to represent the reliability of the evidence. Next, the uncertainty of each piece of evidence is measured by means of the belief entropy. Based on the quantitative uncertainty measured above, the fuzzy preference relations are applied to represent the relative credibility preference of the evidence. Afterwards, the support degree of each piece of evidence is adjusted by taking advantage of the relative credibility preference of the evidence that can be utilized to generate an appropriate weight with respect to each piece of evidence. Finally, the modified weights of the evidence are adopted to adjust the bodies of the evidence in the advance of utilizing Dempster’s combination rule. A numerical example and a practical application in fault diagnosis are used as illustrations to demonstrate that the proposal is reasonable and efficient in the management of conflict and fault diagnosis.

  5. Reliability of measured data for pH sensor arrays with fault diagnosis and data fusion based on LabVIEW.

    Science.gov (United States)

    Liao, Yi-Hung; Chou, Jung-Chuan; Lin, Chin-Yi

    2013-12-13

    Fault diagnosis (FD) and data fusion (DF) technologies implemented in the LabVIEW program were used for a ruthenium dioxide pH sensor array. The purpose of the fault diagnosis and data fusion technologies is to increase the reliability of measured data. Data fusion is a very useful statistical method used for sensor arrays in many fields. Fault diagnosis is used to avoid sensor faults and to measure errors in the electrochemical measurement system, therefore, in this study, we use fault diagnosis to remove any faulty sensors in advance, and then proceed with data fusion in the sensor array. The average, self-adaptive and coefficient of variance data fusion methods are used in this study. The pH electrode is fabricated with ruthenium dioxide (RuO2) sensing membrane using a sputtering system to deposit it onto a silicon substrate, and eight RuO2 pH electrodes are fabricated to form a sensor array for this study.

  6. Reliability of Measured Data for pH Sensor Arrays with Fault Diagnosis and Data Fusion Based on LabVIEW

    Directory of Open Access Journals (Sweden)

    Yi-Hung Liao

    2013-12-01

    Full Text Available Fault diagnosis (FD and data fusion (DF technologies implemented in the LabVIEW program were used for a ruthenium dioxide pH sensor array. The purpose of the fault diagnosis and data fusion technologies is to increase the reliability of measured data. Data fusion is a very useful statistical method used for sensor arrays in many fields. Fault diagnosis is used to avoid sensor faults and to measure errors in the electrochemical measurement system, therefore, in this study, we use fault diagnosis to remove any faulty sensors in advance, and then proceed with data fusion in the sensor array. The average, self-adaptive and coefficient of variance data fusion methods are used in this study. The pH electrode is fabricated with ruthenium dioxide (RuO2 sensing membrane using a sputtering system to deposit it onto a silicon substrate, and eight RuO2 pH electrodes are fabricated to form a sensor array for this study.

  7. A simple approach to detect and correct signal faults of Hall position sensors for brushless DC motors at steady speed

    Science.gov (United States)

    Shi, Yongli; Wu, Zhong; Zhi, Kangyi; Xiong, Jun

    2018-03-01

    In order to realize reliable commutation of brushless DC motors (BLDCMs), a simple approach is proposed to detect and correct signal faults of Hall position sensors in this paper. First, the time instant of the next jumping edge for Hall signals is predicted by using prior information of pulse intervals in the last electrical period. Considering the possible errors between the predicted instant and the real one, a confidence interval is set by using the predicted value and a suitable tolerance for the next pulse edge. According to the relationship between the real pulse edge and the confidence interval, Hall signals can be judged and the signal faults can be corrected. Experimental results of a BLDCM at steady speed demonstrate the effectiveness of the approach.

  8. Robust model reference adaptive output feedback tracking for uncertain linear systems with actuator fault based on reinforced dead-zone modification.

    Science.gov (United States)

    Bagherpoor, H M; Salmasi, Farzad R

    2015-07-01

    In this paper, robust model reference adaptive tracking controllers are considered for Single-Input Single-Output (SISO) and Multi-Input Multi-Output (MIMO) linear systems containing modeling uncertainties, unknown additive disturbances and actuator fault. Two new lemmas are proposed for both SISO and MIMO, under which dead-zone modification rule is improved such that the tracking error for any reference signal tends to zero in such systems. In the conventional approach, adaption of the controller parameters is ceased inside the dead-zone region which results tracking error, while preserving the system stability. In the proposed scheme, control signal is reinforced with an additive term based on tracking error inside the dead-zone which results in full reference tracking. In addition, no Fault Detection and Diagnosis (FDD) unit is needed in the proposed approach. Closed loop system stability and zero tracking error are proved by considering a suitable Lyapunov functions candidate. It is shown that the proposed control approach can assure that all the signals of the close loop system are bounded in faulty conditions. Finally, validity and performance of the new schemes have been illustrated through numerical simulations of SISO and MIMO systems in the presence of actuator faults, modeling uncertainty and output disturbance. Copyright © 2015 ISA. Published by Elsevier Ltd. All rights reserved.

  9. A Robust High-Accuracy Ultrasound Indoor Positioning System Based on a Wireless Sensor Network.

    Science.gov (United States)

    Qi, Jun; Liu, Guo-Ping

    2017-11-06

    This paper describes the development and implementation of a robust high-accuracy ultrasonic indoor positioning system (UIPS). The UIPS consists of several wireless ultrasonic beacons in the indoor environment. Each of them has a fixed and known position coordinate and can collect all the transmissions from the target node or emit ultrasonic signals. Every wireless sensor network (WSN) node has two communication modules: one is WiFi, that transmits the data to the server, and the other is the radio frequency (RF) module, which is only used for time synchronization between different nodes, with accuracy up to 1 μ s. The distance between the beacon and the target node is calculated by measuring the time-of-flight (TOF) for the ultrasonic signal, and then the position of the target is computed by some distances and the coordinate of the beacons. TOF estimation is the most important technique in the UIPS. A new time domain method to extract the envelope of the ultrasonic signals is presented in order to estimate the TOF. This method, with the envelope detection filter, estimates the value with the sampled values on both sides based on the least squares method (LSM). The simulation results show that the method can achieve envelope detection with a good filtering effect by means of the LSM. The highest precision and variance can reach 0.61 mm and 0.23 mm, respectively, in pseudo-range measurements with UIPS. A maximum location error of 10.2 mm is achieved in the positioning experiments for a moving robot, when UIPS works on the line-of-sight (LOS) signal.

  10. A Robust High-Accuracy Ultrasound Indoor Positioning System Based on a Wireless Sensor Network

    Directory of Open Access Journals (Sweden)

    Jun Qi

    2017-11-01

    Full Text Available This paper describes the development and implementation of a robust high-accuracy ultrasonic indoor positioning system (UIPS. The UIPS consists of several wireless ultrasonic beacons in the indoor environment. Each of them has a fixed and known position coordinate and can collect all the transmissions from the target node or emit ultrasonic signals. Every wireless sensor network (WSN node has two communication modules: one is WiFi, that transmits the data to the server, and the other is the radio frequency (RF module, which is only used for time synchronization between different nodes, with accuracy up to 1 μs. The distance between the beacon and the target node is calculated by measuring the time-of-flight (TOF for the ultrasonic signal, and then the position of the target is computed by some distances and the coordinate of the beacons. TOF estimation is the most important technique in the UIPS. A new time domain method to extract the envelope of the ultrasonic signals is presented in order to estimate the TOF. This method, with the envelope detection filter, estimates the value with the sampled values on both sides based on the least squares method (LSM. The simulation results show that the method can achieve envelope detection with a good filtering effect by means of the LSM. The highest precision and variance can reach 0.61 mm and 0.23 mm, respectively, in pseudo-range measurements with UIPS. A maximum location error of 10.2 mm is achieved in the positioning experiments for a moving robot, when UIPS works on the line-of-sight (LOS signal.

  11. A Robust and Low-Complexity Gas Recognition Technique for On-Chip Tin-Oxide Gas Sensor Array

    Directory of Open Access Journals (Sweden)

    Farid Flitti

    2008-01-01

    Full Text Available Gas recognition is a new emerging research area with many civil, military, and industrial applications. The success of any gas recognition system depends on its computational complexity and its robustness. In this work, we propose a new low-complexity recognition method which is tested and successfully validated for tin-oxide gas sensor array chip. The recognition system is based on a vector angle similarity measure between the query gas and the representatives of the different gas classes. The latter are obtained using a clustering algorithm based on the same measure within the training data set. Experimented results on our in-house gas sensors array show more than 98% of correct recognition. The robustness of the proposed method is tested by recognizing gas measurements with simulated drift. Less than 1% of performance degradation is noted at the worst case scenario which represents a significant improvement when compared to the current state-of-the-art.

  12. Automatic Supervision And Fault Detection In PV System By Wireless Sensors With Interfacing By Labview Program

    Directory of Open Access Journals (Sweden)

    Yousra M Abbas

    2015-08-01

    Full Text Available In this work a wireless monitoring system are designed for automatic detection localization fault in photovoltaic system. In order to avoid the use of modeling and simulation of the PV system we detected the fault by monitoring the output of each individual photovoltaic panel connected in the system by Arduino and transmit this data wirelessly to laptop then interface it by LabVIEW program which made comparison between this data and the measured data taking from reference module at the same condition. The proposed method is very simple but effective detecting and diagnosing the main faults of a PV system and was experimentally validated and has demonstrated its effectiveness in the detection and diagnosing of main faults present in the DC side of PV system.

  13. Rigorously modeling self-stabilizing fault-tolerant circuits: An ultra-robust clocking scheme for systems-on-chip☆

    Science.gov (United States)

    Dolev, Danny; Függer, Matthias; Posch, Markus; Schmid, Ulrich; Steininger, Andreas; Lenzen, Christoph

    2014-01-01

    We present the first implementation of a distributed clock generation scheme for Systems-on-Chip that recovers from an unbounded number of arbitrary transient faults despite a large number of arbitrary permanent faults. We devise self-stabilizing hardware building blocks and a hybrid synchronous/asynchronous state machine enabling metastability-free transitions of the algorithm's states. We provide a comprehensive modeling approach that permits to prove, given correctness of the constructed low-level building blocks, the high-level properties of the synchronization algorithm (which have been established in a more abstract model). We believe this approach to be of interest in its own right, since this is the first technique permitting to mathematically verify, at manageable complexity, high-level properties of a fault-prone system in terms of its very basic components. We evaluate a prototype implementation, which has been designed in VHDL, using the Petrify tool in conjunction with some extensions, and synthesized for an Altera Cyclone FPGA. PMID:26516290

  14. Rigorously modeling self-stabilizing fault-tolerant circuits: An ultra-robust clocking scheme for systems-on-chip.

    Science.gov (United States)

    Dolev, Danny; Függer, Matthias; Posch, Markus; Schmid, Ulrich; Steininger, Andreas; Lenzen, Christoph

    2014-06-01

    We present the first implementation of a distributed clock generation scheme for Systems-on-Chip that recovers from an unbounded number of arbitrary transient faults despite a large number of arbitrary permanent faults. We devise self-stabilizing hardware building blocks and a hybrid synchronous/asynchronous state machine enabling metastability-free transitions of the algorithm's states. We provide a comprehensive modeling approach that permits to prove, given correctness of the constructed low-level building blocks, the high-level properties of the synchronization algorithm (which have been established in a more abstract model). We believe this approach to be of interest in its own right, since this is the first technique permitting to mathematically verify, at manageable complexity, high-level properties of a fault-prone system in terms of its very basic components. We evaluate a prototype implementation, which has been designed in VHDL, using the Petrify tool in conjunction with some extensions, and synthesized for an Altera Cyclone FPGA.

  15. Adaptive Robust Sliding Mode Vibration Control of a Flexible Beam Using Piezoceramic Sensor and Actuator: An Experimental Study

    Directory of Open Access Journals (Sweden)

    Ruo Lin Wang

    2014-01-01

    Full Text Available This paper presents an experimental study of an adaptive robust sliding mode control scheme based on the Lyapunov’s direct method for active vibration control of a flexible beam using PZT (lead zirconate titanate sensor and actuator. PZT, a type of piezoceramic material, has the advantages of high reliability, high bandwidth, and solid state actuation and is adopted here in forms of surface-bond patches for vibration control. Two adaptive robust sliding mode controllers for vibration suppression are designed: one uses a discontinuous bang-bang robust compensator and the other uses a smooth compensator with a hyperbolic tangent function. Both controllers guarantee asymptotic stability, as proved by the Lyapunov’s direct method. Experimental results verified the effectiveness and the robustness of both adaptive sliding mode controllers. However, from the experimental results, the bang-bang robust compensator causes small-magnitude chattering because of the discontinuous switching actions. With the smooth compensator, vibration is quickly suppressed and no chattering is induced. Furthermore, the robustness of the controllers is successfully demonstrated with ensured effectiveness in vibration control when masses are added to the flexible beam.

  16. A knowledge-based system approach for sensor fault modeling, detection and mitigation

    Data.gov (United States)

    National Aeronautics and Space Administration — Sensors are vital components for control and advanced health management techniques. However, sensors continue to be considered the weak link in many engineering...

  17. Robust Modal Filtering and Control of the X-56A Model with Simulated Fiber Optic Sensor Failures

    Science.gov (United States)

    Suh, Peter M.; Chin, Alexander W.; Mavris, Dimitri N.

    2016-01-01

    The X-56A aircraft is a remotely-piloted aircraft with flutter modes intentionally designed into the flight envelope. The X-56A program must demonstrate flight control while suppressing all unstable modes. A previous X-56A model study demonstrated a distributed-sensing-based active shape and active flutter suppression controller. The controller relies on an estimator which is sensitive to bias. This estimator is improved herein, and a real-time robust estimator is derived and demonstrated on 1530 fiber optic sensors. It is shown in simulation that the estimator can simultaneously reject 230 worst-case fiber optic sensor failures automatically. These sensor failures include locations with high leverage (or importance). To reduce the impact of leverage outliers, concentration based on a Mahalanobis trim criterion is introduced. A redescending M-estimator with Tukey bisquare weights is used to improve location and dispersion estimates within each concentration step in the presence of asymmetry (or leverage). A dynamic simulation is used to compare the concentrated robust estimator to a state-of-the-art real-time robust multivariate estimator. The estimators support a previously-derived mu-optimal shape controller. It is found that during the failure scenario, the concentrated modal estimator keeps the system stable.

  18. High-Intensity Radiated Field Fault-Injection Experiment for a Fault-Tolerant Distributed Communication System

    Science.gov (United States)

    Yates, Amy M.; Torres-Pomales, Wilfredo; Malekpour, Mahyar R.; Gonzalez, Oscar R.; Gray, W. Steven

    2010-01-01

    Safety-critical distributed flight control systems require robustness in the presence of faults. In general, these systems consist of a number of input/output (I/O) and computation nodes interacting through a fault-tolerant data communication system. The communication system transfers sensor data and control commands and can handle most faults under typical operating conditions. However, the performance of the closed-loop system can be adversely affected as a result of operating in harsh environments. In particular, High-Intensity Radiated Field (HIRF) environments have the potential to cause random fault manifestations in individual avionic components and to generate simultaneous system-wide communication faults that overwhelm existing fault management mechanisms. This paper presents the design of an experiment conducted at the NASA Langley Research Center's HIRF Laboratory to statistically characterize the faults that a HIRF environment can trigger on a single node of a distributed flight control system.

  19. Robust driver heartbeat estimation: A q-Hurst exponent based automatic sensor change with interactive multi-model EKF.

    Science.gov (United States)

    Vrazic, Sacha

    2015-08-01

    Preventing car accidents by monitoring the driver's physiological parameters is of high importance. However, existing measurement methods are not robust to driver's body movements. In this paper, a system that estimates the heartbeat from the seat embedded piezoelectric sensors, and that is robust to strong body movements is presented. Multifractal q-Hurst exponents are used within a classifier to predict the most probable best sensor signal to be used in an Interactive Multi-Model Extended Kalman Filter pulsation estimation procedure. The car vibration noise is reduced using an autoregressive exogenous model to predict the noise on sensors. The performance of the proposed system was evaluated on real driving data up to 100 km/h and with slaloms at high speed. It is shown that this method improves by 36.7% the pulsation estimation under strong body movement compared to static sensor pulsation estimation and appears to provide reliable pulsation variability information for top-level analysis of drowsiness or other conditions.

  20. An Immune Cooperative Particle Swarm Optimization Algorithm for Fault-Tolerant Routing Optimization in Heterogeneous Wireless Sensor Networks

    Directory of Open Access Journals (Sweden)

    Yifan Hu

    2012-01-01

    Full Text Available The fault-tolerant routing problem is important consideration in the design of heterogeneous wireless sensor networks (H-WSNs applications, and has recently been attracting growing research interests. In order to maintain k disjoint communication paths from source sensors to the macronodes, we present a hybrid routing scheme and model, in which multiple paths are calculated and maintained in advance, and alternate paths are created once the previous routing is broken. Then, we propose an immune cooperative particle swarm optimization algorithm (ICPSOA in the model to provide the fast routing recovery and reconstruct the network topology for path failure in H-WSNs. In the ICPSOA, mutation direction of the particle is determined by multi-swarm evolution equation, and its diversity is improved by immune mechanism, which can enhance the capacity of global search and improve the converging rate of the algorithm. Then we validate this theoretical model with simulation results. The results indicate that the ICPSOA-based fault-tolerant routing protocol outperforms several other protocols due to its capability of fast routing recovery mechanism, reliable communications, and prolonging the lifetime of WSNs.

  1. A Cubature-Principle-Assisted IMM-Adaptive UKF Algorithm for Maneuvering Target Tracking Caused by Sensor Faults

    Directory of Open Access Journals (Sweden)

    Huan Zhou

    2017-09-01

    Full Text Available Aimed at solving the problem of decreased filtering precision while maneuvering target tracking caused by non-Gaussian distribution and sensor faults, we developed an efficient interacting multiple model-unscented Kalman filter (IMM-UKF algorithm. By dividing the IMM-UKF into two links, the algorithm introduces the cubature principle to approximate the probability density of the random variable, after the interaction, by considering the external link of IMM-UKF, which constitutes the cubature-principle-assisted IMM method (CPIMM for solving the non-Gaussian problem, and leads to an adaptive matrix to balance the contribution of the state. The algorithm provides filtering solutions by considering the internal link of IMM-UKF, which is called a new adaptive UKF algorithm (NAUKF to address sensor faults. The proposed CPIMM-NAUKF is evaluated in a numerical simulation and two practical experiments including one navigation experiment and one maneuvering target tracking experiment. The simulation and experiment results show that the proposed CPIMM-NAUKF has greater filtering precision and faster convergence than the existing IMM-UKF. The proposed algorithm achieves a very good tracking performance, and will be effective and applicable in the field of maneuvering target tracking.

  2. Monitoring and diagnosis for sensor fault detection using GMDH methodology; Monitoracao e diagnostico para deteccao de falhas de sensores utilizando a metodologia GMDH

    Energy Technology Data Exchange (ETDEWEB)

    Goncalves, Iraci Martinez Pereira

    2006-07-01

    The fault detection and diagnosis system is an Operator Support System dedicated to specific functions that alerts operators to sensors and actuators fault problems, and guide them in the diagnosis before the normal alarm limits are reached. Operator Support Systems appears to reduce panels complexity caused by the increase of the available information in nuclear power plants control room. In this work a Monitoring and Diagnosis System was developed based on the GMDH (Group Method of Data Handling) methodology. The methodology was applied to the IPEN research reactor IEA-R1. The system performs the monitoring, comparing GMDH model calculated values with measured values. The methodology developed was firstly applied in theoretical models: a heat exchanger model and an IPEN reactor theoretical model. The results obtained with theoretical models gave a base to methodology application to the actual reactor operation data. Three GMDH models were developed for actual operation data monitoring: the first one using just the thermal process variables, the second one was developed considering also some nuclear variables, and the third GMDH model considered all the reactor variables. The three models presented excellent results, showing the methodology utilization viability in monitoring the operation data. The comparison between the three developed models results also shows the methodology capacity to choose by itself the best set of input variables for the model optimization. For the system diagnosis implementation, faults were simulated in the actual temperature variable values by adding a step change. The fault values correspond to a typical temperature descalibration and the result of monitoring faulty data was then used to build a simple diagnosis system based on fuzzy logic. (author)

  3. Robust and accurate detection algorithm for multimode polymer optical FBG sensor system

    DEFF Research Database (Denmark)

    Ganziy, Denis; Jespersen, O.; Rose, B.

    2015-01-01

    We propose a novel dynamic gate algorithm (DGA) for robust and fast peak detection. The algorithm uses a threshold determined detection window and center of gravity algorithm with bias compensation. Our experiment demonstrates that the DGA method is fast and robust with better stability and accur...

  4. CONTEXT-CAPTURE MULTI-VALUED DECISION FUSION WITH FAULT TOLERANT CAPABILITY FOR WIRELESS SENSOR NETWORKS

    OpenAIRE

    Jun Wu; Shigeru Shimamoto

    2011-01-01

    Wireless sensor networks (WSNs) are usually utilized to perform decision fusion of event detection. Current decision fusion schemes are based on binary valued decision and do not consider bursty contextcapture. However, bursty context and multi-valued data are important characteristics of WSNs. One on hand, the local decisions from sensors usually have bursty and contextual characteristics. Fusion center must capture the bursty context information from the sensors. On the other hand, in pract...

  5. A packaged, low-cost, robust optical fiber strain sensor based on small cladding fiber sandwiched within periodic polymer grating.

    Science.gov (United States)

    Chiang, Chia-Chin; Li, Chein-Hsing

    2014-06-02

    In the present study, a novel packaged long-period fiber grating (PLPFG) strain sensor is first presented. The MEMS process was utilized to fabricate the packaged optical fiber strain sensor. The sensor structure consisted of etched optical fiber sandwiched between two layers of thick photoresist SU-8 3050 and then packaged with poly (dimethylsiloxane) (PDMS) polymer material to construct the PLPFG strain sensor. The PDMS packaging material was used to prevent the glue effect, wherein glue flows into the LPFG structure and reduces coupling strength, in the surface bonding process. Because the fiber grating was packaged with PDMS material, it was effectively protected and made robust. The resonance attenuation dip of PLPFG grows when it is loading. This study explored the size effect of the grating period and fiber diameter of PLPFG via tensile testing. The experimental results found that the best strain sensitivity of the PLPFG strain sensor was -0.0342 dB/με, and that an R2 value of 0.963 was reached.

  6. Robust Networking Architecture and Secure Communication Scheme for Heterogeneous Wireless Sensor Networks

    Science.gov (United States)

    McNeal, McKenzie, III.

    2012-01-01

    Current networking architectures and communication protocols used for Wireless Sensor Networks (WSNs) have been designed to be energy efficient, low latency, and long network lifetime. One major issue that must be addressed is the security in data communication. Due to the limited capabilities of low cost and small sized sensor nodes, designing…

  7. Robust Chemiresistive Sensor for Continuous Monitoring of Free Chlorine Using Graphene-like Carbon.

    Science.gov (United States)

    Aryasomayajula, Aditya; Wojnas, Caroline; Divigalpitiya, Ranjith; Selvaganapathy, Ponnambalam Ravi; Kruse, Peter

    2018-02-23

    Free chlorine is widely used in industry as a bleaching and oxidizing agent. Its concentration is tightly monitored to avoid environmental contamination and deleterious human health effects. Here, we demonstrate a solid state chemiresistive sensor using graphene like carbon (GLC) to detect free chlorine in water. A 15-20 nm thick GLC layer on a PET substrate was modified with a redox-active aniline oligomer (phenyl-capped aniline tetramer, PCAT) to increase sensitivity, improve selectivity, and impart fouling resistance. Both the bare GLC sensor and the PCAT-modified GLC sensor can detect free chlorine continuously and, unlike previous chemiresistive sensors, do not require a reset. The PCAT-modified sensor showed a linear response with a slope of 13.89 (mg/L) -1 to free chlorine concentrations between 0.2 and 0.8 mg/L which is relevant for free chlorine monitoring for drinking water and wastewater applications. The PCAT-modified GLC sensors were found to be selective and showed less than 0.5% change in current in response to species such as nitrates, phosphates and sulfates in water. They also were resistant to fouling from organic material and showed only a 2% loss in signal. Tap water samples from residential area were tested using this sensor which showed good agreement with standard colorimetric measurement methods. The GLC and PCAT-GLC sensors show high sensitivity and excellent selectivity to free chlorine and can be used for continuous automated monitoring of free chlorine.

  8. Absolute Position Sensing Based on a Robust Differential Capacitive Sensor with a Grounded Shield Window

    Directory of Open Access Journals (Sweden)

    Yang Bai

    2016-05-01

    Full Text Available A simple differential capacitive sensor is provided in this paper to measure the absolute positions of length measuring systems. By utilizing a shield window inside the differential capacitor, the measurement range and linearity range of the sensor can reach several millimeters. What is more interesting is that this differential capacitive sensor is only sensitive to one translational degree of freedom (DOF movement, and immune to the vibration along the other two translational DOFs. In the experiment, we used a novel circuit based on an AC capacitance bridge to directly measure the differential capacitance value. The experimental result shows that this differential capacitive sensor has a sensitivity of 2 × 10−4 pF/μm with 0.08 μm resolution. The measurement range of this differential capacitive sensor is 6 mm, and the linearity error are less than 0.01% over the whole absolute position measurement range.

  9. AROSICS: An Automated and Robust Open-Source Image Co-Registration Software for Multi-Sensor Satellite Data

    Directory of Open Access Journals (Sweden)

    Daniel Scheffler

    2017-07-01

    Full Text Available Geospatial co-registration is a mandatory prerequisite when dealing with remote sensing data. Inter- or intra-sensoral misregistration will negatively affect any subsequent image analysis, specifically when processing multi-sensoral or multi-temporal data. In recent decades, many algorithms have been developed to enable manual, semi- or fully automatic displacement correction. Especially in the context of big data processing and the development of automated processing chains that aim to be applicable to different remote sensing systems, there is a strong need for efficient, accurate and generally usable co-registration. Here, we present AROSICS (Automated and Robust Open-Source Image Co-Registration Software, a Python-based open-source software including an easy-to-use user interface for automatic detection and correction of sub-pixel misalignments between various remote sensing datasets. It is independent of spatial or spectral characteristics and robust against high degrees of cloud coverage and spectral and temporal land cover dynamics. The co-registration is based on phase correlation for sub-pixel shift estimation in the frequency domain utilizing the Fourier shift theorem in a moving-window manner. A dense grid of spatial shift vectors can be created and automatically filtered by combining various validation and quality estimation metrics. Additionally, the software supports the masking of, e.g., clouds and cloud shadows to exclude such areas from spatial shift detection. The software has been tested on more than 9000 satellite images acquired by different sensors. The results are evaluated exemplarily for two inter-sensoral and two intra-sensoral use cases and show registration results in the sub-pixel range with root mean square error fits around 0.3 pixels and better.

  10. Robust Diagnosis Method Based on Parameter Estimation for an Interturn Short-Circuit Fault in Multipole PMSM under High-Speed Operation.

    Science.gov (United States)

    Lee, Jewon; Moon, Seokbae; Jeong, Hyeyun; Kim, Sang Woo

    2015-11-20

    This paper proposes a diagnosis method for a multipole permanent magnet synchronous motor (PMSM) under an interturn short circuit fault. Previous works in this area have suffered from the uncertainties of the PMSM parameters, which can lead to misdiagnosis. The proposed method estimates the q-axis inductance (Lq) of the faulty PMSM to solve this problem. The proposed method also estimates the faulty phase and the value of G, which serves as an index of the severity of the fault. The q-axis current is used to estimate the faulty phase, the values of G and Lq. For this reason, two open-loop observers and an optimization method based on a particle-swarm are implemented. The q-axis current of a healthy PMSM is estimated by the open-loop observer with the parameters of a healthy PMSM. The Lq estimation significantly compensates for the estimation errors in high-speed operation. The experimental results demonstrate that the proposed method can estimate the faulty phase, G, and Lq besides exhibiting robustness against parameter uncertainties.

  11. HiRLoc: High-resolution Robust Localization for Wireless Sensor Networks

    National Research Council Canada - National Science Library

    Lazos, Loukas; Poovendran, Radha

    2005-01-01

    ..., or the complexity of the hardware of each reference point. In HiRLoc, sensors determine their location based on the intersection of the areas covered by the beacons transmitted by multiple reference points...

  12. Robust sensor for turbidity measurement from light scattering and absorbing liquids.

    Science.gov (United States)

    Kontturi, Ville; Turunen, Petri; Uozumi, Jun; Peiponen, Kai-Erik

    2009-12-01

    Internationally standardized turbidity measurements for probing solid particles in liquid are problematic in the case of simultaneous light scattering and absorption. A method and a sensor to determine the turbidity in the presence of light absorption are presented. The developed sensor makes use of the total internal reflection of a laser beam at the liquid-prism interface, and the turbidity is assessed using the concept of laser speckle pattern. Using average filtering in speckle data analyzing the observed dynamic speckle pattern, which is due to light scattering from particles and the static speckle due to stray light of the sensor, can be separated from each other. Good correlation between the standard deviation of dynamic speckle and turbidity value for nonabsorbing and for absorbing liquids was observed. The sensor is suggested, for instance, for the measurement of ill-behaved as well as small-volume turbid liquids in both medicine and process industry.

  13. Radial basis function neural network in fault detection of automotive ...

    African Journals Online (AJOL)

    Radial basis function neural network in fault detection of automotive engines. ... Five faults have been simulated on the MVEM, including three sensor faults, one component fault and one actuator fault. The three sensor faults ... Keywords: Automotive engine, independent RBFNN model, RBF neural network, fault detection

  14. Schmitt-Trigger-based Recycling Sensor and Robust and High-Quality PUFs for Counterfeit IC Detection

    OpenAIRE

    Lin, Cheng-Wei; Jang, Jae-Won; Ghosh, Swaroop

    2015-01-01

    We propose Schmitt-Trigger (ST) based recycling sensor that are tailored to amplify the aging mechanisms and detect fine grained recycling (minutes to seconds). We exploit the susceptibility of ST to process variations to realize high-quality arbiter PUF. Conventional SRAM PUF suffer from environmental fluctuation-induced bit flipping. We propose 8T SRAM PUF with a back-to-back PMOS latch to improve robustness by 4X. We also propose a low-power 7T SRAM with embedded Magnetic Tunnel Junction (...

  15. Thermal-hydraulic modeling of deaerator and fault detection and diagnosis of measurement sensor

    International Nuclear Information System (INIS)

    Lee, Jung Woon; Park, Jae Chang; Kim, Jung Taek; Kim, Kyung Youn; Lee, In Soo; Kim, Bong Seok; Kang, Sook In

    2003-05-01

    It is important to note that an effective means to assure the reliability and security for the nuclear power plant is to detect and diagnose the faults (failures) as soon and as accurately as possible. The objective of the project is to develop model-based fault detection and diagnosis algorithm for the deaerator and evaluate the performance of the developed algorithm. The scope of the work can be classified into two categories. The one is state-space model-based FDD algorithm using Adaptive Estimator(AE) algorithm. The other is input-output model-based FDD algorithm using ART neural network. Extensive computer simulations for the real data obtained from Younggwang 3 and 4 FSAR are carried out to evaluate the performance in terms of speed and accuracy

  16. Customized Multiwavelets for Planetary Gearbox Fault Detection Based on Vibration Sensor Signals

    Directory of Open Access Journals (Sweden)

    Lue Chen

    2013-01-01

    Full Text Available Planetary gearboxes exhibit complicated dynamic responses which are more difficult to detect in vibration signals than fixed-axis gear trains because of the special gear transmission structures. Diverse advanced methods have been developed for this challenging task to reduce or avoid unscheduled breakdown and catastrophic accidents. It is feasible to make fault features distinct by using multiwavelet denoising which depends on the feature separation and the threshold denoising. However, standard and fixed multiwavelets are not suitable for accurate fault feature detections because they are usually independent of the measured signals. To overcome this drawback, a method to construct customized multiwavelets based on the redundant symmetric lifting scheme is proposed in this paper. A novel indicator which combines kurtosis and entropy is applied to select the optimal multiwavelets, because kurtosis is sensitive to sharp impulses and entropy is effective for periodic impulses. The improved neighboring coefficients method is introduced into multiwavelet denoising. The vibration signals of a planetary gearbox from a satellite communication antenna on a measurement ship are captured under various motor speeds. The results show the proposed method could accurately detect the incipient pitting faults on two neighboring teeth in the planetary gearbox.

  17. Robust site security using smart seismic array technology and multi-sensor data fusion

    Science.gov (United States)

    Hellickson, Dean; Richards, Paul; Reynolds, Zane; Keener, Joshua

    2010-04-01

    Traditional site security systems are susceptible to high individual sensor nuisance alarm rates that reduce the overall system effectiveness. Visual assessment of intrusions can be intensive and manually difficult as cameras are slewed by the system to non intrusion areas or as operators respond to nuisance alarms. Very little system intrusion performance data are available other than discrete sensor alarm indications that provide no real value. This paper discusses the system architecture, integration and display of a multi-sensor data fused system for wide area surveillance, local site intrusion detection and intrusion classification. The incorporation of a novel seismic array of smart sensors using FK Beamforming processing that greatly enhances the overall system detection and classification performance of the system is discussed. Recent test data demonstrates the performance of the seismic array within several different installations and its ability to classify and track moving targets at significant standoff distances with exceptional immunity to background clutter and noise. Multi-sensor data fusion is applied across a suite of complimentary sensors eliminating almost all nuisance alarms while integrating within a geographical information system to feed a visual-fusion display of the area being secured. Real-time sensor detection and intrusion classification data is presented within a visual-fusion display providing greatly enhanced situational awareness, system performance information and real-time assessment of intrusions and situations of interest with limited security operator involvement. This approach scales from a small local perimeter to very large geographical area and can be used across multiple sites controlled at a single command and control station.

  18. Group method of data handling and neral networks applied in monitoring and fault detection in sensors in nuclear power plants; Group Method of Data Handling (GMDH) e Redes Neurais na Monitoracao e Deteccao de Falhas em sensores de centrais nucleares

    Energy Technology Data Exchange (ETDEWEB)

    Bueno, Elaine Inacio

    2011-07-01

    The increasing demand in the complexity, efficiency and reliability in modern industrial systems stimulated studies on control theory applied to the development of Monitoring and Fault Detection system. In this work a new Monitoring and Fault Detection methodology was developed using GMDH (Group Method of Data Handling) algorithm and Artificial Neural Networks (ANNs) which was applied to the IEA-R1 research reactor at IPEN. The Monitoring and Fault Detection system was developed in two parts: the first was dedicated to preprocess information, using GMDH algorithm; and the second part to the process information using ANNs. The GMDH algorithm was used in two different ways: firstly, the GMDH algorithm was used to generate a better database estimated, called matrix{sub z}, which was used to train the ANNs. After that, the GMDH was used to study the best set of variables to be used to train the ANNs, resulting in a best monitoring variable estimative. The methodology was developed and tested using five different models: one Theoretical Model and four Models using different sets of reactor variables. After an exhausting study dedicated to the sensors Monitoring, the Fault Detection in sensors was developed by simulating faults in the sensors database using values of 5%, 10%, 15% and 20% in these sensors database. The results obtained using GMDH algorithm in the choice of the best input variables to the ANNs were better than that using only ANNs, thus making possible the use of these methods in the implementation of a new Monitoring and Fault Detection methodology applied in sensors. (author)

  19. Development of a mobile sensor for robust assessment of river bed grain forces

    Science.gov (United States)

    Maniatis, G.; Hoey, T.; Sventek, J.; Hodge, R. A.

    2013-12-01

    The forces experienced by sediment grains at entrainment and during transport, and those exerted on river beds, are significant for the development of river systems and landscape evolution. The assessment of local grain forces has been approached using two different methodologies. The first approach uses static impact sensors at points or cross-sections to measure velocity and/or acceleration. A second approach uses mobile natural or artificial 'smart' pebbles instrumented with inertia micro-sensors for directly measuring the local forces experienced by individual grains. The two approaches have yielded significantly different magnitudes of impact forces. Static sensors (piezoelectric plates connected to accelerometers) temporally smooth the impacts from several grains and infrequently detect the higher forces (up to ×100g) generated by direct single-grain impacts. The second method is currently unable to record the full range of impacts in real rivers due to the low measurement range of the deployed inertia sensors (×3g). Laboratory applications have required only low-range accelerometers, so excluding the magnitude of natural impacts from the design criteria. Here we present the first results from the development of a mobile sensor, designed for the purpose of measuring local grain-forces in a natural riverbed. We present two sets of measurements. The first group presents the calibration of a wide range micro-accelerometer from a set of vertical drop experiments (gravitational acceleration) and further experiments on a shaking table moving with pre-defined acceleration. The second group of measurements are from incipient motion experiments performed in a 9m x0.9m flume (slope 0.001 to 0.018) under steadily increasing discharge. Initially the spherical sensor grain was placed on an artificial surface of hemispheres of identical diameter to the sensor (111mm). Incipient motion was assessed under both whole and half-diameter exposure for each slope. Subsequently

  20. Simultaneous Event-Triggered Fault Detection and Estimation for Stochastic Systems Subject to Deception Attacks.

    Science.gov (United States)

    Li, Yunji; Wu, QingE; Peng, Li

    2018-01-23

    In this paper, a synthesized design of fault-detection filter and fault estimator is considered for a class of discrete-time stochastic systems in the framework of event-triggered transmission scheme subject to unknown disturbances and deception attacks. A random variable obeying the Bernoulli distribution is employed to characterize the phenomena of the randomly occurring deception attacks. To achieve a fault-detection residual is only sensitive to faults while robust to disturbances, a coordinate transformation approach is exploited. This approach can transform the considered system into two subsystems and the unknown disturbances are removed from one of the subsystems. The gain of fault-detection filter is derived by minimizing an upper bound of filter error covariance. Meanwhile, system faults can be reconstructed by the remote fault estimator. An recursive approach is developed to obtain fault estimator gains as well as guarantee the fault estimator performance. Furthermore, the corresponding event-triggered sensor data transmission scheme is also presented for improving working-life of the wireless sensor node when measurement information are aperiodically transmitted. Finally, a scaled version of an industrial system consisting of local PC, remote estimator and wireless sensor node is used to experimentally evaluate the proposed theoretical results. In particular, a novel fault-alarming strategy is proposed so that the real-time capacity of fault-detection is guaranteed when the event condition is triggered.

  1. A Framework and Classification for Fault Detection Approaches in Wireless Sensor Networks with an Energy Efficiency Perspective

    DEFF Research Database (Denmark)

    Zhang, Yue; Dragoni, Nicola; Wang, Jiangtao

    2015-01-01

    efficiency to facilitate the design of fault detection methods and the evaluation of their energy efficiency. Following the same design principle of the fault detection framework, the paper proposes a classification for fault detection approaches. The classification is applied to a number of fault detection...

  2. Robust segment-type energy harvester and its application to a wireless sensor

    International Nuclear Information System (INIS)

    Lee, Soobum; Youn, Byeng D; Jung, Byung C

    2009-01-01

    This paper presents an innovative design platform of a piezoelectric energy harvester (EH), called a segment-type EH, and its application to a wireless sensor. Energy harvesting technology is motivated to minimize battery replacement cost for wireless sensors, which aims at developing self-powered sensors by utilizing ambient energy sources. Vibration energy is one of the widely available ambient energy sources which can be converted into electrical energy using piezoelectric material. The current state-of-the-art in piezoelectric EH technology mainly utilizes a single natural frequency, which is less effective when utilizing a random ambient vibration with multi-modal frequencies. This research thus proposes a segment-type harvester to generate electric power efficiently which utilizes multiple modes by separating the piezoelectric material. In order to reflect the random nature of ambient vibration energy, a stochastic design optimization is solved to determine the optimal configuration in terms of energy efficiency and durability. A prototype is manufactured and mounted on a heating, ventilation, air conditioning (HVAC) system to operate a temperature wireless sensor. It shows its excellent performance to generate sufficient power for real-time temperature monitoring for building automation

  3. RAHIM: Robust Adaptive Approach Based on Hierarchical Monitoring Providing Trust Aggregation for Wireless Sensor Networks

    NARCIS (Netherlands)

    Labraoui, Nabila; Gueroui, Mourad; Aliouat, Makhlouf; Petit, Jonathan

    2011-01-01

    In-network data aggregation has a great impact on the energy consumption in large-scale wireless sensor networks. However, the resource constraints and vulnerable deployment environments challenge the application of this technique in terms of security and efficiency. A compromised node may forge

  4. Carbon materials-functionalized tin dioxide nanoparticles toward robust, high-performance nitrogen dioxide gas sensor.

    Science.gov (United States)

    Zhang, Rui; Liu, Xiupeng; Zhou, Tingting; Wang, Lili; Zhang, Tong

    2018-08-15

    Carbon (C) materials, which process excellent electrical conductivity and high carrier mobility, are promising sensing materials as active units for gas sensors. However, structural agglomeration caused by chemical processes results in a small resistance change and low sensing response. To address the above issues, structure-derived carbon-coated tin dioxide (SnO 2 ) nanoparticles having distinct core-shell morphology with a 3D net-like structure and highly uniform size are prepared by careful synthesis and fine structural design. The optimum carbon-coated SnO 2 nanoparticles (SnO 2 /C)-based gas sensor exhibits a low working temperature, excellent selectivity and fast response-recovery properties. In addition, the SnO 2 /C-based gas sensor can maintain a sensitivity to nitrogen dioxide (NO 2 ) of 3 after being cycled 4 times at 140 °C for, suggesting its good long-term stability. The structural integrity, good synergistic properties, and high gas-sensing performance of SnO 2 /C render it a promising sensing material for advanced gas sensors. Copyright © 2018 Elsevier Inc. All rights reserved.

  5. Pd-Doped SnO2-Based Sensor Detecting Characteristic Fault Hydrocarbon Gases in Transformer Oil

    Directory of Open Access Journals (Sweden)

    Weigen Chen

    2013-01-01

    Full Text Available Methane (CH4, ethane (C2H6, ethylene (C2H4, and acetylene (C2C2 are important fault characteristic hydrocarbon gases dissolved in power transformer oil. Online monitoring these gaseous components and their generation rates can present the operational state of power transformer timely and effectively. Gas sensing technology is the most sticky and tricky point in online monitoring system. In this paper, pure and Pd-doped SnO2 nanoparticles were synthesized by hydrothermal method and characterized by X-ray powder diffraction, field-emission scanning electron microscopy, and energy dispersive X-ray spectroscopy, respectively. The gas sensors were fabricated by side-heated preparation, and their gas sensing properties against CH4, C2H6, C2H4, and C2H2 were measured. Pd doping increases the electric conductance of the prepared SnO2 sensors and improves their gas sensing performances to hydrocarbon gases. In addition based on the frontier molecular orbital theory, the highest occupied molecular orbital energy and the lowest unoccupied molecular orbital energy were calculated. Calculation results demonstrate that C2H4 has the highest occupied molecular orbital energy among CH4, C2H6, C2H4, and C2H2, which promotes charge transfer in gas sensing process, and SnO2 surfaces capture a relatively larger amount of electric charge from adsorbed C2H4.

  6. Low-Cost, Robust, and Field Portable Smartphone Platform Photometric Sensor for Fluoride Level Detection in Drinking Water.

    Science.gov (United States)

    Hussain, Iftak; Ahamad, Kamal Uddin; Nath, Pabitra

    2017-01-03

    Groundwater is the major source of drinking water for people living in rural areas of India. Pollutants such as fluoride in groundwater may be present in much higher concentration than the permissible limit. Fluoride does not give any visible coloration to water, and hence, no effort is made to remove or reduce the concentration of this chemical present in drinking water. This may lead to a serious health hazard for those people taking groundwater as their primary source of drinking water. Sophisticated laboratory grade tools such as ion selective electrodes (ISE) and portable spectrophotometers are commercially available for in-field detection of fluoride level in drinking water. However, such tools are generally expensive and require expertise to handle. In this paper, we demonstrate the working of a low cost, robust, and field portable smartphone platform fluoride sensor that can detect and analyze fluoride concentration level in drinking water. For development of the proposed sensor, we utilize the ambient light sensor (ALS) of the smartphone as light intensity detector and its LED flash light as an optical source. An android application "FSense" has been developed which can detect and analyze the fluoride concentration level in water samples. The custom developed application can be used for sharing of in-field sensing data from any remote location to the central water quality monitoring station. We envision that the proposed sensing technique could be useful for initiating a fluoride removal program undertaken by governmental and nongovernmental organizations here in India.

  7. Fault diagnosis

    Science.gov (United States)

    Abbott, Kathy

    1990-01-01

    The objective of the research in this area of fault management is to develop and implement a decision aiding concept for diagnosing faults, especially faults which are difficult for pilots to identify, and to develop methods for presenting the diagnosis information to the flight crew in a timely and comprehensible manner. The requirements for the diagnosis concept were identified by interviewing pilots, analyzing actual incident and accident cases, and examining psychology literature on how humans perform diagnosis. The diagnosis decision aiding concept developed based on those requirements takes abnormal sensor readings as input, as identified by a fault monitor. Based on these abnormal sensor readings, the diagnosis concept identifies the cause or source of the fault and all components affected by the fault. This concept was implemented for diagnosis of aircraft propulsion and hydraulic subsystems in a computer program called Draphys (Diagnostic Reasoning About Physical Systems). Draphys is unique in two important ways. First, it uses models of both functional and physical relationships in the subsystems. Using both models enables the diagnostic reasoning to identify the fault propagation as the faulted system continues to operate, and to diagnose physical damage. Draphys also reasons about behavior of the faulted system over time, to eliminate possibilities as more information becomes available, and to update the system status as more components are affected by the fault. The crew interface research is examining display issues associated with presenting diagnosis information to the flight crew. One study examined issues for presenting system status information. One lesson learned from that study was that pilots found fault situations to be more complex if they involved multiple subsystems. Another was pilots could identify the faulted systems more quickly if the system status was presented in pictorial or text format. Another study is currently under way to

  8. Fault Diagnosis and Condition Monitoring of an All Geared Lathe Machine Using Piezoelectric Sensor

    Directory of Open Access Journals (Sweden)

    Amiya BHAUMIK

    2008-12-01

    Full Text Available Undesired vibrations are serious problems that affect and deteriorate the quality of the product. This paper investigates dynamic and vibrational characteristics of a newly installed All Geared Lathe Machine with piezoelectric sensor. A comparison is drawn with the data measured and acceptable data as per ISO 10816 and thus concluded that the machine is in working condition.

  9. A Hybrid Sensor Based Backstepping Control Approach with its Application to Fault-Tolerant Flight Control

    NARCIS (Netherlands)

    Sun, L.G.; De Visser, C.C.; Chu, Q.P.; Falkena, W.

    2013-01-01

    Recently, an incremental type sensor based backstepping (SBB) control approach, based on singular perturbation theory and Tikhonov’s theorem, has been proposed. This Lyapunov function based method uses measurements of control variables and less model knowledge, and it is not susceptible to the model

  10. A Robust Distributed Multipoint Fiber Optic Gas Sensor System Based on AGC Amplifier Structure.

    Science.gov (United States)

    Zhu, Cunguang; Wang, Rende; Tao, Xuechen; Wang, Guangwei; Wang, Pengpeng

    2016-07-28

    A harsh environment-oriented distributed multipoint fiber optic gas sensor system realized by automatic gain control (AGC) technology is proposed. To improve the photoelectric signal reliability, the electronic variable gain can be modified in real time by an AGC closed-loop feedback structure to compensate for optical transmission loss which is caused by the fiber bend loss or other reasons. The deviation of the system based on AGC structure is below 4.02% when photoelectric signal decays due to fiber bending loss for bending radius of 5 mm, which is 20 times lower than the ordinary differential system. In addition, the AGC circuit with the same electric parameters can keep the baseline intensity of signals in different channels of the distributed multipoint sensor system at the same level. This avoids repetitive calibrations and streamlines the installation process.

  11. Nyquist AD Converters, Sensor Interfaces, and Robustness Advances in Analog Circuit Design, 2012

    CERN Document Server

    Baschirotto, Andrea; Steyaert, Michiel

    2013-01-01

    This book is based on the presentations during the 21st workshop on Advances in Analog Circuit Design.  Expert designers provide readers with information about a variety of topics at the frontier of analog circuit design, including Nyquist analog-to-digital converters, capacitive sensor interfaces, reliability, variability, and connectivity.  This book serves as a valuable reference to the state-of-the-art, for anyone involved in analog circuit research and development.  Provides a state-of-the-art reference in analog circuit design, written by experts from industry and academia; Presents material in a tutorial-based format; Includes coverage of Nyquist A/D converters, capacitive sensor interfaces, reliability, variability, and connectivity.

  12. Modular Energy-Efficient and Robust Paradigms for a Disaster-Recovery Process over Wireless Sensor Networks.

    Science.gov (United States)

    Razaque, Abdul; Elleithy, Khaled

    2015-07-06

    Robust paradigms are a necessity, particularly for emerging wireless sensor network (WSN) applications. The lack of robust and efficient paradigms causes a reduction in the provision of quality of service (QoS) and additional energy consumption. In this paper, we introduce modular energy-efficient and robust paradigms that involve two archetypes: (1) the operational medium access control (O-MAC) hybrid protocol and (2) the pheromone termite (PT) model. The O-MAC protocol controls overhearing and congestion and increases the throughput, reduces the latency and extends the network lifetime. O-MAC uses an optimized data frame format that reduces the channel access time and provides faster data delivery over the medium. Furthermore, O-MAC uses a novel randomization function that avoids channel collisions. The PT model provides robust routing for single and multiple links and includes two new significant features: (1) determining the packet generation rate to avoid congestion and (2) pheromone sensitivity to determine the link capacity prior to sending the packets on each link. The state-of-the-art research in this work is based on improving both the QoS and energy efficiency. To determine the strength of O-MAC with the PT model; we have generated and simulated a disaster recovery scenario using a network simulator (ns-3.10) that monitors the activities of disaster recovery staff; hospital staff and disaster victims brought into the hospital. Moreover; the proposed paradigm can be used for general purpose applications. Finally; the QoS metrics of the O-MAC and PT paradigms are evaluated and compared with other known hybrid protocols involving the MAC and routing features. The simulation results indicate that O-MAC with PT produced better outcomes.

  13. Modular Energy-Efficient and Robust Paradigms for a Disaster-Recovery Process over Wireless Sensor Networks

    Directory of Open Access Journals (Sweden)

    Abdul Razaque

    2015-07-01

    Full Text Available Robust paradigms are a necessity, particularly for emerging wireless sensor network (WSN applications. The lack of robust and efficient paradigms causes a reduction in the provision of quality of service (QoS and additional energy consumption. In this paper, we introduce modular energy-efficient and robust paradigms that involve two archetypes: (1 the operational medium access control (O-MAC hybrid protocol and (2 the pheromone termite (PT model. The O-MAC protocol controls overhearing and congestion and increases the throughput, reduces the latency and extends the network lifetime. O-MAC uses an optimized data frame format that reduces the channel access time and provides faster data delivery over the medium. Furthermore, O-MAC uses a novel randomization function that avoids channel collisions. The PT model provides robust routing for single and multiple links and includes two new significant features: (1 determining the packet generation rate to avoid congestion and (2 pheromone sensitivity to determine the link capacity prior to sending the packets on each link. The state-of-the-art research in this work is based on improving both the QoS and energy efficiency. To determine the strength of O-MAC with the PT model; we have generated and simulated a disaster recovery scenario using a network simulator (ns-3.10 that monitors the activities of disaster recovery staff; hospital staff and disaster victims brought into the hospital. Moreover; the proposed paradigm can be used for general purpose applications. Finally; the QoS metrics of the O-MAC and PT paradigms are evaluated and compared with other known hybrid protocols involving the MAC and routing features. The simulation results indicate that O-MAC with PT produced better outcomes.

  14. Inertial Sensor-Based Robust Gait Analysis in Non-Hospital Settings for Neurological Disorders

    Directory of Open Access Journals (Sweden)

    Can Tunca

    2017-04-01

    Full Text Available The gold standards for gait analysis are instrumented walkways and marker-based motion capture systems, which require costly infrastructure and are only available in hospitals and specialized gait clinics. Even though the completeness and the accuracy of these systems are unquestionable, a mobile and pervasive gait analysis alternative suitable for non-hospital settings is a clinical necessity. Using inertial sensors for gait analysis has been well explored in the literature with promising results. However, the majority of the existing work does not consider realistic conditions where data collection and sensor placement imperfections are imminent. Moreover, some of the underlying assumptions of the existing work are not compatible with pathological gait, decreasing the accuracy. To overcome these challenges, we propose a foot-mounted inertial sensor-based gait analysis system that extends the well-established zero-velocity update and Kalman filtering methodology. Our system copes with various cases of data collection difficulties and relaxes some of the assumptions invalid for pathological gait (e.g., the assumption of observing a heel strike during a gait cycle. The system is able to extract a rich set of standard gait metrics, including stride length, cadence, cycle time, stance time, swing time, stance ratio, speed, maximum/minimum clearance and turning rate. We validated the spatio-temporal accuracy of the proposed system by comparing the stride length and swing time output with an IR depth-camera-based reference system on a dataset comprised of 22 subjects. Furthermore, to highlight the clinical applicability of the system, we present a clinical discussion of the extracted metrics on a disjoint dataset of 17 subjects with various neurological conditions.

  15. Hierarchical Self Organizing Map for Novelty Detection using Mobile Robot with Robust Sensor

    International Nuclear Information System (INIS)

    Sha'abani, M N A H; Miskon, M F; Sakidin, H

    2013-01-01

    This paper presents a novelty detection method based on Self Organizing Map neural network using a mobile robot. Based on hierarchical neural network, the network is divided into three networks; position, orientation and sensor measurement network. A simulation was done to demonstrate and validate the proposed method using MobileSim. Three cases of abnormal events; new, missing and shifted objects are employed for performance evaluation. The result of detection was then filtered for false positive detection. The result shows that the inspection produced less than 2% false positive detection at high sensitivity settings

  16. Proposed Robust Entanglement-Based Magnetic Field Sensor Beyond the Standard Quantum Limit.

    Science.gov (United States)

    Tanaka, Tohru; Knott, Paul; Matsuzaki, Yuichiro; Dooley, Shane; Yamaguchi, Hiroshi; Munro, William J; Saito, Shiro

    2015-10-23

    Recently, there have been significant developments in entanglement-based quantum metrology. However, entanglement is fragile against experimental imperfections, and quantum sensing to beat the standard quantum limit in scaling has not yet been achieved in realistic systems. Here, we show that it is possible to overcome such restrictions so that one can sense a magnetic field with an accuracy beyond the standard quantum limit even under the effect of decoherence, by using a realistic entangled state that can be easily created even with current technology. Our scheme could pave the way for the realizations of practical entanglement-based magnetic field sensors.

  17. Robust and reliable banknote authentification and print flaw detection with opto-acoustical sensor fusion methods

    Science.gov (United States)

    Lohweg, Volker; Schaede, Johannes; Türke, Thomas

    2006-02-01

    The authenticity checking and inspection of bank notes is a high labour intensive process where traditionally every note on every sheet is inspected manually. However with the advent of more and more sophisticated security features, both visible and invisible, and the requirement of cost reduction in the printing process, it is clear that automation is required. As more and more print techniques and new security features will be established, total quality security, authenticity and bank note printing must be assured. Therefore, this factor necessitates amplification of a sensorial concept in general. We propose a concept for both authenticity checking and inspection methods for pattern recognition and classification for securities and banknotes, which is based on the concept of sensor fusion and fuzzy interpretation of data measures. In the approach different methods of authenticity analysis and print flaw detection are combined, which can be used for vending or sorting machines, as well as for printing machines. Usually only the existence or appearance of colours and their textures are checked by cameras. Our method combines the visible camera images with IR-spectral sensitive sensors, acoustical and other measurements like temperature and pressure of printing machines.

  18. Robust Drones Formation Control in 5G Wireless Sensor Network Using mmWave

    Directory of Open Access Journals (Sweden)

    Shan Meng

    2018-01-01

    Full Text Available The drones formation control in 5G wireless sensor network is discussed. The base station (BS is used to receive backhaul position signals from the lead drone in formation and launches the beam to the lead one as the fronthaul flying signal enhancement. It is a promising approach to raise the formation strength of drones during flight control. The BS can transform the direction of the antennas and transmit energy to the lead drone that could widely enlarge the number of the receivers and increase the transmission speed of the data links. The millimeter-Wave (mmWave communication system offers new opportunities to meet this requirement owing to the tremendous amount of available spectrums. However, the massive non-line-of-sight (NLoS transmission and the site constraints in urban environment are severely challenging the conventional deploying terrestrial low power nodes (LPNs. Simulation experiments have been performed to verify the availability and effectiveness of mmWave in 5G wireless sensor network.

  19. Robust high temperature composite and CO sensor made from such composite

    Science.gov (United States)

    Dutta, Prabir K.; Ramasamy, Ramamoorthy; Li, Xiaogan; Akbar, Sheikh A.

    2010-04-13

    Described herein is a composite exhibiting a change in electrical resistance proportional to the concentration of a reducing gas present in a gas mixture, detector and sensor devices comprising the composite, a method for making the composite and for making devices comprising the composite, and a process for detecting and measuring a reducing gas in an atmosphere. In particular, the reducing gas may be carbon monoxide and the composite may comprise rutile-phase TiO2 particles and platinum nanoclusters. The composite, upon exposure to a gas mixture containing CO in concentrations of up to 10,000 ppm, exhibits an electrical resistance proportional to the concentration of the CO present. The composite is useful for making sensitive, low drift, fast recovering detectors and sensors, and for measuring CO concentrations in a gas mixture present at levels from sub-ppm up to 10,000 ppm. The composites, and devices made from the composites, are stable and operable in a temperature range of from about 450.degree. C. to about 700.degree. C., such as may be found in a combustion chamber.

  20. A Multi-Sensor RSS Spatial Sensing-Based Robust Stochastic Optimization Algorithm for Enhanced Wireless Tethering

    CERN Document Server

    Parasuraman, Ramviyas; Molinari, Luca; Kershaw, Keith; Di Castro, Mario; Masi, Alessandro; Ferre, Manuel

    2014-01-01

    The reliability of wireless communication in a network of mobile wireless robot nodes depends on the received radio signal strength (RSS). When the robot nodes are deployed in hostile environments with ionizing radiations (such as in some scientific facilities), there is a possibility that some electronic components may fail randomly (due to radiation effects), which causes problems in wireless connectivity. The objective of this paper is to maximize robot mission capabilities by maximizing the wireless network capacity and to reduce the risk of communication failure. Thus, in this paper, we consider a multi-node wireless tethering structure called the “server-relay-client” framework that uses (multiple) relay nodes in between a server and a client node. We propose a robust stochastic optimization (RSO) algorithm using a multi-sensor-based RSS sampling method at the relay nodes to efficiently improve and balance the RSS between the source and client nodes to improve the network capacity and to provide red...

  1. Development of an artificial neural network for monitoring and diagnosis of sensor fault and detection in the IEA-R1 research reactor at IPEN

    International Nuclear Information System (INIS)

    Bueno, Elaine Inacio

    2007-01-01

    The increasing demand on quality in production processes has encouraged the development of several studies on Monitoring and Diagnosis Systems in industrial plant, where the interruption of the production due to some unexpected change can bring risk to the operator's security besides provoking economic losses, increasing the costs to repair some damaged equipment. Because of these two points, the economic losses and the operator's security, it becomes necessary to implement Monitoring and Diagnosis Systems. In this work a Monitoring and Diagnosis Systems was developed based on the Artificial Neural Networks methodology. This methodology was applied to the IEA-R1 research reactor at IPEN. The development of this system was divided in three stages: the first was dedicated to monitoring, the second to the detection and the third to diagnosis of failures. In the first stage, several Artificial Neural Networks were trained to monitor the temperature variables, nuclear power and dose rate. Two databases were used: one with data generated by a theoretical model and another one with data to a typical week of operation of the IEA-R1 reactor. In the second stage, the neural networks used to monitor the variables were tested with a fault database. The faults were inserted artificially in the sensors signals. As the value of the maximum calibration error for special thermocouples couples is ± 0,5 deg C, it had been inserted faults of ±1 deg C in the sensor for the reading of the variables T3 and T4. In the third stage was developed a Fuzzy System to carry out the faults diagnosis, where were considered three conditions: a normal condition, a fault of -1 deg C , and a fault of +1 deg C . This system will indicate which thermocouple is faulty. (author)

  2. Development of an artificial neural network for monitoring and diagnosis of sensor fault and detection in the IEA-R1 research reactor at IPEN

    Energy Technology Data Exchange (ETDEWEB)

    Bueno, Elaine Inacio [Centro Federal de Educacao Tecnologica de Sao Paulo (CEFET/SP), Guarulhos, SP (Brazil). Unidade Guarulhos]. E-mail: ebueno@cefetsp.br; Ting, Daniel Kao Sun; Goncalves, Iraci M.P. [Instituto de Pesquisas Energeticas e Nucleares (IPEN/CNEN-SP), Sao Paulo, SP (Brazil)]. E-mails: dksting@ipen.br; martinez@ipen.br

    2007-07-01

    The increasing demand on quality in production processes has encouraged the development of several studies on Monitoring and Diagnosis Systems in industrial plant, where the interruption of the production due to some unexpected change can bring risk to the operator's security besides provoking economic losses, increasing the costs to repair some damaged equipment. Because of these two points, the economic losses and the operator's security, it becomes necessary to implement Monitoring and Diagnosis Systems. In this work a Monitoring and Diagnosis Systems was developed based on the Artificial Neural Networks methodology. This methodology was applied to the IEA-R1 research reactor at IPEN. The development of this system was divided in three stages: the first was dedicated to monitoring, the second to the detection and the third to diagnosis of failures. In the first stage, several Artificial Neural Networks were trained to monitor the temperature variables, nuclear power and dose rate. Two databases were used: one with data generated by a theoretical model and another one with data to a typical week of operation of the IEA-R1 reactor. In the second stage, the neural networks used to monitor the variables were tested with a fault database. The faults were inserted artificially in the sensors signals. As the value of the maximum calibration error for special thermocouples couples is {+-} 0,5 deg C, it had been inserted faults of {+-}1 deg C in the sensor for the reading of the variables T3 and T4. In the third stage was developed a Fuzzy System to carry out the faults diagnosis, where were considered three conditions: a normal condition, a fault of -1 deg C , and a fault of +1 deg C . This system will indicate which thermocouple is faulty. (author)

  3. Development of an artificial neural network for monitoring and diagnosis of sensor fault and detection in the IEA-R1 research reactor at IPEN

    International Nuclear Information System (INIS)

    Bueno, Elaine Inacio

    2006-01-01

    The increasing demand on quality in production processes has encouraged the development of several studies on Monitoring and Diagnosis Systems in industrial plant, where the interruption of the production due to some unexpected change can bring risk to the operator's security besides provoking economic losses, increasing the costs to repair some damaged equipment. Because of these two points, the economic losses and the operator's security, it becomes necessary to implement Monitoring and Diagnosis Systems. In this work, a Monitoring and Diagnosis Systems was developed based on the Artificial Neural Networks methodology. This methodology was applied to the IEA-R1 research reactor at IPEN. The development of this system was divided in three stages: the first was dedicated to monitoring, the second to the detection and the third to diagnosis of failures. In the first stage, several Artificial Neural Networks were trained to monitor the temperature variables, nuclear power and dose rate. Two databases were used: one with data generated by a theoretical model and another one with data to a typical week of operation of the IEA-R1 reactor. In the second stage, the neural networks used to monitor the variables was tested with a fault database. The faults were inserted artificially in the sensors signals. As the value of the maximum calibration error for special thermocouples is ±0,5 deg C, it had been inserted faults of ± 10 C in the sensors for the reading of the variables T3 and T4. In the third stage a Fuzzy System was developed to carry out the faults diagnosis, where were considered three conditions: a normal condition, a fault of 1 0 C , and a fault of + 10 C . This system will indicate which thermocouple is faulty. (author)

  4. Robust and efficient modulation transfer function measurement with CMOS color sensors

    Science.gov (United States)

    Farsani, Raziyeh A.; Sure, Thomas; Apel, Uwe

    2017-06-01

    Increasing challenges of the industry to improve camera performance with control and test of the alignment process will be discussed in this paper. The major difficulties, such as special CFAs that have white/clear pixels instead of a Bayer pattern and non-homogeneous back light illumination of the targets, used for such tests, will be outlined and strategies on how to handle them will be presented. The proposed algorithms are applied to synthetically generated edges, as well as to experimental images taken from ADAS cameras in standard illumination conditions, to validate the approach. In addition, to consider the influence of the chromatic aberration of the lens and the CFA's influence on the total system MTF, the on-axis focus behavior of the camera module will be presented for each pixel class separately. It will be shown that the repeatability of the measurement results of the system MTF is improved, as a result of a more accurate and robust edge angle detection, elimination of systematic errors, using an improved lateral shift of the pixels and analytical modeling of the edge transition. Results also show the necessity to have separated measurements of contrast in the different pixel classes to ensure a precise focus position.

  5. Robust Road Condition Detection System Using In-Vehicle Standard Sensors

    Directory of Open Access Journals (Sweden)

    Juan Jesús Castillo Aguilar

    2015-12-01

    Full Text Available The appearance of active safety systems, such as Anti-lock Braking System, Traction Control System, Stability Control System, etc., represents a major evolution in road safety. In the automotive sector, the term vehicle active safety systems refers to those whose goal is to help avoid a crash or to reduce the risk of having an accident. These systems safeguard us, being in continuous evolution and incorporating new capabilities continuously. In order for these systems and vehicles to work adequately, they need to know some fundamental information: the road condition on which the vehicle is circulating. This early road detection is intended to allow vehicle control systems to act faster and more suitably, thus obtaining a substantial advantage. In this work, we try to detect the road condition the vehicle is being driven on, using the standard sensors installed in commercial vehicles. Vehicle models were programmed in on-board systems to perform real-time estimations of the forces of contact between the wheel and road and the speed of the vehicle. Subsequently, a fuzzy logic block is used to obtain an index representing the road condition. Finally, an artificial neural network was used to provide the optimal slip for each surface. Simulations and experiments verified the proposed method.

  6. Robust Road Condition Detection System Using In-Vehicle Standard Sensors.

    Science.gov (United States)

    Castillo Aguilar, Juan Jesús; Cabrera Carrillo, Juan Antonio; Guerra Fernández, Antonio Jesús; Carabias Acosta, Enrique

    2015-12-19

    The appearance of active safety systems, such as Anti-lock Braking System, Traction Control System, Stability Control System, etc., represents a major evolution in road safety. In the automotive sector, the term vehicle active safety systems refers to those whose goal is to help avoid a crash or to reduce the risk of having an accident. These systems safeguard us, being in continuous evolution and incorporating new capabilities continuously. In order for these systems and vehicles to work adequately, they need to know some fundamental information: the road condition on which the vehicle is circulating. This early road detection is intended to allow vehicle control systems to act faster and more suitably, thus obtaining a substantial advantage. In this work, we try to detect the road condition the vehicle is being driven on, using the standard sensors installed in commercial vehicles. Vehicle models were programmed in on-board systems to perform real-time estimations of the forces of contact between the wheel and road and the speed of the vehicle. Subsequently, a fuzzy logic block is used to obtain an index representing the road condition. Finally, an artificial neural network was used to provide the optimal slip for each surface. Simulations and experiments verified the proposed method.

  7. Solar system fault detection

    Science.gov (United States)

    Farrington, R.B.; Pruett, J.C. Jr.

    1984-05-14

    A fault detecting apparatus and method are provided for use with an active solar system. The apparatus provides an indication as to whether one or more predetermined faults have occurred in the solar system. The apparatus includes a plurality of sensors, each sensor being used in determining whether a predetermined condition is present. The outputs of the sensors are combined in a pre-established manner in accordance with the kind of predetermined faults to be detected. Indicators communicate with the outputs generated by combining the sensor outputs to give the user of the solar system and the apparatus an indication as to whether a predetermined fault has occurred. Upon detection and indication of any predetermined fault, the user can take appropriate corrective action so that the overall reliability and efficiency of the active solar system are increased.

  8. Biologically-inspired robust and adaptive multi-sensor fusion and active control

    Science.gov (United States)

    Khosla, Deepak; Dow, Paul A.; Huber, David J.

    2009-04-01

    In this paper, we describe a method and system for robust and efficient goal-oriented active control of a machine (e.g., robot) based on processing, hierarchical spatial understanding, representation and memory of multimodal sensory inputs. This work assumes that a high-level plan or goal is known a priori or is provided by an operator interface, which translates into an overall perceptual processing strategy for the machine. Its analogy to the human brain is the download of plans and decisions from the pre-frontal cortex into various perceptual working memories as a perceptual plan that then guides the sensory data collection and processing. For example, a goal might be to look for specific colored objects in a scene while also looking for specific sound sources. This paper combines three key ideas and methods into a single closed-loop active control system. (1) Use high-level plan or goal to determine and prioritize spatial locations or waypoints (targets) in multimodal sensory space; (2) collect/store information about these spatial locations at the appropriate hierarchy and representation in a spatial working memory. This includes invariant learning of these spatial representations and how to convert between them; and (3) execute actions based on ordered retrieval of these spatial locations from hierarchical spatial working memory and using the "right" level of representation that can efficiently translate into motor actions. In its most specific form, the active control is described for a vision system (such as a pantilt- zoom camera system mounted on a robotic head and neck unit) which finds and then fixates on high saliency visual objects. We also describe the approach where the goal is to turn towards and sequentially foveate on salient multimodal cues that include both visual and auditory inputs.

  9. Robust Ground Target Detection by SAR and IR Sensor Fusion Using Adaboost-Based Feature Selection

    Directory of Open Access Journals (Sweden)

    Sungho Kim

    2016-07-01

    Full Text Available Long-range ground targets are difficult to detect in a noisy cluttered environment using either synthetic aperture radar (SAR images or infrared (IR images. SAR-based detectors can provide a high detection rate with a high false alarm rate to background scatter noise. IR-based approaches can detect hot targets but are affected strongly by the weather conditions. This paper proposes a novel target detection method by decision-level SAR and IR fusion using an Adaboost-based machine learning scheme to achieve a high detection rate and low false alarm rate. The proposed method consists of individual detection, registration, and fusion architecture. This paper presents a single framework of a SAR and IR target detection method using modified Boolean map visual theory (modBMVT and feature-selection based fusion. Previous methods applied different algorithms to detect SAR and IR targets because of the different physical image characteristics. One method that is optimized for IR target detection produces unsuccessful results in SAR target detection. This study examined the image characteristics and proposed a unified SAR and IR target detection method by inserting a median local average filter (MLAF, pre-filter and an asymmetric morphological closing filter (AMCF, post-filter into the BMVT. The original BMVT was optimized to detect small infrared targets. The proposed modBMVT can remove the thermal and scatter noise by the MLAF and detect extended targets by attaching the AMCF after the BMVT. Heterogeneous SAR and IR images were registered automatically using the proposed RANdom SAmple Region Consensus (RANSARC-based homography optimization after a brute-force correspondence search using the detected target centers and regions. The final targets were detected by feature-selection based sensor fusion using Adaboost. The proposed method showed good SAR and IR target detection performance through feature selection-based decision fusion on a synthetic

  10. Robust Ground Target Detection by SAR and IR Sensor Fusion Using Adaboost-Based Feature Selection

    Science.gov (United States)

    Kim, Sungho; Song, Woo-Jin; Kim, So-Hyun

    2016-01-01

    Long-range ground targets are difficult to detect in a noisy cluttered environment using either synthetic aperture radar (SAR) images or infrared (IR) images. SAR-based detectors can provide a high detection rate with a high false alarm rate to background scatter noise. IR-based approaches can detect hot targets but are affected strongly by the weather conditions. This paper proposes a novel target detection method by decision-level SAR and IR fusion using an Adaboost-based machine learning scheme to achieve a high detection rate and low false alarm rate. The proposed method consists of individual detection, registration, and fusion architecture. This paper presents a single framework of a SAR and IR target detection method using modified Boolean map visual theory (modBMVT) and feature-selection based fusion. Previous methods applied different algorithms to detect SAR and IR targets because of the different physical image characteristics. One method that is optimized for IR target detection produces unsuccessful results in SAR target detection. This study examined the image characteristics and proposed a unified SAR and IR target detection method by inserting a median local average filter (MLAF, pre-filter) and an asymmetric morphological closing filter (AMCF, post-filter) into the BMVT. The original BMVT was optimized to detect small infrared targets. The proposed modBMVT can remove the thermal and scatter noise by the MLAF and detect extended targets by attaching the AMCF after the BMVT. Heterogeneous SAR and IR images were registered automatically using the proposed RANdom SAmple Region Consensus (RANSARC)-based homography optimization after a brute-force correspondence search using the detected target centers and regions. The final targets were detected by feature-selection based sensor fusion using Adaboost. The proposed method showed good SAR and IR target detection performance through feature selection-based decision fusion on a synthetic database generated

  11. Development of Robust and Standardized Cantilever Sensors Based on Biotin/Neutravidin Coupling for Antibody Detection

    Directory of Open Access Journals (Sweden)

    Christoph Gerber

    2013-04-01

    Full Text Available A cantilever-based protein biosensor has been developed providing a customizable multilayer platform for the detection of antibodies. It consists of a biotin-terminated PEG layer pre-functionalized on the gold-coated cantilever surface, onto which NeutrAvidin is adsorbed through biotin/NeutrAvidin specific binding. NeutrAvidin is used as a bridge layer between the biotin-coated surface and the biotinylated biomolecules, such as biotinylated bovine serum albumin (biotinylated BSA, forming a multilayer sensor for direct antibody capture. The cantilever biosensor has been successfully applied to the detection of mouse anti-BSA (m-IgG and sheep anti-BSA(s-IgG antibodies. As expected, the average differential surface stress signals of about 5.7 ± 0.8 ´ 10−3 N/m are very similar for BSA/m-IgG and BSA/s-IgG binding, i.e., they are independent of the origin of the antibody. A statistic evaluation of 112 response curves confirms that the multilayer protein cantilever biosensor shows high reproducibility. As a control test, a biotinylated maltose binding protein was used for detecting specificity of IgG, the result shows a signal of bBSA layer in response to antibody is 5.8 ´ 10−3 N/m compared to bMBP. The pre-functionalized biotin/PEG cantilever surface is found to show a long shelf-life of at least 40 days and retains its responsivity of above 70% of the signal when stored in PBS buffer at 4 °C. The protein cantilever biosensor represents a rapid, label-free, sensitive and reliable detection technique for a real-time protein assay.

  12. Fault Detection for Diesel Engine Actuator

    DEFF Research Database (Denmark)

    Blanke, M.; Bøgh, S.A.; Jørgensen, R.B.

    1994-01-01

    Feedback control systems are vulnerable to faults in control loop sensors and actuators, because feedback actions may cause abrupt responses and process damage when faults occur.......Feedback control systems are vulnerable to faults in control loop sensors and actuators, because feedback actions may cause abrupt responses and process damage when faults occur....

  13. Surface analysis and electrochemistry of a robust carbon-nanofiber-based electrode platform H{sub 2}O{sub 2} sensor

    Energy Technology Data Exchange (ETDEWEB)

    Suazo-Dávila, D.; Rivera-Meléndez, J. [NASA-MIRO Center for Advanced Nanoscale Materials (CANM), Department of Chemistry, Molecular Sciences Research Center, University of Puerto Rico, Río Piedras Campus, San Juan, PR, 00936 (United States); Koehne, J.; Meyyappan, M. [Center for Nanotechnology, NASA Ames Research Center, Moffett Field, CA 94035 (United States); Cabrera, C.R., E-mail: carlos.cabrera2@upr.edu [NASA-MIRO Center for Advanced Nanoscale Materials (CANM), Department of Chemistry, Molecular Sciences Research Center, University of Puerto Rico, Río Piedras Campus, San Juan, PR, 00936 (United States)

    2016-10-30

    Highlights: • Vertically aligned carbon nanofibers were intercalated with SiO{sub 2} for mechanical strength and isolation of individual electrodes. • Stable and robust electrochemical hydrogen peroxide sensor is stable and robust. • Five consecutive calibration curves were done with different hydrogen peroxide concentrations over a period of 3 days without any deterioration in the electrochemical response. • The sensor was also used for the measurement of hydrogen peroxide as one of the by-products of the reaction of cholesterol oxidase with cholesterol and the sensor response exhibited linear behavior from 50 μM to 1 mM in cholesterol concentration. • In general, the electrochemical sensor is robust, stable, and reproducible, and the detection limit and sensitivity responses were among the best when compared with the literature. - Abstract: A vertically aligned carbon nanofiber-based (VACNF) electrode platform was developed for an enzymeless hydrogen peroxide sensor. Vertical nanofibers have heights on the order of 2–3 μm, and diameters that vary from 50 to 100 nm as seen by atomic force microscopy. The VACNF was grown as individual, vertically, and freestanding structures using plasma-enhanced chemical vapor deposition. The electrochemical sensor, for the hydrogen peroxide measurement in solution, showed stability and reproducibility in five consecutive calibration curves with different hydrogen peroxide concentrations over a period of 3 days. The detection limit was 66 μM. The sensitivity for hydrogen peroxide electrochemical detection was 0.0906 mA cm{sup −2} mM{sup −1}, respectively. The sensor was also used for the measurement of hydrogen peroxide as the by-product of the reaction of cholesterol with cholesterol oxidase as a biosensor application. The sensor exhibits linear behavior in the range of 50 μM–1 mM in cholesterol concentrations. The surface analysis and electrochemistry characterization is presented.

  14. A Multi-Sensor RSS Spatial Sensing-Based Robust Stochastic Optimization Algorithm for Enhanced Wireless Tethering

    Science.gov (United States)

    Parasuraman, Ramviyas; Fabry, Thomas; Molinari, Luca; Kershaw, Keith; Di Castro, Mario; Masi, Alessandro; Ferre, Manuel

    2014-01-01

    The reliability of wireless communication in a network of mobile wireless robot nodes depends on the received radio signal strength (RSS). When the robot nodes are deployed in hostile environments with ionizing radiations (such as in some scientific facilities), there is a possibility that some electronic components may fail randomly (due to radiation effects), which causes problems in wireless connectivity. The objective of this paper is to maximize robot mission capabilities by maximizing the wireless network capacity and to reduce the risk of communication failure. Thus, in this paper, we consider a multi-node wireless tethering structure called the “server-relay-client” framework that uses (multiple) relay nodes in between a server and a client node. We propose a robust stochastic optimization (RSO) algorithm using a multi-sensor-based RSS sampling method at the relay nodes to efficiently improve and balance the RSS between the source and client nodes to improve the network capacity and to provide redundant networking abilities. We use pre-processing techniques, such as exponential moving averaging and spatial averaging filters on the RSS data for smoothing. We apply a receiver spatial diversity concept and employ a position controller on the relay node using a stochastic gradient ascent method for self-positioning the relay node to achieve the RSS balancing task. The effectiveness of the proposed solution is validated by extensive simulations and field experiments in CERN facilities. For the field trials, we used a youBot mobile robot platform as the relay node, and two stand-alone Raspberry Pi computers as the client and server nodes. The algorithm has been proven to be robust to noise in the radio signals and to work effectively even under non-line-of-sight conditions. PMID:25615734

  15. A Multi-Sensor RSS Spatial Sensing-Based Robust Stochastic Optimization Algorithm for Enhanced Wireless Tethering

    Directory of Open Access Journals (Sweden)

    Ramviyas Parasuraman

    2014-12-01

    Full Text Available The reliability of wireless communication in a network of mobile wireless robot nodes depends on the received radio signal strength (RSS. When the robot nodes are deployed in hostile environments with ionizing radiations (such as in some scientific facilities, there is a possibility that some electronic components may fail randomly (due to radiation effects, which causes problems in wireless connectivity. The objective of this paper is to maximize robot mission capabilities by maximizing the wireless network capacity and to reduce the risk of communication failure. Thus, in this paper, we consider a multi-node wireless tethering structure called the “server-relay-client” framework that uses (multiple relay nodes in between a server and a client node. We propose a robust stochastic optimization (RSO algorithm using a multi-sensor-based RSS sampling method at the relay nodes to efficiently improve and balance the RSS between the source and client nodes to improve the network capacity and to provide redundant networking abilities. We use pre-processing techniques, such as exponential moving averaging and spatial averaging filters on the RSS data for smoothing. We apply a receiver spatial diversity concept and employ a position controller on the relay node using a stochastic gradient ascent method for self-positioning the relay node to achieve the RSS balancing task. The effectiveness of the proposed solution is validated by extensive simulations and field experiments in CERN facilities. For the field trials, we used a youBot mobile robot platform as the relay node, and two stand-alone Raspberry Pi computers as the client and server nodes. The algorithm has been proven to be robust to noise in the radio signals and to work effectively even under non-line-of-sight conditions.

  16. Need for Robust Sensors for Inherently Fail-Safe Gas Turbine Engine Controls, Monitoring, and Prognostics (Postprint)

    National Research Council Canada - National Science Library

    Behbahani, Alireza R

    2006-01-01

    Sensor reliability is critical to turbine engine control. Today's aircraft engines demand more sophisticated sensors in the control systems, requiring advanced engine testing for component performance demonstration...

  17. Numerical Control Machine Tool Fault Diagnosis Using Hybrid Stationary Subspace Analysis and Least Squares Support Vector Machine with a Single Sensor

    Directory of Open Access Journals (Sweden)

    Chen Gao

    2017-03-01

    Full Text Available Tool fault diagnosis in numerical control (NC machines plays a significant role in ensuring manufacturing quality. However, current methods of tool fault diagnosis lack accuracy. Therefore, in the present paper, a fault diagnosis method was proposed based on stationary subspace analysis (SSA and least squares support vector machine (LS-SVM using only a single sensor. First, SSA was used to extract stationary and non-stationary sources from multi-dimensional signals without the need for independency and without prior information of the source signals, after the dimensionality of the vibration signal observed by a single sensor was expanded by phase space reconstruction technique. Subsequently, 10 dimensionless parameters in the time-frequency domain for non-stationary sources were calculated to generate samples to train the LS-SVM. Finally, the measured vibration signals from tools of an unknown state and their non-stationary sources were separated by SSA to serve as test samples for the trained SVM. The experimental validation demonstrated that the proposed method has better diagnosis accuracy than three previous methods based on LS-SVM alone, Principal component analysis and LS-SVM or on SSA and Linear discriminant analysis.

  18. Incipient fault detection and identification in process systems using accelerating neural network learning

    International Nuclear Information System (INIS)

    Parlos, A.G.; Muthusami, J.; Atiya, A.F.

    1994-01-01

    The objective of this paper is to present the development and numerical testing of a robust fault detection and identification (FDI) system using artificial neural networks (ANNs), for incipient (slowly developing) faults occurring in process systems. The challenge in using ANNs in FDI systems arises because of one's desire to detect faults of varying severity, faults from noisy sensors, and multiple simultaneous faults. To address these issues, it becomes essential to have a learning algorithm that ensures quick convergence to a high level of accuracy. A recently developed accelerated learning algorithm, namely a form of an adaptive back propagation (ABP) algorithm, is used for this purpose. The ABP algorithm is used for the development of an FDI system for a process composed of a direct current motor, a centrifugal pump, and the associated piping system. Simulation studies indicate that the FDI system has significantly high sensitivity to incipient fault severity, while exhibiting insensitivity to sensor noise. For multiple simultaneous faults, the FDI system detects the fault with the predominant signature. The major limitation of the developed FDI system is encountered when it is subjected to simultaneous faults with similar signatures. During such faults, the inherent limitation of pattern-recognition-based FDI methods becomes apparent. Thus, alternate, more sophisticated FDI methods become necessary to address such problems. Even though the effectiveness of pattern-recognition-based FDI methods using ANNs has been demonstrated, further testing using real-world data is necessary

  19. A Brief Review of the Need for Robust Smart Wireless Sensor Systems for Future Propulsion Systems, Distributed Engine Controls, and Propulsion Health Management

    Science.gov (United States)

    Hunter, Gary W.; Behbahani, Alireza

    2012-01-01

    Smart Sensor Systems with wireless capability operational in high temperature, harsh environments are a significant component in enabling future propulsion systems to meet a range of increasingly demanding requirements. These propulsion systems must incorporate technology that will monitor engine component conditions, analyze the incoming data, and modify operating parameters to optimize propulsion system operations. This paper discusses the motivation towards the development of high temperature, smart wireless sensor systems that include sensors, electronics, wireless communication, and power. The challenges associated with the use of traditional wired sensor systems will be reviewed and potential advantages of Smart Sensor Systems will be discussed. A brief review of potential applications for wireless smart sensor networks and their potential impact on propulsion system operation, with emphasis on Distributed Engine Control and Propulsion Health Management, will be given. A specific example related to the development of high temperature Smart Sensor Systems based on silicon carbide electronics will be discussed. It is concluded that the development of a range of robust smart wireless sensor systems are a foundation for future development of intelligent propulsion systems with enhanced capabilities.

  20. Event-Triggered Fault Estimation for Stochastic Systems over Multi-Hop Relay Networks with Randomly Occurring Sensor Nonlinearities and Packet Dropouts.

    Science.gov (United States)

    Li, Yunji; Peng, Li

    2018-02-28

    Wireless sensors have many new applications where remote estimation is essential. Considering that a remote estimator is located far away from the process and the wireless transmission distance of sensor nodes is limited, sensor nodes always forward data packets to the remote estimator through a series of relays over a multi-hop link. In this paper, we consider a network with sensor nodes and relay nodes where the relay nodes can forward the estimated values to the remote estimator. An event-triggered remote estimator of state and fault with the corresponding data-forwarding scheme is investigated for stochastic systems subject to both randomly occurring nonlinearity and randomly occurring packet dropouts governed by Bernoulli-distributed sequences to achieve a trade-off between estimation accuracy and energy consumption. Recursive Riccati-like matrix equations are established to calculate the estimator gain to minimize an upper bound of the estimator error covariance. Subsequently, a sufficient condition and data-forwarding scheme are presented under which the error covariance is mean-square bounded in the multi-hop links with random packet dropouts. Furthermore, implementation issues of the theoretical results are discussed where a new data-forwarding communication protocol is designed. Finally, the effectiveness of the proposed algorithms and communication protocol are extensively evaluated using an experimental platform that was established for performance evaluation with a sensor and two relay nodes.

  1. Fault diagnosis and fault-tolerant control and guidance for aerospace vehicles from theory to application

    CERN Document Server

    Zolghadri, Ali; Cieslak, Jerome; Efimov, Denis; Goupil, Philippe

    2014-01-01

    Fault Diagnosis and Fault-Tolerant Control and Guidance for Aerospace demonstrates the attractive potential of recent developments in control for resolving such issues as improved flight performance, self-protection and extended life of structures. Importantly, the text deals with a number of practically significant considerations: tuning, complexity of design, real-time capability, evaluation of worst-case performance, robustness in harsh environments, and extensibility when development or adaptation is required. Coverage of such issues helps to draw the advanced concepts arising from academic research back towards the technological concerns of industry. Initial coverage of basic definitions and ideas and a literature review gives way to a treatment of important electrical flight control system failures: the oscillatory failure case, runaway, and jamming. Advanced fault detection and diagnosis for linear and nonlinear systems are described. Lastly recovery strategies appropriate to remaining acuator/sensor/c...

  2. Sliding Mode Fault Tolerant Control with Adaptive Diagnosis for Aircraft Engines

    Science.gov (United States)

    Xiao, Lingfei; Du, Yanbin; Hu, Jixiang; Jiang, Bin

    2018-03-01

    In this paper, a novel sliding mode fault tolerant control method is presented for aircraft engine systems with uncertainties and disturbances on the basis of adaptive diagnostic observer. By taking both sensors faults and actuators faults into account, the general model of aircraft engine control systems which is subjected to uncertainties and disturbances, is considered. Then, the corresponding augmented dynamic model is established in order to facilitate the fault diagnosis and fault tolerant controller design. Next, a suitable detection observer is designed to detect the faults effectively. Through creating an adaptive diagnostic observer and based on sliding mode strategy, the sliding mode fault tolerant controller is constructed. Robust stabilization is discussed and the closed-loop system can be stabilized robustly. It is also proven that the adaptive diagnostic observer output errors and the estimations of faults converge to a set exponentially, and the converge rate greater than some value which can be adjusted by choosing designable parameters properly. The simulation on a twin-shaft aircraft engine verifies the applicability of the proposed fault tolerant control method.

  3. Bayesian Belief Networks (BBN) and Expert Systems for supporting model based sensor fault detection analysis of smart building systems

    NARCIS (Netherlands)

    Schagen, J.D.; Taal, A.; Itard, L.C.M.; Heiselberg, Per Kvols

    2016-01-01

    The Hague University in Delft uses an advanced climate control system. All sensors and actuators are monitored and deviations from the sensor data are reported daily. The building manager will have to combine the information from the sensor data in order to draw the right conclusions. In this paper,

  4. -Net Approach to Sensor -Coverage

    Directory of Open Access Journals (Sweden)

    Fusco Giordano

    2010-01-01

    Full Text Available Wireless sensors rely on battery power, and in many applications it is difficult or prohibitive to replace them. Hence, in order to prolongate the system's lifetime, some sensors can be kept inactive while others perform all the tasks. In this paper, we study the -coverage problem of activating the minimum number of sensors to ensure that every point in the area is covered by at least sensors. This ensures higher fault tolerance, robustness, and improves many operations, among which position detection and intrusion detection. The -coverage problem is trivially NP-complete, and hence we can only provide approximation algorithms. In this paper, we present an algorithm based on an extension of the classical -net technique. This method gives an -approximation, where is the number of sensors in an optimal solution. We do not make any particular assumption on the shape of the areas covered by each sensor, besides that they must be closed, connected, and without holes.

  5. Design of robust reliable control for T-S fuzzy Markovian jumping delayed neutral type neural networks with probabilistic actuator faults and leakage delays: An event-triggered communication scheme.

    Science.gov (United States)

    Syed Ali, M; Vadivel, R; Saravanakumar, R

    2018-06-01

    This study examines the problem of robust reliable control for Takagi-Sugeno (T-S) fuzzy Markovian jumping delayed neural networks with probabilistic actuator faults and leakage terms. An event-triggered communication scheme. First, the randomly occurring actuator faults and their failures rates are governed by two sets of unrelated random variables satisfying certain probabilistic failures of every actuator, new type of distribution based event triggered fault model is proposed, which utilize the effect of transmission delay. Second, Takagi-Sugeno (T-S) fuzzy model is adopted for the neural networks and the randomness of actuators failures is modeled in a Markov jump model framework. Third, to guarantee the considered closed-loop system is exponential mean square stable with a prescribed reliable control performance, a Markov jump event-triggered scheme is designed in this paper, which is the main purpose of our study. Fourth, by constructing appropriate Lyapunov-Krasovskii functional, employing Newton-Leibniz formulation and integral inequalities, several delay-dependent criteria for the solvability of the addressed problem are derived. The obtained stability criteria are stated in terms of linear matrix inequalities (LMIs), which can be checked numerically using the effective LMI toolbox in MATLAB. Finally, numerical examples are given to illustrate the effectiveness and reduced conservatism of the proposed results over the existing ones, among them one example was supported by real-life application of the benchmark problem. Copyright © 2018 ISA. Published by Elsevier Ltd. All rights reserved.

  6. The reflection of evolving bearing faults in the stator current's extended park vector approach for induction machines

    Science.gov (United States)

    Corne, Bram; Vervisch, Bram; Derammelaere, Stijn; Knockaert, Jos; Desmet, Jan

    2018-07-01

    Stator current analysis has the potential of becoming the most cost-effective condition monitoring technology regarding electric rotating machinery. Since both electrical and mechanical faults are detected by inexpensive and robust current-sensors, measuring current is advantageous on other techniques such as vibration, acoustic or temperature analysis. However, this technology is struggling to breach into the market of condition monitoring as the electrical interpretation of mechanical machine-problems is highly complicated. Recently, the authors built a test-rig which facilitates the emulation of several representative mechanical faults on an 11 kW induction machine with high accuracy and reproducibility. Operating this test-rig, the stator current of the induction machine under test can be analyzed while mechanical faults are emulated. Furthermore, while emulating, the fault-severity can be manipulated adaptively under controllable environmental conditions. This creates the opportunity of examining the relation between the magnitude of the well-known current fault components and the corresponding fault-severity. This paper presents the emulation of evolving bearing faults and their reflection in the Extended Park Vector Approach for the 11 kW induction machine under test. The results confirm the strong relation between the bearing faults and the stator current fault components in both identification and fault-severity. Conclusively, stator current analysis increases reliability in the application as a complete, robust, on-line condition monitoring technology.

  7. Development and Flight Test of a Robust Optical-Inertial Navigation System Using Low-Cost Sensors

    National Research Council Canada - National Science Library

    Nielsen, Michael B

    2008-01-01

    .... This algorithm provides an alternative to the Global Positioning System (GPS) as a precision navigation source, enabling navigation in GPS denied environments, using low-cost sensors and equipment...

  8. Ultra-Precision Measurement and Control of Angle Motion in Piezo-Based Platforms Using Strain Gauge Sensors and a Robust Composite Controller

    Science.gov (United States)

    Liu, Lei; Bai, Yu-Guang; Zhang, Da-Li; Wu, Zhi-Gang

    2013-01-01

    The measurement and control strategy of a piezo-based platform by using strain gauge sensors (SGS) and a robust composite controller is investigated in this paper. First, the experimental setup is constructed by using a piezo-based platform, SGS sensors, an AD5435 platform and two voltage amplifiers. Then, the measurement strategy to measure the tip/tilt angles accurately in the order of sub-μrad is presented. A comprehensive composite control strategy design to enhance the tracking accuracy with a novel driving principle is also proposed. Finally, an experiment is presented to validate the measurement and control strategy. The experimental results demonstrate that the proposed measurement and control strategy provides accurate angle motion with a root mean square (RMS) error of 0.21 μrad, which is approximately equal to the noise level. PMID:23860316

  9. Ultra-Precision Measurement and Control of Angle Motion in Piezo-Based Platforms Using Strain Gauge Sensors and a Robust Composite Controller

    Directory of Open Access Journals (Sweden)

    Zhi-Gang Wu

    2013-07-01

    Full Text Available The measurement and control strategy of a piezo-based platform by using strain gauge sensors (SGS and a robust composite controller is investigated in this paper. First, the experimental setup is constructed by using a piezo-based platform, SGS sensors, an AD5435 platform and two voltage amplifiers. Then, the measurement strategy to measure the tip/tilt angles accurately in the order of sub-μrad is presented. A comprehensive composite control strategy design to enhance the tracking accuracy with a novel driving principle is also proposed. Finally, an experiment is presented to validate the measurement and control strategy. The experimental results demonstrate that the proposed measurement and control strategy provides accurate angle motion with a root mean square (RMS error of 0.21 μrad, which is approximately equal to the noise level.

  10. Robust Automated Image Co-Registration of Optical Multi-Sensor Time Series Data: Database Generation for Multi-Temporal Landslide Detection

    Directory of Open Access Journals (Sweden)

    Robert Behling

    2014-03-01

    Full Text Available Reliable multi-temporal landslide detection over longer periods of time requires multi-sensor time series data characterized by high internal geometric stability, as well as high relative and absolute accuracy. For this purpose, a new methodology for fully automated co-registration has been developed allowing efficient and robust spatial alignment of standard orthorectified data products originating from a multitude of optical satellite remote sensing data of varying spatial resolution. Correlation-based co-registration uses world-wide available terrain corrected Landsat Level 1T time series data as the spatial reference, ensuring global applicability. The developed approach has been applied to a multi-sensor time series of 592 remote sensing datasets covering an approximately 12,000 km2 area in Southern Kyrgyzstan (Central Asia strongly affected by landslides. The database contains images acquired during the last 26 years by Landsat (ETM, ASTER, SPOT and RapidEye sensors. Analysis of the spatial shifts obtained from co-registration has revealed sensor-specific alignments ranging between 5 m and more than 400 m. Overall accuracy assessment of these alignments has resulted in a high relative image-to-image accuracy of 17 m (RMSE and a high absolute accuracy of 23 m (RMSE for the whole co-registered database, making it suitable for multi-temporal landslide detection at a regional scale in Southern Kyrgyzstan.

  11. Fault Diagnosis of Induction Machines in a Transient Regime Using Current Sensors with an Optimized Slepian Window.

    Science.gov (United States)

    Burriel-Valencia, Jordi; Puche-Panadero, Ruben; Martinez-Roman, Javier; Sapena-Bano, Angel; Pineda-Sanchez, Manuel

    2018-01-06

    The aim of this paper is to introduce a new methodology for the fault diagnosis of induction machines working in the transient regime, when time-frequency analysis tools are used. The proposed method relies on the use of the optimized Slepian window for performing the short time Fourier transform (STFT) of the stator current signal. It is shown that for a given sequence length of finite duration, the Slepian window has the maximum concentration of energy, greater than can be reached with a gated Gaussian window, which is usually used as the analysis window. In this paper, the use and optimization of the Slepian window for fault diagnosis of induction machines is theoretically introduced and experimentally validated through the test of a 3.15-MW induction motor with broken bars during the start-up transient. The theoretical analysis and the experimental results show that the use of the Slepian window can highlight the fault components in the current's spectrogram with a significant reduction of the required computational resources.

  12. Fault Diagnosis of Induction Machines in a Transient Regime Using Current Sensors with an Optimized Slepian Window

    Directory of Open Access Journals (Sweden)

    Jordi Burriel-Valencia

    2018-01-01

    Full Text Available The aim of this paper is to introduce a new methodology for the fault diagnosis of induction machines working in the transient regime, when time-frequency analysis tools are used. The proposed method relies on the use of the optimized Slepian window for performing the short time Fourier transform (STFT of the stator current signal. It is shown that for a given sequence length of finite duration, the Slepian window has the maximum concentration of energy, greater than can be reached with a gated Gaussian window, which is usually used as the analysis window. In this paper, the use and optimization of the Slepian window for fault diagnosis of induction machines is theoretically introduced and experimentally validated through the test of a 3.15-MW induction motor with broken bars during the start-up transient. The theoretical analysis and the experimental results show that the use of the Slepian window can highlight the fault components in the current’s spectrogram with a significant reduction of the required computational resources.

  13. H infinity Integrated Fault Estimation and Fault Tolerant Control of Discrete-time Piecewise Linear Systems

    DEFF Research Database (Denmark)

    Tabatabaeipour, Seyed Mojtaba; Bak, Thomas

    2012-01-01

    In this paper we consider the problem of fault estimation and accommodation for discrete time piecewise linear systems. A robust fault estimator is designed to estimate the fault such that the estimation error converges to zero and H∞ performance of the fault estimation is minimized. Then, the es...

  14. Optimization of the HyPer sensor for robust real-time detection of hydrogen peroxide in the rice blast fungus.

    Science.gov (United States)

    Huang, Kun; Caplan, Jeff; Sweigard, James A; Czymmek, Kirk J; Donofrio, Nicole M

    2017-02-01

    Reactive oxygen species (ROS) production and breakdown have been studied in detail in plant-pathogenic fungi, including the rice blast fungus, Magnaporthe oryzae; however, the examination of the dynamic process of ROS production in real time has proven to be challenging. We resynthesized an existing ROS sensor, called HyPer, to exhibit optimized codon bias for fungi, specifically Neurospora crassa, and used a combination of microscopy and plate reader assays to determine whether this construct could detect changes in fungal ROS during the plant infection process. Using confocal microscopy, we were able to visualize fluctuating ROS levels during the formation of an appressorium on an artificial hydrophobic surface, as well as during infection on host leaves. Using the plate reader, we were able to ascertain measurements of hydrogen peroxide (H 2 O 2 ) levels in conidia as detected by the MoHyPer sensor. Overall, by the optimization of codon usage for N. crassa and related fungal genomes, the MoHyPer sensor can be used as a robust, dynamic and powerful tool to both monitor and quantify H 2 O 2 dynamics in real time during important stages of the plant infection process. © 2016 BSPP AND JOHN WILEY & SONS LTD.

  15. Estimating Focus and Radial Distances, and Fault Residuals from CD Player Sensor Signals by use of a Kalman Estimator

    DEFF Research Database (Denmark)

    Odgaard, Peter Fogh; Stoustrup, Jakob; Andersen, Palle

    2003-01-01

    Cross coupling between focus and radial loops in Compact Disc players is a problem both in nominal operations, but also in detection of defects such as scratches and finger prints. Using a Kalman estimator with an internal reference, the actual focus and radial distances are estimated. The sensor...

  16. Fault diagnosis for engine air path with neural models and classifier ...

    African Journals Online (AJOL)

    A new FDI scheme is developed for automotive engines in this paper. The method uses an independent radial basis function (RBF) neural ... Five faults have been simulated on the MVEM, including three sensor faults, one component fault and one actuator fault. The three sensor faults considered are 10-20% changes ...

  17. Sensors

    CERN Document Server

    Pigorsch, Enrico

    1997-01-01

    This is the 5th edition of the Metra Martech Directory "EUROPEAN CENTRES OF EXPERTISE - SENSORS." The entries represent a survey of European sensors development. The new edition contains 425 detailed profiles of companies and research institutions in 22 countries. This is reflected in the diversity of sensors development programmes described, from sensors for physical parameters to biosensors and intelligent sensor systems. We do not claim that all European organisations developing sensors are included, but this is a good cross section from an invited list of participants. If you see gaps or omissions, or would like your organisation to be included, please send details. The data base invites the formation of effective joint ventures by identifying and providing access to specific areas in which organisations offer collaboration. This issue is recognised to be of great importance and most entrants include details of collaboration offered and sought. We hope the directory on Sensors will help you to find the ri...

  18. Sensors

    Energy Technology Data Exchange (ETDEWEB)

    Jensen, H. [PBI-Dansensor A/S (Denmark); Toft Soerensen, O. [Risoe National Lab., Materials Research Dept. (Denmark)

    1999-10-01

    A new type of ceramic oxygen sensors based on semiconducting oxides was developed in this project. The advantage of these sensors compared to standard ZrO{sub 2} sensors is that they do not require a reference gas and that they can be produced in small sizes. The sensor design and the techniques developed for production of these sensors are judged suitable by the participating industry for a niche production of a new generation of oxygen sensors. Materials research on new oxygen ion conducting conductors both for applications in oxygen sensors and in fuel was also performed in this project and finally a new process was developed for fabrication of ceramic tubes by dip-coating. (EHS)

  19. Development of Fault Models for Hybrid Fault Detection and Diagnostics Algorithm: October 1, 2014 -- May 5, 2015

    Energy Technology Data Exchange (ETDEWEB)

    Cheung, Howard [Purdue Univ., West Lafayette, IN (United States); Braun, James E. [Purdue Univ., West Lafayette, IN (United States)

    2015-12-31

    This report describes models of building faults created for OpenStudio to support the ongoing development of fault detection and diagnostic (FDD) algorithms at the National Renewable Energy Laboratory. Building faults are operating abnormalities that degrade building performance, such as using more energy than normal operation, failing to maintain building temperatures according to the thermostat set points, etc. Models of building faults in OpenStudio can be used to estimate fault impacts on building performance and to develop and evaluate FDD algorithms. The aim of the project is to develop fault models of typical heating, ventilating and air conditioning (HVAC) equipment in the United States, and the fault models in this report are grouped as control faults, sensor faults, packaged and split air conditioner faults, water-cooled chiller faults, and other uncategorized faults. The control fault models simulate impacts of inappropriate thermostat control schemes such as an incorrect thermostat set point in unoccupied hours and manual changes of thermostat set point due to extreme outside temperature. Sensor fault models focus on the modeling of sensor biases including economizer relative humidity sensor bias, supply air temperature sensor bias, and water circuit temperature sensor bias. Packaged and split air conditioner fault models simulate refrigerant undercharging, condenser fouling, condenser fan motor efficiency degradation, non-condensable entrainment in refrigerant, and liquid line restriction. Other fault models that are uncategorized include duct fouling, excessive infiltration into the building, and blower and pump motor degradation.

  20. Road Lane Detection Robust to Shadows Based on a Fuzzy System Using a Visible Light Camera Sensor

    Directory of Open Access Journals (Sweden)

    Toan Minh Hoang

    2017-10-01

    Full Text Available Recently, autonomous vehicles, particularly self-driving cars, have received significant attention owing to rapid advancements in sensor and computation technologies. In addition to traffic sign recognition, road lane detection is one of the most important factors used in lane departure warning systems and autonomous vehicles for maintaining the safety of semi-autonomous and fully autonomous systems. Unlike traffic signs, road lanes are easily damaged by both internal and external factors such as road quality, occlusion (traffic on the road, weather conditions, and illumination (shadows from objects such as cars, trees, and buildings. Obtaining clear road lane markings for recognition processing is a difficult challenge. Therefore, we propose a method to overcome various illumination problems, particularly severe shadows, by using fuzzy system and line segment detector algorithms to obtain better results for detecting road lanes by a visible light camera sensor. Experimental results from three open databases, Caltech dataset, Santiago Lanes dataset (SLD, and Road Marking dataset, showed that our method outperformed conventional lane detection methods.

  1. Road Lane Detection Robust to Shadows Based on a Fuzzy System Using a Visible Light Camera Sensor.

    Science.gov (United States)

    Hoang, Toan Minh; Baek, Na Rae; Cho, Se Woon; Kim, Ki Wan; Park, Kang Ryoung

    2017-10-28

    Recently, autonomous vehicles, particularly self-driving cars, have received significant attention owing to rapid advancements in sensor and computation technologies. In addition to traffic sign recognition, road lane detection is one of the most important factors used in lane departure warning systems and autonomous vehicles for maintaining the safety of semi-autonomous and fully autonomous systems. Unlike traffic signs, road lanes are easily damaged by both internal and external factors such as road quality, occlusion (traffic on the road), weather conditions, and illumination (shadows from objects such as cars, trees, and buildings). Obtaining clear road lane markings for recognition processing is a difficult challenge. Therefore, we propose a method to overcome various illumination problems, particularly severe shadows, by using fuzzy system and line segment detector algorithms to obtain better results for detecting road lanes by a visible light camera sensor. Experimental results from three open databases, Caltech dataset, Santiago Lanes dataset (SLD), and Road Marking dataset, showed that our method outperformed conventional lane detection methods.

  2. Fault Tolerant Control Systems

    DEFF Research Database (Denmark)

    Bøgh, S. A.

    This thesis considered the development of fault tolerant control systems. The focus was on the category of automated processes that do not necessarily comprise a high number of identical sensors and actuators to maintain safe operation, but still have a potential for improving immunity to component...

  3. Robust Multivariable Estimation of the Relevant Information Coming from a Wheel Speed Sensor and an Accelerometer Embedded in a Car under Performance Tests

    Directory of Open Access Journals (Sweden)

    Wilmar Hernandez

    2005-11-01

    Full Text Available In the present paper, in order to estimate the response of both a wheel speedsensor and an accelerometer placed in a car under performance tests, robust and optimalmultivariable estimation techniques are used. In this case, the disturbances and noisescorrupting the relevant information coming from the sensors’ outputs are so dangerous thattheir negative influence on the electrical systems impoverish the general performance of thecar. In short, the solution to this problem is a safety related problem that deserves our fullattention. Therefore, in order to diminish the negative effects of the disturbances and noiseson the car’s electrical and electromechanical systems, an optimum observer is used. Theexperimental results show a satisfactory improvement in the signal-to-noise ratio of therelevant signals and demonstrate the importance of the fusion of several intelligent sensordesign techniques when designing the intelligent sensors that today’s cars need.

  4. A New Generation of FRET Sensors for Robust Measurement of Gαi1, Gαi2 and Gαi3 Activation Kinetics in Single Cells.

    Directory of Open Access Journals (Sweden)

    Jakobus van Unen

    Full Text Available G-protein coupled receptors (GPCRs can activate a heterotrimeric G-protein complex with subsecond kinetics. Genetically encoded biosensors based on Förster resonance energy transfer (FRET are ideally suited for the study of such fast signaling events in single living cells. Here we report on the construction and characterization of three FRET biosensors for the measurement of Gαi1, Gαi2 and Gαi3 activation. To enable quantitative long-term imaging of FRET biosensors with high dynamic range, fluorescent proteins with enhanced photophysical properties are required. Therefore, we use the currently brightest and most photostable CFP variant, mTurquoise2, as donor fused to Gαi subunit, and cp173Venus fused to the Gγ2 subunit as acceptor. The Gαi FRET biosensors constructs are expressed together with Gβ1 from a single plasmid, providing preferred relative expression levels with reduced variation in mammalian cells. The Gαi FRET sensors showed a robust response to activation of endogenous or over-expressed alpha-2A-adrenergic receptors, which was inhibited by pertussis toxin. Moreover, we observed activation of the Gαi FRET sensor in single cells upon stimulation of several GPCRs, including the LPA2, M3 and BK2 receptor. Furthermore, we show that the sensors are well suited to extract kinetic parameters from fast measurements in the millisecond time range. This new generation of FRET biosensors for Gαi1, Gαi2 and Gαi3 activation will be valuable for live-cell measurements that probe Gαi activation.

  5. Fault estimation - A standard problem approach

    DEFF Research Database (Denmark)

    Stoustrup, J.; Niemann, Hans Henrik

    2002-01-01

    This paper presents a range of optimization based approaches to fault diagnosis. A variety of fault diagnosis problems are reformulated in the so-called standard problem set-up introduced in the literature on robust control. Once the standard problem formulations are given, the fault diagnosis...... problems can be solved by standard optimization techniques. The proposed methods include (1) fault diagnosis (fault estimation, (FE)) for systems with model uncertainties; FE for systems with parametric faults, and FE for a class of nonlinear systems. Copyright...

  6. Faults Images

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — Through the study of faults and their effects, much can be learned about the size and recurrence intervals of earthquakes. Faults also teach us about crustal...

  7. Laboratory scale micro-seismic monitoring of rock faulting and injection-induced fault reactivation

    Science.gov (United States)

    Sarout, J.; Dautriat, J.; Esteban, L.; Lumley, D. E.; King, A.

    2017-12-01

    The South West Hub CCS project in Western Australia aims to evaluate the feasibility and impact of geosequestration of CO2 in the Lesueur sandstone formation. Part of this evaluation focuses on the feasibility and design of a robust passive seismic monitoring array. Micro-seismicity monitoring can be used to image the injected CO2plume, or any geomechanical fracture/fault activity; and thus serve as an early warning system by measuring low-level (unfelt) seismicity that may precede potentially larger (felt) earthquakes. This paper describes laboratory deformation experiments replicating typical field scenarios of fluid injection in faulted reservoirs. Two pairs of cylindrical core specimens were recovered from the Harvey-1 well at depths of 1924 m and 2508 m. In each specimen a fault is first generated at the in situ stress, pore pressure and temperature by increasing the vertical stress beyond the peak in a triaxial stress vessel at CSIRO's Geomechanics & Geophysics Lab. The faulted specimen is then stabilized by decreasing the vertical stress. The freshly formed fault is subsequently reactivated by brine injection and increase of the pore pressure until slip occurs again. This second slip event is then controlled in displacement and allowed to develop for a few millimeters. The micro-seismic (MS) response of the rock during the initial fracturing and subsequent reactivation is monitored using an array of 16 ultrasonic sensors attached to the specimen's surface. The recorded MS events are relocated in space and time, and correlate well with the 3D X-ray CT images of the specimen obtained post-mortem. The time evolution of the structural changes induced within the triaxial stress vessel is therefore reliably inferred. The recorded MS activity shows that, as expected, the increase of the vertical stress beyond the peak led to an inclined shear fault. The injection of fluid and the resulting increase in pore pressure led first to a reactivation of the pre

  8. Fault finder

    Science.gov (United States)

    Bunch, Richard H.

    1986-01-01

    A fault finder for locating faults along a high voltage electrical transmission line. Real time monitoring of background noise and improved filtering of input signals is used to identify the occurrence of a fault. A fault is detected at both a master and remote unit spaced along the line. A master clock synchronizes operation of a similar clock at the remote unit. Both units include modulator and demodulator circuits for transmission of clock signals and data. All data is received at the master unit for processing to determine an accurate fault distance calculation.

  9. Pulse-Like Rupture Induced by Three-Dimensional Fault Zone Flower Structures

    KAUST Repository

    Pelties, Christian; Huang, Yihe; Ampuero, Jean-Paul

    2014-01-01

    interface. This effect is robust against a wide range of fault zone widths, absence of frictional healing, variation of initial stress conditions, attenuation, and off-fault plasticity. These numerical studies covered two-dimensional problems with fault

  10. Model-Based Method for Sensor Validation

    Science.gov (United States)

    Vatan, Farrokh

    2012-01-01

    Fault detection, diagnosis, and prognosis are essential tasks in the operation of autonomous spacecraft, instruments, and in situ platforms. One of NASA s key mission requirements is robust state estimation. Sensing, using a wide range of sensors and sensor fusion approaches, plays a central role in robust state estimation, and there is a need to diagnose sensor failure as well as component failure. Sensor validation can be considered to be part of the larger effort of improving reliability and safety. The standard methods for solving the sensor validation problem are based on probabilistic analysis of the system, from which the method based on Bayesian networks is most popular. Therefore, these methods can only predict the most probable faulty sensors, which are subject to the initial probabilities defined for the failures. The method developed in this work is based on a model-based approach and provides the faulty sensors (if any), which can be logically inferred from the model of the system and the sensor readings (observations). The method is also more suitable for the systems when it is hard, or even impossible, to find the probability functions of the system. The method starts by a new mathematical description of the problem and develops a very efficient and systematic algorithm for its solution. The method builds on the concepts of analytical redundant relations (ARRs).

  11. Development of an artificial neural network for monitoring and diagnosis of sensor fault and detection in the IEA-R1 research reactor at IPEN; Utilizacao de redes neurais artificiais na monitoracao e deteccao de falhas em sensores do reator IEA-R1

    Energy Technology Data Exchange (ETDEWEB)

    Bueno, Elaine Inacio

    2006-07-01

    The increasing demand on quality in production processes has encouraged the development of several studies on Monitoring and Diagnosis Systems in industrial plant, where the interruption of the production due to some unexpected change can bring risk to the operator's security besides provoking economic losses, increasing the costs to repair some damaged equipment. Because of these two points, the economic losses and the operator's security, it becomes necessary to implement Monitoring and Diagnosis Systems. In this work, a Monitoring and Diagnosis Systems was developed based on the Artificial Neural Networks methodology. This methodology was applied to the IEA-R1 research reactor at IPEN. The development of this system was divided in three stages: the first was dedicated to monitoring, the second to the detection and the third to diagnosis of failures. In the first stage, several Artificial Neural Networks were trained to monitor the temperature variables, nuclear power and dose rate. Two databases were used: one with data generated by a theoretical model and another one with data to a typical week of operation of the IEA-R1 reactor. In the second stage, the neural networks used to monitor the variables was tested with a fault database. The faults were inserted artificially in the sensors signals. As the value of the maximum calibration error for special thermocouples is {+-}0,5 deg C, it had been inserted faults of {+-} 10 C in the sensors for the reading of the variables T3 and T4. In the third stage a Fuzzy System was developed to carry out the faults diagnosis, where were considered three conditions: a normal condition, a fault of {sub 1}0 C , and a fault of + 10 C . This system will indicate which thermocouple is faulty. (author)

  12. Sensor

    OpenAIRE

    Gleeson, Helen; Dierking, Ingo; Grieve, Bruce; Woodyatt, Christopher; Brimicombe, Paul

    2015-01-01

    An electrical temperature sensor (10) comprises a liquid crystalline material (12). First and second electrically conductive contacts (14), (16), having a spaced relationship there between, contact the liquid crystalline material (12). An electric property measuring device is electrically connected to the first and second contacts (14), (16) and is arranged to measure an electric property of the liquid crystalline material (12). The liquid crystalline material (12) has a transition temperatur...

  13. Nuclear power plant pressurizer fault diagnosis using fuzzy signed-digraph and spurious faults elimination methods

    International Nuclear Information System (INIS)

    Park, Joo Hyun

    1994-02-01

    In this work, the Fuzzy Signed Digraph(FSD) method which has been researched for the fault diagnosis of industrial process plant systems is improved and applied to the fault diagnosis of the Kori-2 nuclear power plant pressurizer. A method for spurious faults elimination is also suggested and applied to the fault diagnosis. By using these methods, we could diagnose the multi-faults of the pressurizer and could also eliminate the spurious faults of the pressurizer caused by other subsystems. Besides the multi-fault diagnosis and system-wide diagnosis capabilities, the proposed method has many merits such as real-time diagnosis capability, independency of fault pattern, direct use of sensor values, and transparency of the fault propagation to the operators

  14. Nuclear power plant pressurizer fault diagnosis using fuzzy signed-digraph and spurious faults elimination methods

    International Nuclear Information System (INIS)

    Park, Joo Hyun; Seong, Poong Hyun

    1994-01-01

    In this work, the Fuzzy Signed Digraph (FSD) method which has been researched for the fault diagnosis of industrial process plant systems is improved and applied to the fault diagnosis of the Kori-2 nuclear power plant pressurizer. A method for spurious faults elimination is also suggested and applied to the fault diagnosis. By using these methods, we could diagnose the multi-faults of the pressurizer and could also eliminate the spurious faults of the pressurizer caused by other subsystems. Besides the multi-fault diagnosis and system-wide diagnosis capabilities, the proposed method has many merits such as real-time diagnosis capability, independency of fault pattern, direct use of sensor values, and transparency of the fault propagation to the operators. (Author)

  15. Development and Evaluation of Fault-Tolerant Flight Control Systems

    Science.gov (United States)

    Song, Yong D.; Gupta, Kajal (Technical Monitor)

    2004-01-01

    The research is concerned with developing a new approach to enhancing fault tolerance of flight control systems. The original motivation for fault-tolerant control comes from the need for safe operation of control elements (e.g. actuators) in the event of hardware failures in high reliability systems. One such example is modem space vehicle subjected to actuator/sensor impairments. A major task in flight control is to revise the control policy to balance impairment detectability and to achieve sufficient robustness. This involves careful selection of types and parameters of the controllers and the impairment detecting filters used. It also involves a decision, upon the identification of some failures, on whether and how a control reconfiguration should take place in order to maintain a certain system performance level. In this project new flight dynamic model under uncertain flight conditions is considered, in which the effects of both ramp and jump faults are reflected. Stabilization algorithms based on neural network and adaptive method are derived. The control algorithms are shown to be effective in dealing with uncertain dynamics due to external disturbances and unpredictable faults. The overall strategy is easy to set up and the computation involved is much less as compared with other strategies. Computer simulation software is developed. A serious of simulation studies have been conducted with varying flight conditions.

  16. A Novel Data Hierarchical Fusion Method for Gas Turbine Engine Performance Fault Diagnosis

    Directory of Open Access Journals (Sweden)

    Feng Lu

    2016-10-01

    Full Text Available Gas path fault diagnosis involves the effective utilization of condition-based sensor signals along engine gas path to accurately identify engine performance failure. The rapid development of information processing technology has led to the use of multiple-source information fusion for fault diagnostics. Numerous efforts have been paid to develop data-based fusion methods, such as neural networks fusion, while little research has focused on fusion architecture or the fusion of different method kinds. In this paper, a data hierarchical fusion using improved weighted Dempster–Shaffer evidence theory (WDS is proposed, and the integration of data-based and model-based methods is presented for engine gas-path fault diagnosis. For the purpose of simplifying learning machine typology, a recursive reduced kernel based extreme learning machine (RR-KELM is developed to produce the fault probability, which is considered as the data-based evidence. Meanwhile, the model-based evidence is achieved using particle filter-fuzzy logic algorithm (PF-FL by engine health estimation and component fault location in feature level. The outputs of two evidences are integrated using WDS evidence theory in decision level to reach a final recognition decision of gas-path fault pattern. The characteristics and advantages of two evidences are analyzed and used as guidelines for data hierarchical fusion framework. Our goal is that the proposed methodology provides much better performance of gas-path fault diagnosis compared to solely relying on data-based or model-based method. The hierarchical fusion framework is evaluated in terms to fault diagnosis accuracy and robustness through a case study involving fault mode dataset of a turbofan engine that is generated by the general gas turbine simulation. These applications confirm the effectiveness and usefulness of the proposed approach.

  17. Fault Detection and Isolation using Eigenstructure Assignment

    DEFF Research Database (Denmark)

    Jørgensen, R. B.; Patton, R.; Chen, J.

    1994-01-01

    The purpose of this article is to investigate the robustness to model uncertainties of observer based fault detection and isolation. The approach is designed with a straight forward dynamic nad the observer.......The purpose of this article is to investigate the robustness to model uncertainties of observer based fault detection and isolation. The approach is designed with a straight forward dynamic nad the observer....

  18. Real-Time Fault Tolerant Networking Protocols

    National Research Council Canada - National Science Library

    Henzinger, Thomas A

    2004-01-01

    We made significant progress in the areas of video streaming, wireless protocols, mobile ad-hoc and sensor networks, peer-to-peer systems, fault tolerant algorithms, dependability and timing analysis...

  19. Fault tolerant operation of switched reluctance machine

    Science.gov (United States)

    Wang, Wei

    The energy crisis and environmental challenges have driven industry towards more energy efficient solutions. With nearly 60% of electricity consumed by various electric machines in industry sector, advancement in the efficiency of the electric drive system is of vital importance. Adjustable speed drive system (ASDS) provides excellent speed regulation and dynamic performance as well as dramatically improved system efficiency compared with conventional motors without electronics drives. Industry has witnessed tremendous grow in ASDS applications not only as a driving force but also as an electric auxiliary system for replacing bulky and low efficiency auxiliary hydraulic and mechanical systems. With the vast penetration of ASDS, its fault tolerant operation capability is more widely recognized as an important feature of drive performance especially for aerospace, automotive applications and other industrial drive applications demanding high reliability. The Switched Reluctance Machine (SRM), a low cost, highly reliable electric machine with fault tolerant operation capability, has drawn substantial attention in the past three decades. Nevertheless, SRM is not free of fault. Certain faults such as converter faults, sensor faults, winding shorts, eccentricity and position sensor faults are commonly shared among all ASDS. In this dissertation, a thorough understanding of various faults and their influence on transient and steady state performance of SRM is developed via simulation and experimental study, providing necessary knowledge for fault detection and post fault management. Lumped parameter models are established for fast real time simulation and drive control. Based on the behavior of the faults, a fault detection scheme is developed for the purpose of fast and reliable fault diagnosis. In order to improve the SRM power and torque capacity under faults, the maximum torque per ampere excitation are conceptualized and validated through theoretical analysis and

  20. Rectifier Fault Diagnosis and Fault Tolerance of a Doubly Fed Brushless Starter Generator

    Directory of Open Access Journals (Sweden)

    Liwei Shi

    2015-01-01

    Full Text Available This paper presents a rectifier fault diagnosis method with wavelet packet analysis to improve the fault tolerant four-phase doubly fed brushless starter generator (DFBLSG system reliability. The system components and fault tolerant principle of the high reliable DFBLSG are given. And the common fault of the rectifier is analyzed. The process of wavelet packet transforms fault detection/identification algorithm is introduced in detail. The fault tolerant performance and output voltage experiments were done to gather the energy characteristics with a voltage sensor. The signal is analyzed with 5-layer wavelet packets, and the energy eigenvalue of each frequency band is obtained. Meanwhile, the energy-eigenvalue tolerance was introduced to improve the diagnostic accuracy. With the wavelet packet fault diagnosis, the fault tolerant four-phase DFBLSG can detect the usual open-circuit fault and operate in the fault tolerant mode if there is a fault. The results indicate that the fault analysis techniques in this paper are accurate and effective.

  1. New development in relay protection for smart grid : new principles of fault distinction

    Energy Technology Data Exchange (ETDEWEB)

    Zhang, B.; Hao, Z. [Xi' an Jiaotong Univ., Xian (China); Klimek, A. [Powertech Labs Inc., Surrey, BC (Canada); Bo, Z. [Alstom Grid Automation (United Kingdom)

    2010-07-01

    China is planning to integrate a smart grid into a proposed 750/1000 kV transmission network. However, the performance of the protection relay must be assured in order to have this kind of transmitting capacity. There are many protection strategies that address the many demands of a smart grid, including ultra-high-speed transient-based fault discrimination; new co-ordination principles of main and back-up protection to suit the diversification of the power network; optimal co-ordination between relay protection; and autoreclosure to enhance robustness of the power network. There are also new developments in protection early warning and tripping functions in the protection concepts based on wide area information. This paper presented the principles, algorithms and techniques of single-ended, transient-based and ultra-high-speed protection for extra-high voltage (EHV) transmission lines, buses, DC transmission lines and faulted line selection for non-solid earthed networks. Test results have verified that the proposed methods can determine fault characteristics with ultra-high-speed (5 ms), and that the new principles of fault discrimination can satisfy the demand of EHV systems within a smart grid. High speed Digital Signal Processor (DSP) embedded system techniques combined with optical sensors provide the ability to record and compute detailed fault transients. This technology makes it possible to implement protection principles based on transient information. Due to the inconsistent nature of the wave impedance for various power apparatuses and the reflection and refraction characteristics of their interconnection points, the fault transients contain abundant information about the fault location and type. It is possible to construct ultra high speed and more sensitive AC, DC and busbar main protection through the correct analysis of such information. 23 refs., 6 figs.

  2. Fault tolerant control for steam generators in nuclear power plant

    International Nuclear Information System (INIS)

    Deng Zhihong; Shi Xiaocheng; Xia Guoqing; Fu Mingyu

    2010-01-01

    Based on the nonlinear system with stochastic noise, a bank of extended Kalman filters is used to estimate the state of sensors. It can real-time detect and isolate the single sensor fault, and reconstruct the sensor output to keep steam generator water level stable. The simulation results show that the methodology of employing a bank of extended Kalman filters for steam generator fault tolerant control design is feasible. (authors)

  3. A Low-Power Wireless Image Sensor Node with Noise-Robust Moving Object Detection and a Region-of-Interest Based Rate Controller

    Science.gov (United States)

    2017-03-01

    from both environment and hardware further reduces the transmission energy with negligible computation and memory overhead. The rate controller...detection, Region-of-interest, Rate control Introduction In wireless image sensor nodes for moving object surveillance, energy efficiency can be...noise, reliable moving object detection is required to avoid unnecessary transmission of background scenes [1]. Transmission energy can be further

  4. Research on a Rotating Machinery Fault Prognosis Method Using Three-Dimensional Spatial Representations

    Directory of Open Access Journals (Sweden)

    Xiaoni Dong

    2016-01-01

    Full Text Available Process models and parameters are two critical steps for fault prognosis in the operation of rotating machinery. Due to the requirement for a short and rapid response, it is important to study robust sensor data representation schemes. However, the conventional holospectrum defined by one-dimensional or two-dimensional methods does not sufficiently present this information in both the frequency and time domains. To supply a complete holospectrum model, a new three-dimensional spatial representation method is proposed. This method integrates improved three-dimensional (3D holospectra and 3D filtered orbits, leading to the integration of radial and axial vibration features in one bearing section. The results from simulation and experimental analysis on a complex compressor show that the proposed method can present the real operational status and clearly reveal early faults, thus demonstrating great potential for condition-based maintenance prediction in industrial machinery.

  5. Robust Scientists

    DEFF Research Database (Denmark)

    Gorm Hansen, Birgitte

    their core i nterests, 2) developing a selfsupply of industry interests by becoming entrepreneurs and thus creating their own compliant industry partner and 3) balancing resources within a larger collective of researchers, thus countering changes in the influx of funding caused by shifts in political...... knowledge", Danish research policy seems to have helped develop politically and economically "robust scientists". Scientific robustness is acquired by way of three strategies: 1) tasting and discriminating between resources so as to avoid funding that erodes academic profiles and push scientists away from...

  6. Fault Isolation and quality assessment for shipboard monitoring

    DEFF Research Database (Denmark)

    Lajic, Zoran; Nielsen, Ulrik Dam; Blanke, Mogens

    2010-01-01

    system and to improve multi-sensor data fusion for the particular system. Fault isolation is an important part of the fault tolerant design for in-service monitoring and decision support systems for ships. In the paper, a virtual example of fault isolation will be presented. Several possible faults...... will be simulated and isolated using residuals and the generalized likelihood ratio (GLR) algorithm. It will be demonstrated that the approach can be used to increase accuracy of sea state estimations employing sensor fusion quality test....

  7. Optimal fault signal estimation

    NARCIS (Netherlands)

    Stoorvogel, Antonie Arij; Niemann, H.H.; Saberi, A.; Sannuti, P.

    2002-01-01

    We consider here both fault identification and fault signal estimation. Regarding fault identification, we seek either exact or almost fault identification. On the other hand, regarding fault signal estimation, we seek either $H_2$ optimal, $H_2$ suboptimal or Hinfinity suboptimal estimation. By

  8. An innovative modular device and wireless control system enabling thermal and pressure sensors using FPGA on real-time fault diagnostics of steam turbine functional deterioration

    Science.gov (United States)

    Devi, S.; Saravanan, M.

    2018-03-01

    It is necessary that the condition of the steam turbines is continuously monitored on a scheduled basis for the safe operation of the steam turbines. The review showed that steam turbine fault detection and operation maintenance system (STFDOMS) is gaining importance recently. In this paper, novel hardware architecture is proposed for STFDOMS that can be communicated through the GSM network. Arduino is interfaced with the FPGA so as to transfer the message. The design has been simulated using the Verilog programming language and implemented in hardware using FPGA. The proposed system is shown to be a simple, cost effective and flexible and thereby making it suitable for the maintenance of steam turbines. This system forewarns the experts to access to data messages and take necessary action in a short period with great accuracy. The hardware developed is promised as a real-time test bench, specifically for investigations of long haul effects with different parameter settings.

  9. Adaptive FTC based on Control Allocation and Fault Accommodation for Satellite Reaction Wheels

    DEFF Research Database (Denmark)

    Baldi, P.; Blanke, Mogens; Castaldi, P.

    2016-01-01

    and fault accommodation module directly exploiting the on-line fault estimates. The use of the nonlinear geometric approach and radial basis function neural networks allows to obtain a precise fault isolation, independently from the knowledge of aerodynamic disturbance parameters, and to design generalised......This paper proposes an active fault tolerant control scheme to cope with faults or failures affecting the flywheel spin rate sensors or satellite reaction wheel motors. The active fault tolerant control system consists of a fault detection and diagnosis module along with a control allocation...... estimation filters, which do not need a priori information about the internal model of the signal to be estimated. The adaptive control allocation and sensor fault accommodation can handle both temporal faults and failures. Simulation results illustrate the convincing fault correction and attitude control...

  10. Mine-hoist active fault tolerant control system and strategy

    Energy Technology Data Exchange (ETDEWEB)

    Wang, Z.; Wang, Y.; Meng, J.; Zhao, P.; Chang, Y. [China University of Mining and Technology, Xuzhou (China)] wzjsdstu@163.com

    2005-06-01

    Based on fault diagnosis and fault tolerant technologies, the mine-hoist active fault-tolerant control system (MAFCS) is presented with corresponding strategies, which includes the fault diagnosis module (FDM), the dynamic library (DL) and the fault-tolerant control model (FCM). When a fault is judged from some sensor by the FDM, FCM reconfigures the state of the MAFCS by calling the parameters from all sub libraries in DL, in order to ensure the reliability and safety of the mine hoist. The simulating result shows that MAFCS is of certain intelligence, which can adopt the corresponding control strategies according to different fault modes, even when there is quite a difference between the real data and the prior fault modes. 7 refs., 5 figs., 1 tab.

  11. Fault-tolerant and Diagnostic Methods for Navigation

    DEFF Research Database (Denmark)

    Blanke, Mogens

    2003-01-01

    to diagnose faults and autonomously provide valid navigation data, disregarding any faulty sensor data and use sensor fusion to obtain a best estimate for users. This paper discusses how diagnostic and fault-tolerant methods are applicable in marine systems. An example chosen is sensor fusion for navigation......Precise and reliable navigation is crucial, and for reasons of safety, essential navigation instruments are often duplicated. Hardware redundancy is mostly used to manually switch between instruments should faults occur. In contrast, diagnostic methods are available that can use analytic redundancy...

  12. Fault Tolerant Control of Wind Turbines

    DEFF Research Database (Denmark)

    Odgaard, Peter Fogh; Stoustrup, Jakob; Kinnaert, Michel

    2013-01-01

    This paper presents a test benchmark model for the evaluation of fault detection and accommodation schemes. This benchmark model deals with the wind turbine on a system level, and it includes sensor, actuator, and system faults, namely faults in the pitch system, the drive train, the generator......, and the converter system. Since it is a system-level model, converter and pitch system models are simplified because these are controlled by internal controllers working at higher frequencies than the system model. The model represents a three-bladed pitch-controlled variable-speed wind turbine with a nominal power...

  13. Fault Tolerant Control: A Simultaneous Stabilization Result

    DEFF Research Database (Denmark)

    Stoustrup, Jakob; Blondel, V.D.

    2004-01-01

    This paper discusses the problem of designing fault tolerant compensators that stabilize a given system both in the nominal situation, as well as in the situation where one of the sensors or one of the actuators has failed. It is shown that such compensators always exist, provided that the system...... is detectable from each output and that it is stabilizable. The proof of this result is constructive, and a worked example shows how to design a fault tolerant compensator for a simple, yet challeging system. A family of second order systems is described that requires fault tolerant compensators of arbitrarily...

  14. Navigation System Fault Diagnosis for Underwater Vehicle

    DEFF Research Database (Denmark)

    Falkenberg, Thomas; Gregersen, Rene Tavs; Blanke, Mogens

    2014-01-01

    This paper demonstrates fault diagnosis on unmanned underwater vehicles (UUV) based on analysis of structure of the nonlinear dynamics. Residuals are generated using dierent approaches in structural analysis followed by statistical change detection. Hypothesis testing thresholds are made signal...... based to cope with non-ideal properties seen in real data. Detection of both sensor and thruster failures are demonstrated. Isolation is performed using the residual signature of detected faults and the change detection algorithm is used to assess severity of faults by estimating their magnitude...

  15. Fault size classification of rotating machinery using support vector machine

    Energy Technology Data Exchange (ETDEWEB)

    Kim, Y. S.; Lee, D. H.; Park, S. K. [Korea Hydro and Nuclear Power Co. Ltd., Daejeon (Korea, Republic of)

    2012-03-15

    Studies on fault diagnosis of rotating machinery have been carried out to obtain a machinery condition in two ways. First is a classical approach based on signal processing and analysis using vibration and acoustic signals. Second is to use artificial intelligence techniques to classify machinery conditions into normal or one of the pre-determined fault conditions. Support Vector Machine (SVM) is well known as intelligent classifier with robust generalization ability. In this study, a two-step approach is proposed to predict fault types and fault sizes of rotating machinery in nuclear power plants using multi-class SVM technique. The model firstly classifies normal and 12 fault types and then identifies their sizes in case of predicting any faults. The time and frequency domain features are extracted from the measured vibration signals and used as input to SVM. A test rig is used to simulate normal and the well-know 12 artificial fault conditions with three to six fault sizes of rotating machinery. The application results to the test data show that the present method can estimate fault types as well as fault sizes with high accuracy for bearing an shaft-related faults and misalignment. Further research, however, is required to identify fault size in case of unbalance, rubbing, looseness, and coupling-related faults.

  16. Fault size classification of rotating machinery using support vector machine

    International Nuclear Information System (INIS)

    Kim, Y. S.; Lee, D. H.; Park, S. K.

    2012-01-01

    Studies on fault diagnosis of rotating machinery have been carried out to obtain a machinery condition in two ways. First is a classical approach based on signal processing and analysis using vibration and acoustic signals. Second is to use artificial intelligence techniques to classify machinery conditions into normal or one of the pre-determined fault conditions. Support Vector Machine (SVM) is well known as intelligent classifier with robust generalization ability. In this study, a two-step approach is proposed to predict fault types and fault sizes of rotating machinery in nuclear power plants using multi-class SVM technique. The model firstly classifies normal and 12 fault types and then identifies their sizes in case of predicting any faults. The time and frequency domain features are extracted from the measured vibration signals and used as input to SVM. A test rig is used to simulate normal and the well-know 12 artificial fault conditions with three to six fault sizes of rotating machinery. The application results to the test data show that the present method can estimate fault types as well as fault sizes with high accuracy for bearing an shaft-related faults and misalignment. Further research, however, is required to identify fault size in case of unbalance, rubbing, looseness, and coupling-related faults

  17. Active Sensor Configuration Validation for Refrigeration Systems

    DEFF Research Database (Denmark)

    Hovgaard, Tobias Gybel; Blanke, Mogens; Niemann, Hans Henrik

    2010-01-01

    -diagnosis methods falling short on this problem, this paper suggests an active diagnosis procedure to isolate sensor faults at the commissioning stage, before normal operation has started. Using statistical methods, residuals are evaluated versus multiple hypothesis models in a minimization process to uniquely......Major faults in the commissioning phase of refrigeration systems are caused by defects related to sensors. With a number of similar sensors available that do not differ by type but only by spatial location in the plant, interchange of sensors is a common defect. With sensors being used quite...... differently by the control system, fault-finding is difficult in practice and defects are regularly causing commissioning delays at considerable expense. Validation and handling of faults in the sensor configuration are therefore essential to cut costs during commissioning. With passive fault...

  18. Robust Floor Determination Algorithm for Indoor Wireless Localization Systems under Reference Node Failure

    Directory of Open Access Journals (Sweden)

    Kriangkrai Maneerat

    2016-01-01

    Full Text Available One of the challenging problems for indoor wireless multifloor positioning systems is the presence of reference node (RN failures, which cause the values of received signal strength (RSS to be missed during the online positioning phase of the location fingerprinting technique. This leads to performance degradation in terms of floor accuracy, which in turn affects other localization procedures. This paper presents a robust floor determination algorithm called Robust Mean of Sum-RSS (RMoS, which can accurately determine the floor on which mobile objects are located and can work under either the fault-free scenario or the RN-failure scenarios. The proposed fault tolerance floor algorithm is based on the mean of the summation of the strongest RSSs obtained from the IEEE 802.15.4 Wireless Sensor Networks (WSNs during the online phase. The performance of the proposed algorithm is compared with those of different floor determination algorithms in literature. The experimental results show that the proposed robust floor determination algorithm outperformed the other floor algorithms and can achieve the highest percentage of floor determination accuracy in all scenarios tested. Specifically, the proposed algorithm can achieve greater than 95% correct floor determination under the scenario in which 40% of RNs failed.

  19. A Game Theoretic Fault Detection Filter

    Science.gov (United States)

    Chung, Walter H.; Speyer, Jason L.

    1995-01-01

    The fault detection process is modelled as a disturbance attenuation problem. The solution to this problem is found via differential game theory, leading to an H(sub infinity) filter which bounds the transmission of all exogenous signals save the fault to be detected. For a general class of linear systems which includes some time-varying systems, it is shown that this transmission bound can be taken to zero by simultaneously bringing the sensor noise weighting to zero. Thus, in the limit, a complete transmission block can he achieved, making the game filter into a fault detection filter. When we specialize this result to time-invariant system, it is found that the detection filter attained in the limit is identical to the well known Beard-Jones Fault Detection Filter. That is, all fault inputs other than the one to be detected (the "nuisance faults") are restricted to an invariant subspace which is unobservable to a projection on the output. For time-invariant systems, it is also shown that in the limit, the order of the state-space and the game filter can be reduced by factoring out the invariant subspace. The result is a lower dimensional filter which can observe only the fault to be detected. A reduced-order filter can also he generated for time-varying systems, though the computational overhead may be intensive. An example given at the end of the paper demonstrates the effectiveness of the filter as a tool for fault detection and identification.

  20. Enhanced Maritime Safety through Diagnosis and Fault Tolerant Control

    DEFF Research Database (Denmark)

    Blanke, Mogens

    2001-01-01

    Faults in steering, navigation instruments or propulsion machinery are serious on a marine vessel since the consequence could be loss of maneuvering ability, and imply risk of damage to vessel personnel or environment. Early diagnosis and accomodation of faults could enhance safety. Fault...... of properties of a falty system; means to determine remedial actions. The paper illustrates the techniques by two marine examples, sensor fusion for automatic steering and control of the main engine....

  1. Fault tolerant architecture for artificial olfactory system

    International Nuclear Information System (INIS)

    Lotfivand, Nasser; Hamidon, Mohd Nizar; Abdolzadeh, Vida

    2015-01-01

    In this paper, to cover and mask the faults that occur in the sensing unit of an artificial olfactory system, a novel architecture is offered. The proposed architecture is able to tolerate failures in the sensors of the array and the faults that occur are masked. The proposed architecture for extracting the correct results from the output of the sensors can provide the quality of service for generated data from the sensor array. The results of various evaluations and analysis proved that the proposed architecture has acceptable performance in comparison with the classic form of the sensor array in gas identification. According to the results, achieving a high odor discrimination based on the suggested architecture is possible. (paper)

  2. Information Based Fault Diagnosis

    DEFF Research Database (Denmark)

    Niemann, Hans Henrik; Poulsen, Niels Kjølstad

    2008-01-01

    Fault detection and isolation, (FDI) of parametric faults in dynamic systems will be considered in this paper. An active fault diagnosis (AFD) approach is applied. The fault diagnosis will be investigated with respect to different information levels from the external inputs to the systems. These ...

  3. Diagnosis of airspeed measurement faults for unmanned aerial vehicles

    DEFF Research Database (Denmark)

    Hansen, Søren; Blanke, Mogens

    2014-01-01

    Airspeed sensor faults are common causes for incidents with unmanned aerial vehicles with pitot tube clogging or icing being the most common causes. Timely diagnosis of such faults or other artifacts in signals from airspeed sensing systems could potentially prevent crashes. This paper employs...

  4. Robust cylinder pressure estimation in heavy-duty diesel engines

    NARCIS (Netherlands)

    Kulah, S.; Forrai, A.; Rentmeester, F.; Donkers, T.; Willems, F.P.T.

    2017-01-01

    The robustness of a new single-cylinder pressure sensor concept is experimentally demonstrated on a six-cylinder heavy-duty diesel engine. Using a single-cylinder pressure sensor and a crank angle sensor, this single-cylinder pressure sensor concept estimates the in-cylinder pressure traces in the

  5. Design of fault tolerant control system for steam generator using

    Energy Technology Data Exchange (ETDEWEB)

    Kim, Myung Ki; Seo, Mi Ro [Korea Electric Power Research Institute, Taejon (Korea, Republic of)

    1998-12-31

    A controller and sensor fault tolerant system for a steam generator is designed with fuzzy logic. A structure of the proposed fault tolerant redundant system is composed of a supervisor and two fuzzy weighting modulators. A supervisor alternatively checks a controller and a sensor induced performances to identify which part, a controller or a sensor, is faulty. In order to analyze controller induced performance both an error and a change in error of the system output are chosen as fuzzy variables. The fuzzy logic for a sensor induced performance uses two variables : a deviation between two sensor outputs and its frequency. Fuzzy weighting modulator generates an output signal compensated for faulty input signal. Simulations show that the proposed fault tolerant control scheme for a steam generator regulates well water level by suppressing fault effect of either controllers or sensors. Therefore through duplicating sensors and controllers with the proposed fault tolerant scheme, both a reliability of a steam generator control and sensor system and that of a power plant increase even more. 2 refs., 9 figs., 1 tab. (Author)

  6. IrisDenseNet: Robust Iris Segmentation Using Densely Connected Fully Convolutional Networks in the Images by Visible Light and Near-Infrared Light Camera Sensors.

    Science.gov (United States)

    Arsalan, Muhammad; Naqvi, Rizwan Ali; Kim, Dong Seop; Nguyen, Phong Ha; Owais, Muhammad; Park, Kang Ryoung

    2018-05-10

    The recent advancements in computer vision have opened new horizons for deploying biometric recognition algorithms in mobile and handheld devices. Similarly, iris recognition is now much needed in unconstraint scenarios with accuracy. These environments make the acquired iris image exhibit occlusion, low resolution, blur, unusual glint, ghost effect, and off-angles. The prevailing segmentation algorithms cannot cope with these constraints. In addition, owing to the unavailability of near-infrared (NIR) light, iris recognition in visible light environment makes the iris segmentation challenging with the noise of visible light. Deep learning with convolutional neural networks (CNN) has brought a considerable breakthrough in various applications. To address the iris segmentation issues in challenging situations by visible light and near-infrared light camera sensors, this paper proposes a densely connected fully convolutional network (IrisDenseNet), which can determine the true iris boundary even with inferior-quality images by using better information gradient flow between the dense blocks. In the experiments conducted, five datasets of visible light and NIR environments were used. For visible light environment, noisy iris challenge evaluation part-II (NICE-II selected from UBIRIS.v2 database) and mobile iris challenge evaluation (MICHE-I) datasets were used. For NIR environment, the institute of automation, Chinese academy of sciences (CASIA) v4.0 interval, CASIA v4.0 distance, and IIT Delhi v1.0 iris datasets were used. Experimental results showed the optimal segmentation of the proposed IrisDenseNet and its excellent performance over existing algorithms for all five datasets.

  7. IrisDenseNet: Robust Iris Segmentation Using Densely Connected Fully Convolutional Networks in the Images by Visible Light and Near-Infrared Light Camera Sensors

    Directory of Open Access Journals (Sweden)

    Muhammad Arsalan

    2018-05-01

    Full Text Available The recent advancements in computer vision have opened new horizons for deploying biometric recognition algorithms in mobile and handheld devices. Similarly, iris recognition is now much needed in unconstraint scenarios with accuracy. These environments make the acquired iris image exhibit occlusion, low resolution, blur, unusual glint, ghost effect, and off-angles. The prevailing segmentation algorithms cannot cope with these constraints. In addition, owing to the unavailability of near-infrared (NIR light, iris recognition in visible light environment makes the iris segmentation challenging with the noise of visible light. Deep learning with convolutional neural networks (CNN has brought a considerable breakthrough in various applications. To address the iris segmentation issues in challenging situations by visible light and near-infrared light camera sensors, this paper proposes a densely connected fully convolutional network (IrisDenseNet, which can determine the true iris boundary even with inferior-quality images by using better information gradient flow between the dense blocks. In the experiments conducted, five datasets of visible light and NIR environments were used. For visible light environment, noisy iris challenge evaluation part-II (NICE-II selected from UBIRIS.v2 database and mobile iris challenge evaluation (MICHE-I datasets were used. For NIR environment, the institute of automation, Chinese academy of sciences (CASIA v4.0 interval, CASIA v4.0 distance, and IIT Delhi v1.0 iris datasets were used. Experimental results showed the optimal segmentation of the proposed IrisDenseNet and its excellent performance over existing algorithms for all five datasets.

  8. On the Use of Sensor Fusion to Reduce the Impact of Rotational and Additive Noise in Human Activity Recognition

    Directory of Open Access Journals (Sweden)

    Ignacio Rojas

    2012-06-01

    Full Text Available The main objective of fusion mechanisms is to increase the individual reliability of the systems through the use of the collectivity knowledge. Moreover, fusion models are also intended to guarantee a certain level of robustness. This is particularly required for problems such as human activity recognition where runtime changes in the sensor setup seriously disturb the reliability of the initial deployed systems. For commonly used recognition systems based on inertial sensors, these changes are primarily characterized as sensor rotations, displacements or faults related to the batteries or calibration. In this work we show the robustness capabilities of a sensor-weighted fusion model when dealing with such disturbances under different circumstances. Using the proposed method, up to 60% outperformance is obtained when a minority of the sensors are artificially rotated or degraded, independent of the level of disturbance (noise imposed. These robustness capabilities also apply for any number of sensors affected by a low to moderate noise level. The presented fusion mechanism compensates the poor performance that otherwise would be obtained when just a single sensor is considered.

  9. Fault Tolerant Position-mooring Control for Offshore Vessels

    DEFF Research Database (Denmark)

    Blanke, Mogens; Nguyen, Trong Dong

    2018-01-01

    Fault-tolerance is crucial to maintain safety in offshore operations. The objective of this paper is to show how systematic analysis and design of fault-tolerance is conducted for a complex automation system, exemplified by thruster assisted Position-mooring. Using redundancy as required....... Functional faults that are only detectable, are rendered isolable through an active isolation approach. Once functional faults are isolated, they are handled by fault accommodation techniques to meet overall control objectives specified by class requirements. The paper illustrates the generic methodology...... by a system to handle faults in mooring lines, sensors or thrusters. Simulations and model basin experiments are carried out to validate the concept for scenarios with single or multiple faults. The results demonstrate that enhanced availability and safety are obtainable with this design approach. While...

  10. Robust data reconciliation with TEMPO

    International Nuclear Information System (INIS)

    Sunde, Svein; Banati, Jozsef

    2004-03-01

    The Halden Project's TEMPO system is devised to meet the increasing challenges facing utilities in performance monitoring and optimisation, among other things due to deregulation and market liberalisation. The data reconciliation mode is an important one in the TEMPO system. Data reconciliation is a method to provide reliable estimates of process states, parameters, the state of equipment and instruments, and efficiency. Data reconciliation follows maximum likelihood principles, and is usually based on a Gaussian error model for the data. Data reconciliation thus estimates the most likely state of the process, given the heat and mass balance and the current instrument readings. If the assumption of normally (Gaussian) distributed errors in the measurements break down, as it will in the case of gross errors (outliers) in the data set, this needs to be detected by the monitoring system. TEMPO does this by computing an overall test statistic subjected to a hypothesis test, resulting in the so-called goodness-of-fit. If the goodness-of-fit takes a too low value, a fault is probably present in the process. Identifying the location of the fault is more difficult, however. With the Gaussian error model this is still possible employing a so-called serial elimination method. The serial elimination method is, however, cumbersome to use and leads to quite lengthy calculations. The present report describes the application of alternative distributions in the maximum likelihood estimation in an attempt to directly identify the location of faulty sensors. For direct identification of sensor faults significant improvements over the method based on a Gaussian error model has been achieved. However, serial elimination still leads to the highest success rate for fault identification. All results were based on Monte Carlo simulations. (Author)

  11. Fault diagnosis methods for district heating substations

    Energy Technology Data Exchange (ETDEWEB)

    Pakanen, J.; Hyvaerinen, J.; Kuismin, J.; Ahonen, M. [VTT Building Technology, Espoo (Finland). Building Physics, Building Services and Fire Technology

    1996-12-31

    A district heating substation is a demanding process for fault diagnosis. The process is nonlinear, load conditions of the district heating network change unpredictably and standard instrumentation is designed only for control and local monitoring purposes, not for automated diagnosis. Extra instrumentation means additional cost, which is usually not acceptable to consumers. That is why all conventional methods are not applicable in this environment. The paper presents five different approaches to fault diagnosis. While developing the methods, various kinds of pragmatic aspects and robustness had to be considered in order to achieve practical solutions. The presented methods are: classification of faults using performance indexing, static and physical modelling of process equipment, energy balance of the process, interactive fault tree reasoning and statistical tests. The methods are applied to a control valve, a heat excharger, a mud separating device and the whole process. The developed methods are verified in practice using simulation, simulation or field tests. (orig.) (25 refs.)

  12. Summary: beyond fault trees to fault graphs

    International Nuclear Information System (INIS)

    Alesso, H.P.; Prassinos, P.; Smith, C.F.

    1984-09-01

    Fault Graphs are the natural evolutionary step over a traditional fault-tree model. A Fault Graph is a failure-oriented directed graph with logic connectives that allows cycles. We intentionally construct the Fault Graph to trace the piping and instrumentation drawing (P and ID) of the system, but with logical AND and OR conditions added. Then we evaluate the Fault Graph with computer codes based on graph-theoretic methods. Fault Graph computer codes are based on graph concepts, such as path set (a set of nodes traveled on a path from one node to another) and reachability (the complete set of all possible paths between any two nodes). These codes are used to find the cut-sets (any minimal set of component failures that will fail the system) and to evaluate the system reliability

  13. Fault Detection and Isolation for a Supermarket Refrigeration System - Part One

    DEFF Research Database (Denmark)

    Yang, Zhenyu; Rasmussen, Karsten B.; Kieu, Anh T.

    2011-01-01

    Fault Detection and Isolation (FDI) using the Kalman Filter (KF) technique for a supermarket refrigeration system is explored. Four types of sensor fault scenarios, namely drift, offset, freeze and hard-over, are considered for two temperature sensors, and one type of parametric fault scenario, n....... The test results show that the EKF-based FDI method generally performances better and faster than the KF-based method does. However, both methods can not handle the isolation between sensor faults and parametric fault.......Fault Detection and Isolation (FDI) using the Kalman Filter (KF) technique for a supermarket refrigeration system is explored. Four types of sensor fault scenarios, namely drift, offset, freeze and hard-over, are considered for two temperature sensors, and one type of parametric fault scenario...... isolation purpose, a bank of KFs arranged by splitting measurements is constructed for sensor fault isolation, while the Multi-Model Adaptive Estimation (MMAE) method is employed to handle parametric fault isolation. All these approaches are extended and checked by using Extended KF technique afterwards...

  14. Superconducting dc fault current limiter

    International Nuclear Information System (INIS)

    Cointe, Y.

    2007-12-01

    Within the framework of the electric power market liberalization, DC networks have many interests compared to alternative ones, but their protections need to use new systems. Superconducting fault current limiters enable by an overstepping of the critical current to limit the fault current to a preset value, lower than the theoretical short-circuit current. For these applications, coated conductors offer excellent opportunities. We worked on the implementation of these materials and built a test bench. We carried out limiting experiments to estimate the quench homogeneity at various short-circuit parameters. An important point is the temperature measurement by deposited sensors on the ribbon, results are in good correlation with the theoretical models. Improved quench behaviours for temperatures close to the critical temperature have been confirmed. Our results enable to better understand the limitation mechanisms of coated conductors. (author)

  15. Sensor Validation using Bayesian Networks

    Data.gov (United States)

    National Aeronautics and Space Administration — One of NASA’s key mission requirements is robust state estimation. Sensing, using a wide range of sensors and sensor fusion approaches, plays a central role in...

  16. Reset Tree-Based Optical Fault Detection

    Directory of Open Access Journals (Sweden)

    Howon Kim

    2013-05-01

    Full Text Available In this paper, we present a new reset tree-based scheme to protect cryptographic hardware against optical fault injection attacks. As one of the most powerful invasive attacks on cryptographic hardware, optical fault attacks cause semiconductors to misbehave by injecting high-energy light into a decapped integrated circuit. The contaminated result from the affected chip is then used to reveal secret information, such as a key, from the cryptographic hardware. Since the advent of such attacks, various countermeasures have been proposed. Although most of these countermeasures are strong, there is still the possibility of attack. In this paper, we present a novel optical fault detection scheme that utilizes the buffers on a circuit’s reset signal tree as a fault detection sensor. To evaluate our proposal, we model radiation-induced currents into circuit components and perform a SPICE simulation. The proposed scheme is expected to be used as a supplemental security tool.

  17. Fault tree handbook

    International Nuclear Information System (INIS)

    Haasl, D.F.; Roberts, N.H.; Vesely, W.E.; Goldberg, F.F.

    1981-01-01

    This handbook describes a methodology for reliability analysis of complex systems such as those which comprise the engineered safety features of nuclear power generating stations. After an initial overview of the available system analysis approaches, the handbook focuses on a description of the deductive method known as fault tree analysis. The following aspects of fault tree analysis are covered: basic concepts for fault tree analysis; basic elements of a fault tree; fault tree construction; probability, statistics, and Boolean algebra for the fault tree analyst; qualitative and quantitative fault tree evaluation techniques; and computer codes for fault tree evaluation. Also discussed are several example problems illustrating the basic concepts of fault tree construction and evaluation

  18. Fault Tolerant Feedback Control

    DEFF Research Database (Denmark)

    Stoustrup, Jakob; Niemann, H.

    2001-01-01

    An architecture for fault tolerant feedback controllers based on the Youla parameterization is suggested. It is shown that the Youla parameterization will give a residual vector directly in connection with the fault diagnosis part of the fault tolerant feedback controller. It turns out...... that there is a separation be-tween the feedback controller and the fault tolerant part. The closed loop feedback properties are handled by the nominal feedback controller and the fault tolerant part is handled by the design of the Youla parameter. The design of the fault tolerant part will not affect the design...... of the nominal feedback con-troller....

  19. Active fault tolerant control of piecewise affine systems with reference tracking and input constraints

    DEFF Research Database (Denmark)

    Gholami, M.; Cocquempot, V.; Schiøler, H.

    2014-01-01

    An active fault tolerant control (AFTC) method is proposed for discrete-time piecewise affine (PWA) systems. Only actuator faults are considered. The AFTC framework contains a supervisory scheme, which selects a suitable controller in a set of controllers such that the stability and an acceptable...... performance of the faulty system are held. The design of the supervisory scheme is not considered here. The set of controllers is composed of a normal controller for the fault-free case, an active fault detection and isolation controller for isolation and identification of the faults, and a set of passive...... fault tolerant controllers (PFTCs) modules designed to be robust against a set of actuator faults. In this research, the piecewise nonlinear model is approximated by a PWA system. The PFTCs are state feedback laws. Each one is robust against a fixed set of actuator faults and is able to track...

  20. Fault Tolerant Control for Civil Structures Based on LMI Approach

    Directory of Open Access Journals (Sweden)

    Chunxu Qu

    2013-01-01

    Full Text Available The control system may lose the performance to suppress the structural vibration due to the faults in sensors or actuators. This paper designs the filter to perform the fault detection and isolation (FDI and then reforms the control strategy to achieve the fault tolerant control (FTC. The dynamic equation of the structure with active mass damper (AMD is first formulated. Then, an estimated system is built to transform the FDI filter design problem to the static gain optimization problem. The gain is designed to minimize the gap between the estimated system and the practical system, which can be calculated by linear matrix inequality (LMI approach. The FDI filter is finally used to isolate the sensor faults and reform the FTC strategy. The efficiency of FDI and FTC is validated by the numerical simulation of a three-story structure with AMD system with the consideration of sensor faults. The results show that the proposed FDI filter can detect the sensor faults and FTC controller can effectively tolerate the faults and suppress the structural vibration.

  1. Automatic identification of otological drilling faults: an intelligent recognition algorithm.

    Science.gov (United States)

    Cao, Tianyang; Li, Xisheng; Gao, Zhiqiang; Feng, Guodong; Shen, Peng

    2010-06-01

    This article presents an intelligent recognition algorithm that can recognize milling states of the otological drill by fusing multi-sensor information. An otological drill was modified by the addition of sensors. The algorithm was designed according to features of the milling process and is composed of a characteristic curve, an adaptive filter and a rule base. The characteristic curve can weaken the impact of the unstable normal milling process and reserve the features of drilling faults. The adaptive filter is capable of suppressing interference in the characteristic curve by fusing multi-sensor information. The rule base can identify drilling faults through the filtering result data. The experiments were repeated on fresh porcine scapulas, including normal milling and two drilling faults. The algorithm has high rates of identification. This study shows that the intelligent recognition algorithm can identify drilling faults under interference conditions. (c) 2010 John Wiley & Sons, Ltd.

  2. Design of fault simulator

    Energy Technology Data Exchange (ETDEWEB)

    Gabbar, Hossam A. [Faculty of Energy Systems and Nuclear Science, University of Ontario Institute of Technology (UOIT), Ontario, L1H 7K4 (Canada)], E-mail: hossam.gabbar@uoit.ca; Sayed, Hanaa E.; Osunleke, Ajiboye S. [Okayama University, Graduate School of Natural Science and Technology, Division of Industrial Innovation Sciences Department of Intelligent Systems Engineering, Okayama 700-8530 (Japan); Masanobu, Hara [AspenTech Japan Co., Ltd., Kojimachi Crystal City 10F, Kojimachi, Chiyoda-ku, Tokyo 102-0083 (Japan)

    2009-08-15

    Fault simulator is proposed to understand and evaluate all possible fault propagation scenarios, which is an essential part of safety design and operation design and support of chemical/production processes. Process models are constructed and integrated with fault models, which are formulated in qualitative manner using fault semantic networks (FSN). Trend analysis techniques are used to map real time and simulation quantitative data into qualitative fault models for better decision support and tuning of FSN. The design of the proposed fault simulator is described and applied on experimental plant (G-Plant) to diagnose several fault scenarios. The proposed fault simulator will enable industrial plants to specify and validate safety requirements as part of safety system design as well as to support recovery and shutdown operation and disaster management.

  3. Iowa Bedrock Faults

    Data.gov (United States)

    Iowa State University GIS Support and Research Facility — This fault coverage locates and identifies all currently known/interpreted fault zones in Iowa, that demonstrate offset of geologic units in exposure or subsurface...

  4. Layered Fault Management Architecture

    National Research Council Canada - National Science Library

    Sztipanovits, Janos

    2004-01-01

    ... UAVs or Organic Air Vehicles. The approach of this effort was to analyze fault management requirements of formation flight for fleets of UAVs, and develop a layered fault management architecture which demonstrates significant...

  5. Fault detection and isolation in systems with parametric faults

    DEFF Research Database (Denmark)

    Stoustrup, Jakob; Niemann, Hans Henrik

    1999-01-01

    The problem of fault detection and isolation of parametric faults is considered in this paper. A fault detection problem based on parametric faults are associated with internal parameter variations in the dynamical system. A fault detection and isolation method for parametric faults is formulated...

  6. Smart intimation and location of faults in distribution system

    Science.gov (United States)

    Hari Krishna, K.; Srinivasa Rao, B.

    2018-04-01

    Location of faults in the distribution system is one of the most complicated problems that we are facing today. Identification of fault location and severity of fault within a short time is required to provide continuous power supply but fault identification and information transfer to the operator is the biggest challenge in the distribution network. This paper proposes a fault location method in the distribution system based on Arduino nano and GSM module with flame sensor. The main idea is to locate the fault in the distribution transformer by sensing the arc coming out from the fuse element. The biggest challenge in the distribution network is to identify the location and the severity of faults under different conditions. Well operated transmission and distribution systems will play a key role for uninterrupted power supply. Whenever fault occurs in the distribution system the time taken to locate and eliminate the fault has to be reduced. The proposed design was achieved with flame sensor and GSM module. Under faulty condition, the system will automatically send an alert message to the operator in the distribution system, about the abnormal conditions near the transformer, site code and its exact location for possible power restoration.

  7. Fault tolerant computing systems

    International Nuclear Information System (INIS)

    Randell, B.

    1981-01-01

    Fault tolerance involves the provision of strategies for error detection damage assessment, fault treatment and error recovery. A survey is given of the different sorts of strategies used in highly reliable computing systems, together with an outline of recent research on the problems of providing fault tolerance in parallel and distributed computing systems. (orig.)

  8. Fault zone hydrogeology

    Science.gov (United States)

    Bense, V. F.; Gleeson, T.; Loveless, S. E.; Bour, O.; Scibek, J.

    2013-12-01

    Deformation along faults in the shallow crust (research effort of structural geologists and hydrogeologists. However, we find that these disciplines often use different methods with little interaction between them. In this review, we document the current multi-disciplinary understanding of fault zone hydrogeology. We discuss surface- and subsurface observations from diverse rock types from unlithified and lithified clastic sediments through to carbonate, crystalline, and volcanic rocks. For each rock type, we evaluate geological deformation mechanisms, hydrogeologic observations and conceptual models of fault zone hydrogeology. Outcrop observations indicate that fault zones commonly have a permeability structure suggesting they should act as complex conduit-barrier systems in which along-fault flow is encouraged and across-fault flow is impeded. Hydrogeological observations of fault zones reported in the literature show a broad qualitative agreement with outcrop-based conceptual models of fault zone hydrogeology. Nevertheless, the specific impact of a particular fault permeability structure on fault zone hydrogeology can only be assessed when the hydrogeological context of the fault zone is considered and not from outcrop observations alone. To gain a more integrated, comprehensive understanding of fault zone hydrogeology, we foresee numerous synergistic opportunities and challenges for the discipline of structural geology and hydrogeology to co-evolve and address remaining challenges by co-locating study areas, sharing approaches and fusing data, developing conceptual models from hydrogeologic data, numerical modeling, and training interdisciplinary scientists.

  9. Performance based fault diagnosis

    DEFF Research Database (Denmark)

    Niemann, Hans Henrik

    2002-01-01

    Different aspects of fault detection and fault isolation in closed-loop systems are considered. It is shown that using the standard setup known from feedback control, it is possible to formulate fault diagnosis problems based on a performance index in this general standard setup. It is also shown...

  10. Fuzzy delay model based fault simulator for crosstalk delay fault test ...

    Indian Academy of Sciences (India)

    In this paper, a fuzzy delay model based crosstalk delay fault simulator is proposed. As design .... To find the quality of non-robust tests, a fuzzy delay ..... Dubois D and Prade H 1989 Processing Fuzzy temporal knowledge. IEEE Transactions ...

  11. Fault-Tolerant Vision for Vehicle Guidance in Agriculture

    DEFF Research Database (Denmark)

    Blas, Morten Rufus

    , and aiding sensors such as GPS provide means to detect and isolate single faults in the system. In addition, learning is employed to adapt the system to variational changes in the natural environment. 3D vision is enhanced by learning texture and color information. Intensity gradients on small neighborhoods...... dropout of 3D vision, faults in classification, or other defects, redundant information should be utilized. Such information can be used to diagnose faulty behavior and to temporarily continue operation with a reduced set of sensors when faults or artifacts occur. Additional sensors include GPS receivers...... and inertial sensors. To fully utilize the possibilities in 3D vision, the system must also be able to learn and adapt to changing environments. By learning features of the environment new diagnostic relations can be generated by creating redundant feed-forward information about crop location. Also, by mapping...

  12. Methods for Fault Diagnosability Analysis of a Class of Affine Nonlinear Systems

    Directory of Open Access Journals (Sweden)

    Xiafu Peng

    2015-01-01

    Full Text Available The fault diagnosability analysis for a given model, before developing a diagnosis algorithm, can be used to answer questions like “can the fault fi be detected by observed states?” and “can it separate fault fi from fault fj by observed states?” If not, we should redesign the sensor placement. This paper deals with the problem of the evaluation of detectability and separability for the diagnosability analysis of affine nonlinear system. First, we used differential geometry theory to analyze the nonlinear system and proposed new detectability criterion and separability criterion. Second, the related matrix between the faults and outputs of the system and the fault separable matrix are designed for quantitative fault diagnosability calculation and fault separability calculation, respectively. Finally, we illustrate our approach to exemplify how to analyze diagnosability by a certain nonlinear system example, and the experiment results indicate the effectiveness of the fault evaluation methods.

  13. Robust Multi-Agent Sensor Network Systems

    Science.gov (United States)

    2012-05-08

    IFAC World Congress, COEX, South Korea, pages 6–11, 2008. [6] G. Laman . On graphs and rigidity of plane skeletal structures. Journal of Engineering...it is rigid and removing any one of the edges results in a nonrigid graph (Figure 1b). In ( Laman , 1970), it is proved that every rigid graph contains...preserving the global rigidity of graphs and frameworks in the plane. Computational Geometry, 42(6-7), 511–521. Laman , G. (1970). On graphs and rigidity of

  14. Hybrid online sensor error detection and functional redundancy for systems with time-varying parameters.

    Science.gov (United States)

    Feng, Jianyuan; Turksoy, Kamuran; Samadi, Sediqeh; Hajizadeh, Iman; Littlejohn, Elizabeth; Cinar, Ali

    2017-12-01

    Supervision and control systems rely on signals from sensors to receive information to monitor the operation of a system and adjust manipulated variables to achieve the control objective. However, sensor performance is often limited by their working conditions and sensors may also be subjected to interference by other devices. Many different types of sensor errors such as outliers, missing values, drifts and corruption with noise may occur during process operation. A hybrid online sensor error detection and functional redundancy system is developed to detect errors in online signals, and replace erroneous or missing values detected with model-based estimates. The proposed hybrid system relies on two techniques, an outlier-robust Kalman filter (ORKF) and a locally-weighted partial least squares (LW-PLS) regression model, which leverage the advantages of automatic measurement error elimination with ORKF and data-driven prediction with LW-PLS. The system includes a nominal angle analysis (NAA) method to distinguish between signal faults and large changes in sensor values caused by real dynamic changes in process operation. The performance of the system is illustrated with clinical data continuous glucose monitoring (CGM) sensors from people with type 1 diabetes. More than 50,000 CGM sensor errors were added to original CGM signals from 25 clinical experiments, then the performance of error detection and functional redundancy algorithms were analyzed. The results indicate that the proposed system can successfully detect most of the erroneous signals and substitute them with reasonable estimated values computed by functional redundancy system.

  15. The mechanics of fault-bend folding and tear-fault systems in the Niger Delta

    Science.gov (United States)

    Benesh, Nathan Philip

    This dissertation investigates the mechanics of fault-bend folding using the discrete element method (DEM) and explores the nature of tear-fault systems in the deep-water Niger Delta fold-and-thrust belt. In Chapter 1, we employ the DEM to investigate the development of growth structures in anticlinal fault-bend folds. This work was inspired by observations that growth strata in active folds show a pronounced upward decrease in bed dip, in contrast to traditional kinematic fault-bend fold models. Our analysis shows that the modeled folds grow largely by parallel folding as specified by the kinematic theory; however, the process of folding over a broad axial surface zone yields a component of fold growth by limb rotation that is consistent with the patterns observed in natural folds. This result has important implications for how growth structures can he used to constrain slip and paleo-earthquake ages on active blind-thrust faults. In Chapter 2, we expand our DEM study to investigate the development of a wider range of fault-bend folds. We examine the influence of mechanical stratigraphy and quantitatively compare our models with the relationships between fold and fault shape prescribed by the kinematic theory. While the synclinal fault-bend models closely match the kinematic theory, the modeled anticlinal fault-bend folds show robust behavior that is distinct from the kinematic theory. Specifically, we observe that modeled structures maintain a linear relationship between fold shape (gamma) and fault-horizon cutoff angle (theta), rather than expressing the non-linear relationship with two distinct modes of anticlinal folding that is prescribed by the kinematic theory. These observations lead to a revised quantitative relationship for fault-bend folds that can serve as a useful interpretation tool. Finally, in Chapter 3, we examine the 3D relationships of tear- and thrust-fault systems in the western, deep-water Niger Delta. Using 3D seismic reflection data and new

  16. Chaos Synchronization Based Novel Real-Time Intelligent Fault Diagnosis for Photovoltaic Systems

    Directory of Open Access Journals (Sweden)

    Chin-Tsung Hsieh

    2014-01-01

    Full Text Available The traditional solar photovoltaic fault diagnosis system needs two to three sets of sensing elements to capture fault signals as fault features and many fault diagnosis methods cannot be applied with real time. The fault diagnosis method proposed in this study needs only one set of sensing elements to intercept the fault features of the system, which can be real-time-diagnosed by creating the fault data of only one set of sensors. The aforesaid two points reduce the cost and fault diagnosis time. It can improve the construction of the huge database. This study used Matlab to simulate the faults in the solar photovoltaic system. The maximum power point tracker (MPPT is used to keep a stable power supply to the system when the system has faults. The characteristic signal of system fault voltage is captured and recorded, and the dynamic error of the fault voltage signal is extracted by chaos synchronization. Then, the extension engineering is used to implement the fault diagnosis. Finally, the overall fault diagnosis system only needs to capture the voltage signal of the solar photovoltaic system, and the fault type can be diagnosed instantly.

  17. A Fault Diagnosis Methodology for Gear Pump Based on EEMD and Bayesian Network.

    Science.gov (United States)

    Liu, Zengkai; Liu, Yonghong; Shan, Hongkai; Cai, Baoping; Huang, Qing

    2015-01-01

    This paper proposes a fault diagnosis methodology for a gear pump based on the ensemble empirical mode decomposition (EEMD) method and the Bayesian network. Essentially, the presented scheme is a multi-source information fusion based methodology. Compared with the conventional fault diagnosis with only EEMD, the proposed method is able to take advantage of all useful information besides sensor signals. The presented diagnostic Bayesian network consists of a fault layer, a fault feature layer and a multi-source information layer. Vibration signals from sensor measurement are decomposed by the EEMD method and the energy of intrinsic mode functions (IMFs) are calculated as fault features. These features are added into the fault feature layer in the Bayesian network. The other sources of useful information are added to the information layer. The generalized three-layer Bayesian network can be developed by fully incorporating faults and fault symptoms as well as other useful information such as naked eye inspection and maintenance records. Therefore, diagnostic accuracy and capacity can be improved. The proposed methodology is applied to the fault diagnosis of a gear pump and the structure and parameters of the Bayesian network is established. Compared with artificial neural network and support vector machine classification algorithms, the proposed model has the best diagnostic performance when sensor data is used only. A case study has demonstrated that some information from human observation or system repair records is very helpful to the fault diagnosis. It is effective and efficient in diagnosing faults based on uncertain, incomplete information.

  18. Fault tolerant control for uncertain systems with parametric faults

    DEFF Research Database (Denmark)

    Niemann, Hans Henrik; Poulsen, Niels Kjølstad

    2006-01-01

    A fault tolerant control (FTC) architecture based on active fault diagnosis (AFD) and the YJBK (Youla, Jarb, Bongiorno and Kucera)parameterization is applied in this paper. Based on the FTC architecture, fault tolerant control of uncertain systems with slowly varying parametric faults...... is investigated. Conditions are given for closed-loop stability in case of false alarms or missing fault detection/isolation....

  19. A Concept for fault tolerant controllers

    DEFF Research Database (Denmark)

    Niemann, Hans Henrik; Poulsen, Niels Kjølstad

    2009-01-01

    This paper describe a concept for fault tolerant controllers (FTC) based on the YJBK (after Youla, Jabr, Bongiorno and Kucera) parameterization. This controller architecture will allow to change the controller on-line in the case of faults in the system. In the described FTC concept, a safe mode...... controller is applied as the basic feedback controller. A controller for normal operation with high performance is obtained by including certain YJBK parameters (transfer functions) in the controller. This will allow a fast switch from normal operation to safe mode operation in case of critical faults...... in the system. The described FTC architecture allow the different feedback controllers to apply different sets of sensors and actuators....

  20. Bayesian based design of real-time sensor systems for high-risk indoor contaminants

    Energy Technology Data Exchange (ETDEWEB)

    Sreedharan, Priya [Univ. of California, Berkeley, CA (United States)

    2007-01-01

    The sudden release of toxic contaminants that reach indoor spaces can be hazardousto building occupants. To respond effectively, the contaminant release must be quicklydetected and characterized to determine unobserved parameters, such as release locationand strength. Characterizing the release requires solving an inverse problem. Designinga robust real-time sensor system that solves the inverse problem is challenging becausethe fate and transport of contaminants is complex, sensor information is limited andimperfect, and real-time estimation is computationally constrained.This dissertation uses a system-level approach, based on a Bayes Monte Carloframework, to develop sensor-system design concepts and methods. I describe threeinvestigations that explore complex relationships among sensors, network architecture,interpretation algorithms, and system performance. The investigations use data obtainedfrom tracer gas experiments conducted in a real building. The influence of individual sensor characteristics on the sensor-system performance for binary-type contaminant sensors is analyzed. Performance tradeoffs among sensor accuracy, threshold level and response time are identified; these attributes could not be inferred without a system-level analysis. For example, more accurate but slower sensors are found to outperform less accurate but faster sensors. Secondly, I investigate how the sensor-system performance can be understood in terms of contaminant transport processes and the model representation that is used to solve the inverse problem. The determination of release location and mass are shown to be related to and constrained by transport and mixing time scales. These time scales explain performance differences among different sensor networks. For example, the effect of longer sensor response times is comparably less for releases with longer mixing time scales. The third investigation explores how information fusion from heterogeneous sensors may improve the sensor

  1. Robust and Fault Tolerant Control of CD-players

    DEFF Research Database (Denmark)

    Vidal, Enrique Sanchez

    Several new standards have emerged recently in the area of portable optical data sto-rage media and more are on their way. In addition to the well known Compact Disc(CD), portable optical media now also feature media for video storage (DVDs) and ge-neral data storage media for computer purposes (CD...

  2. HOT Faults", Fault Organization, and the Occurrence of the Largest Earthquakes

    Science.gov (United States)

    Carlson, J. M.; Hillers, G.; Archuleta, R. J.

    2006-12-01

    We apply the concept of "Highly Optimized Tolerance" (HOT) for the investigation of spatio-temporal seismicity evolution, in particular mechanisms associated with largest earthquakes. HOT provides a framework for investigating both qualitative and quantitative features of complex feedback systems that are far from equilibrium and punctuated by rare, catastrophic events. In HOT, robustness trade-offs lead to complexity and power laws in systems that are coupled to evolving environments. HOT was originally inspired by biology and engineering, where systems are internally very highly structured, through biological evolution or deliberate design, and perform in an optimum manner despite fluctuations in their surroundings. Though faults and fault systems are not designed in ways comparable to biological and engineered structures, feedback processes are responsible in a conceptually comparable way for the development, evolution and maintenance of younger fault structures and primary slip surfaces of mature faults, respectively. Hence, in geophysical applications the "optimization" approach is perhaps more aptly replaced by "organization", reflecting the distinction between HOT and random, disorganized configurations, and highlighting the importance of structured interdependencies that evolve via feedback among and between different spatial and temporal scales. Expressed in the terminology of the HOT concept, mature faults represent a configuration optimally organized for the release of strain energy; whereas immature, more heterogeneous fault networks represent intermittent, suboptimal systems that are regularized towards structural simplicity and the ability to generate large earthquakes more easily. We discuss fault structure and associated seismic response pattern within the HOT concept, and outline fundamental differences between this novel interpretation to more orthodox viewpoints like the criticality concept. The discussion is flanked by numerical simulations of a

  3. Working Conditions-Aware Fault Injection Technique

    OpenAIRE

    Alouani , Ihsen; Niar , Smail; Jemai , Mohamed; Kuradi , Fadi; Abid , Mohamed

    2012-01-01

    International audience; With new integration rates, the circuits sensitivity to environmental and working conditions has increased dramatically. Thus, presenting reliable, less consuming energy and error resilient architectures is being one of the major problems to deal with. Besides, evaluating robustness and effectiveness of the proposed architectures is also an urgent need. In this paper, we present an extension of SimpleScalar simulation tool having the ability to inject faults in a given...

  4. Frequency Based Fault Detection in Wind Turbines

    DEFF Research Database (Denmark)

    Odgaard, Peter Fogh; Stoustrup, Jakob

    2014-01-01

    In order to obtain lower cost of energy for wind turbines fault detection and accommodation is important. Expensive condition monitoring systems are often used to monitor the condition of rotating and vibrating system parts. One example is the gearbox in a wind turbine. This system is operated...... in parallel to the control system, using different computers and additional often expensive sensors. In this paper a simple filter based algorithm is proposed to detect changes in a resonance frequency in a system, exemplified with faults resulting in changes in the resonance frequency in the wind turbine...... gearbox. Only the generator speed measurement which is available in even simple wind turbine control systems is used as input. Consequently this proposed scheme does not need additional sensors and computers for monitoring the condition of the wind gearbox. The scheme is evaluated on a wide-spread wind...

  5. A Prognostic Method for Fault Detection in Wind Turbine Drivetrains

    DEFF Research Database (Denmark)

    Nejada, Amir R.; Odgaard, Peter Fogh; Gao, Zhen

    2014-01-01

    In this paper, a prognostic method is presented for fault detection in gears and bearings in wind turbine drivetrains. This method is based on angular velocity measurements from the gearbox input shaft and the output to the generator, using two additional angular velocity sensors on the intermedi......In this paper, a prognostic method is presented for fault detection in gears and bearings in wind turbine drivetrains. This method is based on angular velocity measurements from the gearbox input shaft and the output to the generator, using two additional angular velocity sensors...... bearing faults in three locations: the high-speed shaft stage, the planetary stage and the intermediate-speed shaft stage. Simulations of the faulty and fault-free cases are performed on a gearbox model implemented in multibody dynamic simulation software. The global loads on the gearbox are obtained from...

  6. Fault diagnosis of rolling bearings with recurrent neural network-based autoencoders.

    Science.gov (United States)

    Liu, Han; Zhou, Jianzhong; Zheng, Yang; Jiang, Wei; Zhang, Yuncheng

    2018-04-19

    As the rolling bearings being the key part of rotary machine, its healthy condition is quite important for safety production. Fault diagnosis of rolling bearing has been research focus for the sake of improving the economic efficiency and guaranteeing the operation security. However, the collected signals are mixed with ambient noise during the operation of rotary machine, which brings great challenge to the exact diagnosis results. Using signals collected from multiple sensors can avoid the loss of local information and extract more helpful characteristics. Recurrent Neural Networks (RNN) is a type of artificial neural network which can deal with multiple time sequence data. The capacity of RNN has been proved outstanding for catching time relevance about time sequence data. This paper proposed a novel method for bearing fault diagnosis with RNN in the form of an autoencoder. In this approach, multiple vibration value of the rolling bearings of the next period are predicted from the previous period by means of Gated Recurrent Unit (GRU)-based denoising autoencoder. These GRU-based non-linear predictive denoising autoencoders (GRU-NP-DAEs) are trained with strong generalization ability for each different fault pattern. Then for the given input data, the reconstruction errors between the next period data and the output data generated by different GRU-NP-DAEs are used to detect anomalous conditions and classify fault type. Classic rotating machinery datasets have been employed to testify the effectiveness of the proposed diagnosis method and its preponderance over some state-of-the-art methods. The experiment results indicate that the proposed method achieves satisfactory performance with strong robustness and high classification accuracy. Copyright © 2018 ISA. Published by Elsevier Ltd. All rights reserved.

  7. Fault tolerant system based on IDDQ testing

    Science.gov (United States)

    Guibane, Badi; Hamdi, Belgacem; Mtibaa, Abdellatif; Bensalem, Brahim

    2018-06-01

    Offline test is essential to ensure good manufacturing quality. However, for permanent or transient faults that occur during the use of the integrated circuit in an application, an online integrated test is needed as well. This procedure should ensure the detection and possibly the correction or the masking of these faults. This requirement of self-correction is sometimes necessary, especially in critical applications that require high security such as automotive, space or biomedical applications. We propose a fault-tolerant design for analogue and mixed-signal design complementary metal oxide (CMOS) circuits based on the quiescent current supply (IDDQ) testing. A defect can cause an increase in current consumption. IDDQ testing technique is based on the measurement of power supply current to distinguish between functional and failed circuits. The technique has been an effective testing method for detecting physical defects such as gate-oxide shorts, floating gates (open) and bridging defects in CMOS integrated circuits. An architecture called BICS (Built In Current Sensor) is used for monitoring the supply current (IDDQ) of the connected integrated circuit. If the measured current is not within the normal range, a defect is signalled and the system switches connection from the defective to a functional integrated circuit. The fault-tolerant technique is composed essentially by a double mirror built-in current sensor, allowing the detection of abnormal current consumption and blocks allowing the connection to redundant circuits, if a defect occurs. Spices simulations are performed to valid the proposed design.

  8. Applying Parametric Fault Detection to a Mechanical System

    DEFF Research Database (Denmark)

    Felício, P.; Stoustrup, Jakob; Niemann, H.

    2002-01-01

    A way of doing parametric fault detection is described. It is based on the representation of parameter changes as linear fractional transformations (lfts). We describe a model with parametric uncertainty. Then a stabilizing controller is chosen and its robustness properties are studied via mu. Th....... The parameter changes (faults) are estimated based on estimates of the fictitious signals that enter the delta block in the lft. These signal estimators are designed by H-infinity techniques. The chosen example is an inverted pendulum....

  9. Quantitative Diagnosis of Rotor Vibration Fault Using Process Power Spectrum Entropy and Support Vector Machine Method

    Directory of Open Access Journals (Sweden)

    Cheng-Wei Fei

    2014-01-01

    Full Text Available To improve the diagnosis capacity of rotor vibration fault in stochastic process, an effective fault diagnosis method (named Process Power Spectrum Entropy (PPSE and Support Vector Machine (SVM (PPSE-SVM, for short method was proposed. The fault diagnosis model of PPSE-SVM was established by fusing PPSE method and SVM theory. Based on the simulation experiment of rotor vibration fault, process data for four typical vibration faults (rotor imbalance, shaft misalignment, rotor-stator rubbing, and pedestal looseness were collected under multipoint (multiple channels and multispeed. By using PPSE method, the PPSE values of these data were extracted as fault feature vectors to establish the SVM model of rotor vibration fault diagnosis. From rotor vibration fault diagnosis, the results demonstrate that the proposed method possesses high precision, good learning ability, good generalization ability, and strong fault-tolerant ability (robustness in four aspects of distinguishing fault types, fault severity, fault location, and noise immunity of rotor stochastic vibration. This paper presents a novel method (PPSE-SVM for rotor vibration fault diagnosis and real-time vibration monitoring. The presented effort is promising to improve the fault diagnosis precision of rotating machinery like gas turbine.

  10. Robust FDI for a Class of Nonlinear Networked Systems with ROQs

    Directory of Open Access Journals (Sweden)

    An-quan Sun

    2014-01-01

    Full Text Available This paper considers the robust fault detection and isolation (FDI problem for a class of nonlinear networked systems (NSs with randomly occurring quantisations (ROQs. After vector augmentation, Lyapunov function is introduced to ensure the asymptotically mean-square stability of fault detection system. By transforming the quantisation effects into sector-bounded parameter uncertainties, sufficient conditions ensuring the existence of fault detection filter are proposed, which can reduce the difference between output residuals and fault signals as small as possible under H∞ framework. Finally, an example linearized from a vehicle system is introduced to show the efficiency of the proposed fault detection filter.

  11. Methods for robustness programming

    NARCIS (Netherlands)

    Olieman, N.J.

    2008-01-01

    Robustness of an object is defined as the probability that an object will have properties as required. Robustness Programming (RP) is a mathematical approach for Robustness estimation and Robustness optimisation. An example in the context of designing a food product, is finding the best composition

  12. Robustness in laying hens

    NARCIS (Netherlands)

    Star, L.

    2008-01-01

    The aim of the project ‘The genetics of robustness in laying hens’ was to investigate nature and regulation of robustness in laying hens under sub-optimal conditions and the possibility to increase robustness by using animal breeding without loss of production. At the start of the project, a robust

  13. Fault diagnosis for agitator driving system in a high temperature reduction reactor

    Energy Technology Data Exchange (ETDEWEB)

    Park, Gee Young; Hong, Dong Hee; Jung, Jae Hoo; Kim, Young Hwan; Jin, Jae Hyun; Yoon, Ji Sup [KAERI, Taejon (Korea, Republic of)

    2003-10-01

    In this paper, a preliminary study for development of a fault diagnosis is presented for monitoring and diagnosing faults in the agitator driving system of a high temperature reduction reactor. In order to identify a fault occurrence and classify the fault cause, vibration signals measured by accelerometers on the outer shroud of the agitator driving system are firstly decomposed by Wavelet Transform (WT) and the features corresponding to each fault type are extracted. For the diagnosis, the fuzzy ARTMAP is employed and thereby, based on the features extracted from the WT, the robust fault classifier can be implemented with a very short training time - a single training epoch and a single learning iteration is sufficient for training the fault classifier. The test results demonstrate satisfactory classification for the faults pre-categorized from considerations of possible occurrence during experiments on a small-scale reduction reactor.

  14. A New Method of Improving Transformer Restricted Earth Fault Protection

    Directory of Open Access Journals (Sweden)

    KRSTIVOJEVIC, J. P.

    2014-08-01

    Full Text Available A new method of avoiding malfunctioning of the transformer restricted earth fault (REF protection is presented. Application of the proposed method would eliminate unnecessary operation of REF protection in the cases of faults outside protected zone of a transformer or a magnetizing inrush accompanied by current transformer (CT saturation. On the basis of laboratory measurements and simulations the paper presents a detailed performance assessment of the proposed method which is based on digital phase comparator. The obtained results show that the new method was stable and precise for all tested faults and that its application would allow making a clear and precise difference between an internal fault and: (i external fault or (ii magnetizing inrush. The proposed method would improve performance of REF protection and reduce probability of maloperation due to CT saturation. The new method is robust and characterized by high speed of operation and high reliability and security.

  15. Fault-tolerant Control of a Cyber-physical System

    Science.gov (United States)

    Roxana, Rusu-Both; Eva-Henrietta, Dulf

    2017-10-01

    Cyber-physical systems represent a new emerging field in automatic control. The fault system is a key component, because modern, large scale processes must meet high standards of performance, reliability and safety. Fault propagation in large scale chemical processes can lead to loss of production, energy, raw materials and even environmental hazard. The present paper develops a multi-agent fault-tolerant control architecture using robust fractional order controllers for a (13C) cryogenic separation column cascade. The JADE (Java Agent DEvelopment Framework) platform was used to implement the multi-agent fault tolerant control system while the operational model of the process was implemented in Matlab/SIMULINK environment. MACSimJX (Multiagent Control Using Simulink with Jade Extension) toolbox was used to link the control system and the process model. In order to verify the performance and to prove the feasibility of the proposed control architecture several fault simulation scenarios were performed.

  16. Filtering, control and fault detection with randomly occurring incomplete information

    CERN Document Server

    Dong, Hongli; Gao, Huijun

    2013-01-01

    This book investigates the filtering, control and fault detection problems for several classes of nonlinear systems with randomly occurring incomplete information. It proposes new concepts, including RVNs, ROMDs, ROMTCDs, and ROQEs. The incomplete information under consideration primarily includes missing measurements, time-delays, sensor and actuator saturations, quantization effects and time-varying nonlinearities. The first part of this book focuses on the filtering, control and fault detection problems for several classes of nonlinear stochastic discrete-time systems and

  17. Robust continuous clustering.

    Science.gov (United States)

    Shah, Sohil Atul; Koltun, Vladlen

    2017-09-12

    Clustering is a fundamental procedure in the analysis of scientific data. It is used ubiquitously across the sciences. Despite decades of research, existing clustering algorithms have limited effectiveness in high dimensions and often require tuning parameters for different domains and datasets. We present a clustering algorithm that achieves high accuracy across multiple domains and scales efficiently to high dimensions and large datasets. The presented algorithm optimizes a smooth continuous objective, which is based on robust statistics and allows heavily mixed clusters to be untangled. The continuous nature of the objective also allows clustering to be integrated as a module in end-to-end feature learning pipelines. We demonstrate this by extending the algorithm to perform joint clustering and dimensionality reduction by efficiently optimizing a continuous global objective. The presented approach is evaluated on large datasets of faces, hand-written digits, objects, newswire articles, sensor readings from the Space Shuttle, and protein expression levels. Our method achieves high accuracy across all datasets, outperforming the best prior algorithm by a factor of 3 in average rank.

  18. EKF-based fault detection for guided missiles flight control system

    Science.gov (United States)

    Feng, Gang; Yang, Zhiyong; Liu, Yongjin

    2017-03-01

    The guided missiles flight control system is essential for guidance accuracy and kill probability. It is complicated and fragile. Since actuator faults and sensor faults could seriously affect the security and reliability of the system, fault detection for missiles flight control system is of great significance. This paper deals with the problem of fault detection for the closed-loop nonlinear model of the guided missiles flight control system in the presence of disturbance. First, set up the fault model of flight control system, and then design the residual generation based on the extended Kalman filter (EKF) for the Eulerian-discrete fault model. After that, the Chi-square test was selected for the residual evaluation and the fault detention task for guided missiles closed-loop system was accomplished. Finally, simulation results are provided to illustrate the effectiveness of the approach proposed in the case of elevator fault separately.

  19. Automatic identification of otologic drilling faults: a preliminary report.

    Science.gov (United States)

    Shen, Peng; Feng, Guodong; Cao, Tianyang; Gao, Zhiqiang; Li, Xisheng

    2009-09-01

    A preliminary study was carried out to identify parameters to characterize drilling faults when using an otologic drill under various operating conditions. An otologic drill was modified by the addition of four sensors. Under consistent conditions, the drill was used to simulate three important types of drilling faults and the captured data were analysed to extract characteristic signals. A multisensor information fusion system was designed to fuse the signals and automatically identify the faults. When identifying drilling faults, there was a high degree of repeatability and regularity, with an average recognition rate of >70%. This study shows that the variables measured change in a fashion that allows the identification of particular drilling faults, and that it is feasible to use these data to provide rapid feedback for a control system. Further experiments are being undertaken to implement such a system.

  20. Natural Environment Modeling and Fault-Diagnosis for Automated Agricultural Vehicle

    DEFF Research Database (Denmark)

    Blas, Morten Rufus; Blanke, Mogens

    2008-01-01

    This paper presents results for an automatic navigation system for agricultural vehicles. The system uses stereo-vision, inertial sensors and GPS. Special emphasis has been placed on modeling the natural environment in conjunction with a fault-tolerant navigation system. The results are exemplified...... by an agricultural vehicle following cut grass (swath). It is demonstrated how faults in the system can be detected and diagnosed using state of the art techniques from fault-tolerant literature. Results in performing fault-diagnosis and fault accomodation are presented using real data....

  1. Layered clustering multi-fault diagnosis for hydraulic piston pump

    Science.gov (United States)

    Du, Jun; Wang, Shaoping; Zhang, Haiyan

    2013-04-01

    Efficient diagnosis is very important for improving reliability and performance of aircraft hydraulic piston pump, and it is one of the key technologies in prognostic and health management system. In practice, due to harsh working environment and heavy working loads, multiple faults of an aircraft hydraulic pump may occur simultaneously after long time operations. However, most existing diagnosis methods can only distinguish pump faults that occur individually. Therefore, new method needs to be developed to realize effective diagnosis of simultaneous multiple faults on aircraft hydraulic pump. In this paper, a new method based on the layered clustering algorithm is proposed to diagnose multiple faults of an aircraft hydraulic pump that occur simultaneously. The intensive failure mechanism analyses of the five main types of faults are carried out, and based on these analyses the optimal combination and layout of diagnostic sensors is attained. The three layered diagnosis reasoning engine is designed according to the faults' risk priority number and the characteristics of different fault feature extraction methods. The most serious failures are first distinguished with the individual signal processing. To the desultory faults, i.e., swash plate eccentricity and incremental clearance increases between piston and slipper, the clustering diagnosis algorithm based on the statistical average relative power difference (ARPD) is proposed. By effectively enhancing the fault features of these two faults, the ARPDs calculated from vibration signals are employed to complete the hypothesis testing. The ARPDs of the different faults follow different probability distributions. Compared with the classical fast Fourier transform-based spectrum diagnosis method, the experimental results demonstrate that the proposed algorithm can diagnose the multiple faults, which occur synchronously, with higher precision and reliability.

  2. Fault tree graphics

    International Nuclear Information System (INIS)

    Bass, L.; Wynholds, H.W.; Porterfield, W.R.

    1975-01-01

    Described is an operational system that enables the user, through an intelligent graphics terminal, to construct, modify, analyze, and store fault trees. With this system, complex engineering designs can be analyzed. This paper discusses the system and its capabilities. Included is a brief discussion of fault tree analysis, which represents an aspect of reliability and safety modeling

  3. Fault Detection and Isolation for Wind Turbine Electric Pitch System

    DEFF Research Database (Denmark)

    Zhu, Jiangsheng; Ma, Kuichao; Hajizadeh, Amin

    2017-01-01

    This paper presents a model-based fault detection and isolation scheme applied on electric pitch system of wind turbines. Pitch system is one of the most critical components due to its effect on the operational safety and the dynamics of wind turbines. Faults in this system should be precisely...... detected to prevent failures and decrease downtime. To detect faults of electric pitch actuators and sensors, an extended kalman filter (EKF) based multiple model adaptive estimation (MMAE) designed to estimate the states of the system. The proposed method is demonstrated in case studies. The simulation...

  4. How do normal faults grow?

    OpenAIRE

    Blækkan, Ingvild; Bell, Rebecca; Rotevatn, Atle; Jackson, Christopher; Tvedt, Anette

    2018-01-01

    Faults grow via a sympathetic increase in their displacement and length (isolated fault model), or by rapid length establishment and subsequent displacement accrual (constant-length fault model). To test the significance and applicability of these two models, we use time-series displacement (D) and length (L) data extracted for faults from nature and experiments. We document a range of fault behaviours, from sympathetic D-L fault growth (isolated growth) to sub-vertical D-L growth trajectorie...

  5. Characterization of leaky faults

    International Nuclear Information System (INIS)

    Shan, Chao.

    1990-05-01

    Leaky faults provide a flow path for fluids to move underground. It is very important to characterize such faults in various engineering projects. The purpose of this work is to develop mathematical solutions for this characterization. The flow of water in an aquifer system and the flow of air in the unsaturated fault-rock system were studied. If the leaky fault cuts through two aquifers, characterization of the fault can be achieved by pumping water from one of the aquifers, which are assumed to be horizontal and of uniform thickness. Analytical solutions have been developed for two cases of either a negligibly small or a significantly large drawdown in the unpumped aquifer. Some practical methods for using these solutions are presented. 45 refs., 72 figs., 11 tabs

  6. Ocean Economy and Fault Diagnosis of Electric Submersible Pump applied in Floating platform

    Directory of Open Access Journals (Sweden)

    Panlong Zhang

    2017-04-01

    Full Text Available Ocean economy plays a crucial role in the strengthening maritime safety industry and in the welfare of human beings. Electric Submersible Pumps (ESP have been widely used in floating platforms on the sea to provide oil for machines. However, the ESP fault may lead to ocean environment pollution, on the other hand, a timely fault diagnosis of ESP can improve the ocean economy. In order to meet the strict regulations of the ocean economy and environmental protection, the fault diagnosis of ESP system has become more and more popular in many countries. The vibration mechanical models of typical faults have been able to successfully diagnose the faults of ESP. And different types of sensors are used to monitor the vibration signal for the signal analysis and fault diagnosis in the ESP system. Meanwhile, physical sensors would increase the fault diagnosis challenge. Nowadays, the method of neural network for the fault diagnosis of ESP has been applied widely, which can diagnose the fault of an electric pump accurately based on the large database. To reduce the number of sensors and to avoid the large database, in this paper, algorithms are designed based on feature extraction to diagnose the fault of the ESP system. Simulation results show that the algorithms can achieve the prospective objectives superbly.

  7. Joint High-Order Synchrosqueezing Transform and Multi-Taper Empirical Wavelet Transform for Fault Diagnosis of Wind Turbine Planetary Gearbox under Nonstationary Conditions

    Directory of Open Access Journals (Sweden)

    Yue Hu

    2018-01-01

    Full Text Available Wind turbines usually operate under nonstationary conditions, such as wide-range speed fluctuation and time-varying load. Its critical component, the planetary gearbox, is prone to malfunction or failure, which leads to downtime and repair costs. Therefore, fault diagnosis and condition monitoring for the planetary gearbox in wind turbines is a vital research topic. Meanwhile, the signals measured by the vibration sensors mounted in the gearbox exhibit time-varying and nonstationary features. In this study, a novel time-frequency method based on high-order synchrosqueezing transform (SST and multi-taper empirical wavelet transform (MTEWT is proposed for the wind turbine planetary gearbox under nonstationary conditions. The high-order SST uses accurate instantaneous frequency approximations to obtain a sharper time-frequency representation (TFR. As the acquired signal consists of many components, like the meshing and rotating components of the gear and bearing, the fault component may be masked by other unrelated components. The MTEWT is used to separate the fault feature from the masking components. A variety of experimental signals of the wind turbine planetary gearbox under nonstationary conditions have been analyzed to demonstrate the effectiveness and robustness of the proposed method. Results show that the proposed method is effective in diagnosing both gear and bearing faults.

  8. Joint High-Order Synchrosqueezing Transform and Multi-Taper Empirical Wavelet Transform for Fault Diagnosis of Wind Turbine Planetary Gearbox under Nonstationary Conditions.

    Science.gov (United States)

    Hu, Yue; Tu, Xiaotong; Li, Fucai; Meng, Guang

    2018-01-07

    Wind turbines usually operate under nonstationary conditions, such as wide-range speed fluctuation and time-varying load. Its critical component, the planetary gearbox, is prone to malfunction or failure, which leads to downtime and repair costs. Therefore, fault diagnosis and condition monitoring for the planetary gearbox in wind turbines is a vital research topic. Meanwhile, the signals measured by the vibration sensors mounted in the gearbox exhibit time-varying and nonstationary features. In this study, a novel time-frequency method based on high-order synchrosqueezing transform (SST) and multi-taper empirical wavelet transform (MTEWT) is proposed for the wind turbine planetary gearbox under nonstationary conditions. The high-order SST uses accurate instantaneous frequency approximations to obtain a sharper time-frequency representation (TFR). As the acquired signal consists of many components, like the meshing and rotating components of the gear and bearing, the fault component may be masked by other unrelated components. The MTEWT is used to separate the fault feature from the masking components. A variety of experimental signals of the wind turbine planetary gearbox under nonstationary conditions have been analyzed to demonstrate the effectiveness and robustness of the proposed method. Results show that the proposed method is effective in diagnosing both gear and bearing faults.

  9. Fault and meal detection by redundant continuous glucose monitors and the unscented Kalman filter

    DEFF Research Database (Denmark)

    Mahmoudi, Zeinab; Nørgaard, Kirsten; Poulsen, Niels Kjølstad

    2017-01-01

    The purpose of this study is to develop a method for detecting and compensating the anomalies of continuous glucose monitoring (CGM) sensors as well as detecting unannounced meals. Both features, sensor fault detection/correction and meal detection, are necessary to have a reliable artificial pan...... is corrupted by PISA. The fault isolator can detect 199 out of 200 unannounced meals. The average change in the glucose concentrations between the meals and the detection time points is 46.3 mg/dL.......The purpose of this study is to develop a method for detecting and compensating the anomalies of continuous glucose monitoring (CGM) sensors as well as detecting unannounced meals. Both features, sensor fault detection/correction and meal detection, are necessary to have a reliable artificial...... from the two fault detectors differentiates between a sensor fault and an unannounced meal appearing as an anomaly in the CGM data. If the fault isolator indicates a sensor fault, a method based on the covariance matching technique tunes the covariance of the measurement noise associated...

  10. Modeling, Monitoring and Fault Diagnosis of Spacecraft Air Contaminants

    Science.gov (United States)

    Ramirez, W. Fred; Skliar, Mikhail; Narayan, Anand; Morgenthaler, George W.; Smith, Gerald J.

    1998-01-01

    Control of air contaminants is a crucial factor in the safety considerations of crewed space flight. Indoor air quality needs to be closely monitored during long range missions such as a Mars mission, and also on large complex space structures such as the International Space Station. This work mainly pertains to the detection and simulation of air contaminants in the space station, though much of the work is easily extended to buildings, and issues of ventilation systems. Here we propose a method with which to track the presence of contaminants using an accurate physical model, and also develop a robust procedure that would raise alarms when certain tolerance levels are exceeded. A part of this research concerns the modeling of air flow inside a spacecraft, and the consequent dispersal pattern of contaminants. Our objective is to also monitor the contaminants on-line, so we develop a state estimation procedure that makes use of the measurements from a sensor system and determines an optimal estimate of the contamination in the system as a function of time and space. The real-time optimal estimates in turn are used to detect faults in the system and also offer diagnoses as to their sources. This work is concerned with the monitoring of air contaminants aboard future generation spacecraft and seeks to satisfy NASA's requirements as outlined in their Strategic Plan document (Technology Development Requirements, 1996).

  11. Adaptive Observer-Based Fault-Tolerant Control Design for Uncertain Systems

    Directory of Open Access Journals (Sweden)

    Huaming Qian

    2015-01-01

    Full Text Available This study focuses on the design of the robust fault-tolerant control (FTC system based on adaptive observer for uncertain linear time invariant (LTI systems. In order to improve robustness, rapidity, and accuracy of traditional fault estimation algorithm, an adaptive fault estimation algorithm (AFEA using an augmented observer is presented. By utilizing a new fault estimator model, an improved AFEA based on linear matrix inequality (LMI technique is proposed to increase the performance. Furthermore, an observer-based state feedback fault-tolerant control strategy is designed, which guarantees the stability and performance of the faulty system. Moreover, the adaptive observer and the fault-tolerant controller are designed separately, whose performance can be considered, respectively. Finally, simulation results of an aircraft application are presented to illustrate the effectiveness of the proposed design methods.

  12. Fault Management Metrics

    Science.gov (United States)

    Johnson, Stephen B.; Ghoshal, Sudipto; Haste, Deepak; Moore, Craig

    2017-01-01

    This paper describes the theory and considerations in the application of metrics to measure the effectiveness of fault management. Fault management refers here to the operational aspect of system health management, and as such is considered as a meta-control loop that operates to preserve or maximize the system's ability to achieve its goals in the face of current or prospective failure. As a suite of control loops, the metrics to estimate and measure the effectiveness of fault management are similar to those of classical control loops in being divided into two major classes: state estimation, and state control. State estimation metrics can be classified into lower-level subdivisions for detection coverage, detection effectiveness, fault isolation and fault identification (diagnostics), and failure prognosis. State control metrics can be classified into response determination effectiveness and response effectiveness. These metrics are applied to each and every fault management control loop in the system, for each failure to which they apply, and probabilistically summed to determine the effectiveness of these fault management control loops to preserve the relevant system goals that they are intended to protect.

  13. Fault-tolerant Control of Inverter-fed Induction Motor Drives

    DEFF Research Database (Denmark)

    Thybo, C.

    . A description of the different frequency converter components, including models of the inverter, sensors and controllers was given, followed by a fault mode and effect analysis, which points out the potential fault modes of the design. Among the listed fault modes, two were found to be of particular practical...... University, was used as a framework for this work. A short review of the development cycle, including methods for generating and evaluating residuals, was presented. A cost-benefit analysis was proposed, as an extension to the FTC development cycle, to provide a better background for selecting the fault...... bilinear observers. A brief description of threshold- and statistical change detection was included with focus on mean value change detection in a noisy residual. The detection of encoder sensor faults was analysed and three approaches, for encoder fault detection, were proposed. The reference band...

  14. Towards the Robotic “Avatar”: An Extensive Survey of the Cooperation between and within Networked Mobile Sensors

    Directory of Open Access Journals (Sweden)

    Aydan M. Erkmen

    2010-09-01

    Full Text Available Cooperation between networked mobile sensors, wearable and sycophant sensor networks with parasitically sticking agents, and also having human beings involved in the loop is the “Avatarization” within the robotic research community, where all networks are connected and where you can connect/disconnect at any time to acquire data from a vast unstructured world. This paper extensively surveys the networked robotic foundations of this robotic biological “Avatar” that awaits us in the future. Cooperation between networked mobile sensors as well as cooperation of nodes within a network are becoming more robust, fault tolerant and enable adaptation of the networks to changing environment conditions. In this paper, we survey and comparatively discuss the current state of networked robotics via their critical application areas and their design characteristics. We conclude by discussing future challenges.

  15. Fault isolability conditions for linear systems with additive faults

    DEFF Research Database (Denmark)

    Niemann, Hans Henrik; Stoustrup, Jakob

    2006-01-01

    In this paper, we shall show that an unlimited number of additive single faults can be isolated under mild conditions if a general isolation scheme is applied. Multiple faults are also covered. The approach is algebraic and is based on a set representation of faults, where all faults within a set...

  16. Transposing an active fault database into a seismic hazard fault model for nuclear facilities. Pt. 1. Building a database of potentially active faults (BDFA) for metropolitan France

    Energy Technology Data Exchange (ETDEWEB)

    Jomard, Herve; Cushing, Edward Marc; Baize, Stephane; Chartier, Thomas [IRSN - Institute of Radiological Protection and Nuclear Safety, Fontenay-aux-Roses (France); Palumbo, Luigi; David, Claire [Neodyme, Joue les Tours (France)

    2017-07-01

    The French Institute for Radiation Protection and Nuclear Safety (IRSN), with the support of the Ministry of Environment, compiled a database (BDFA) to define and characterize known potentially active faults of metropolitan France. The general structure of BDFA is presented in this paper. BDFA reports to date 136 faults and represents a first step toward the implementation of seismic source models that would be used for both deterministic and probabilistic seismic hazard calculations. A robustness index was introduced, highlighting that less than 15% of the database is controlled by reasonably complete data sets. An example of transposing BDFA into a fault source model for PSHA (probabilistic seismic hazard analysis) calculation is presented for the Upper Rhine Graben (eastern France) and exploited in the companion paper (Chartier et al., 2017, hereafter Part 2) in order to illustrate ongoing challenges for probabilistic fault-based seismic hazard calculations.

  17. Perceptual Robust Design

    DEFF Research Database (Denmark)

    Pedersen, Søren Nygaard

    The research presented in this PhD thesis has focused on a perceptual approach to robust design. The results of the research and the original contribution to knowledge is a preliminary framework for understanding, positioning, and applying perceptual robust design. Product quality is a topic...... been presented. Therefore, this study set out to contribute to the understanding and application of perceptual robust design. To achieve this, a state-of-the-art and current practice review was performed. From the review two main research problems were identified. Firstly, a lack of tools...... for perceptual robustness was found to overlap with the optimum for functional robustness and at most approximately 2.2% out of the 14.74% could be ascribed solely to the perceptual robustness optimisation. In conclusion, the thesis have offered a new perspective on robust design by merging robust design...

  18. Fault Analysis in Cryptography

    CERN Document Server

    Joye, Marc

    2012-01-01

    In the 1970s researchers noticed that radioactive particles produced by elements naturally present in packaging material could cause bits to flip in sensitive areas of electronic chips. Research into the effect of cosmic rays on semiconductors, an area of particular interest in the aerospace industry, led to methods of hardening electronic devices designed for harsh environments. Ultimately various mechanisms for fault creation and propagation were discovered, and in particular it was noted that many cryptographic algorithms succumb to so-called fault attacks. Preventing fault attacks without

  19. Development of a fault test experimental facility model using Matlab

    Energy Technology Data Exchange (ETDEWEB)

    Pereira, Iraci Martinez; Moraes, Davi Almeida, E-mail: martinez@ipen.br, E-mail: dmoraes@dk8.com.br [Instituto de Pesquisas Energeticas e Nucleares (IPEN/CNEN-SP), Sao Paulo, SP (Brazil)

    2015-07-01

    The Fault Test Experimental Facility was developed to simulate a PWR nuclear power plant and is instrumented with temperature, level and pressure sensors. The Fault Test Experimental Facility can be operated to generate normal and fault data, and these failures can be added initially small, and their magnitude being increasing gradually. This work presents the Fault Test Experimental Facility model developed using the Matlab GUIDE (Graphical User Interface Development Environment) toolbox that consists of a set of functions designed to create interfaces in an easy and fast way. The system model is based on the mass and energy inventory balance equations. Physical as well as operational aspects are taken into consideration. The interface layout looks like a process flowchart and the user can set the input variables. Besides the normal operation conditions, there is the possibility to choose a faulty variable from a list. The program also allows the user to set the noise level for the input variables. Using the model, data were generated for different operational conditions, both under normal and fault conditions with different noise levels added to the input variables. Data generated by the model will be compared with Fault Test Experimental Facility data. The Fault Test Experimental Facility theoretical model results will be used for the development of a Monitoring and Fault Detection System. (author)

  20. Development of a fault test experimental facility model using Matlab

    International Nuclear Information System (INIS)

    Pereira, Iraci Martinez; Moraes, Davi Almeida

    2015-01-01

    The Fault Test Experimental Facility was developed to simulate a PWR nuclear power plant and is instrumented with temperature, level and pressure sensors. The Fault Test Experimental Facility can be operated to generate normal and fault data, and these failures can be added initially small, and their magnitude being increasing gradually. This work presents the Fault Test Experimental Facility model developed using the Matlab GUIDE (Graphical User Interface Development Environment) toolbox that consists of a set of functions designed to create interfaces in an easy and fast way. The system model is based on the mass and energy inventory balance equations. Physical as well as operational aspects are taken into consideration. The interface layout looks like a process flowchart and the user can set the input variables. Besides the normal operation conditions, there is the possibility to choose a faulty variable from a list. The program also allows the user to set the noise level for the input variables. Using the model, data were generated for different operational conditions, both under normal and fault conditions with different noise levels added to the input variables. Data generated by the model will be compared with Fault Test Experimental Facility data. The Fault Test Experimental Facility theoretical model results will be used for the development of a Monitoring and Fault Detection System. (author)

  1. Robustizing Circuit Optimization using Huber Functions

    DEFF Research Database (Denmark)

    Bandler, John W.; Biernacki, Radek M.; Chen, Steve H.

    1993-01-01

    The authors introduce a novel approach to 'robustizing' microwave circuit optimization using Huber functions, both two-sided and one-sided. They compare Huber optimization with l/sub 1/, l/sub 2/, and minimax methods in the presence of faults, large and small measurement errors, bad starting poin......, a preliminary optimization by selecting a small number of dominant variables. It is demonstrated, through multiplexer optimization, that the one-sided Huber function can be more effective and efficient than minimax in overcoming a bad starting point.......The authors introduce a novel approach to 'robustizing' microwave circuit optimization using Huber functions, both two-sided and one-sided. They compare Huber optimization with l/sub 1/, l/sub 2/, and minimax methods in the presence of faults, large and small measurement errors, bad starting points......, and statistical uncertainties. They demonstrate FET statistical modeling, multiplexer optimization, analog fault location, and data fitting. They extend the Huber concept by introducing a 'one-sided' Huber function for large-scale optimization. For large-scale problems, the designer often attempts, by intuition...

  2. Fault tolerant control based on active fault diagnosis

    DEFF Research Database (Denmark)

    Niemann, Hans Henrik

    2005-01-01

    An active fault diagnosis (AFD) method will be considered in this paper in connection with a Fault Tolerant Control (FTC) architecture based on the YJBK parameterization of all stabilizing controllers. The architecture consists of a fault diagnosis (FD) part and a controller reconfiguration (CR......) part. The FTC architecture can be applied for additive faults, parametric faults, and for system structural changes. Only parametric faults will be considered in this paper. The main focus in this paper is on the use of the new approach of active fault diagnosis in connection with FTC. The active fault...... diagnosis approach is based on including an auxiliary input in the system. A fault signature matrix is introduced in connection with AFD, given as the transfer function from the auxiliary input to the residual output. This can be considered as a generalization of the passive fault diagnosis case, where...

  3. Parametric fault estimation based on H∞ optimization in a satellite launch vehicle

    DEFF Research Database (Denmark)

    Soltani, Mohsen; Izadi-Zamanabadi, Roozbeh; Stoustrup, Jakob

    2008-01-01

    Correct diagnosis under harsh environmental conditions is crucial for space vehiclespsila health management systems to avoid possible hazardous situations. Consequently, the diagnosis methods are required to be robust toward these conditions. Design of a parametric fault detector, where the fault...... for the satellite launch vehicle and the results are discussed....

  4. High Order Sliding Mode Control of Doubly-fed Induction Generator under Unbalanced Grid Faults

    DEFF Research Database (Denmark)

    Zhu, Rongwu; Chen, Zhe; Wu, Xiaojie

    2013-01-01

    This paper deals with a doubly-fed induction generator-based (DFIG) wind turbine system under grid fault conditions such as: unbalanced grid voltage, three-phase grid fault, using a high order sliding mode control (SMC). A second order sliding mode controller, which is robust with respect...

  5. Fault Diagnosis for Actuators in a Class of Nonlinear Systems Based on an Adaptive Fault Detection Observer

    Directory of Open Access Journals (Sweden)

    Runxia Guo

    2016-01-01

    Full Text Available The problem of actuators’ fault diagnosis is pursued for a class of nonlinear control systems that are affected by bounded measurement noise and external disturbances. A novel fault diagnosis algorithm has been proposed by combining the idea of adaptive control theory and the approach of fault detection observer. The asymptotical stability of the fault detection observer is guaranteed by setting the adaptive adjusting law of the unknown fault vector. A theoretically rigorous proof of asymptotical stability has been given. Under the condition that random measurement noise generated by the sensors of control systems and external disturbances exist simultaneously, the designed fault diagnosis algorithm is able to successfully give specific estimated values of state variables and failures rather than just giving a simple fault warning. Moreover, the proposed algorithm is very simple and concise and is easy to be applied to practical engineering. Numerical experiments are carried out to evaluate the performance of the fault diagnosis algorithm. Experimental results show that the proposed diagnostic strategy has a satisfactory estimation effect.

  6. Fuzzy associative memories for instrument fault detection

    International Nuclear Information System (INIS)

    Heger, A.S.

    1996-01-01

    A fuzzy logic instrument fault detection scheme is developed for systems having two or three redundant sensors. In the fuzzy logic approach the deviation between each signal pairing is computed and classified into three fuzzy sets. A rule base is created allowing the human perception of the situation to be represented mathematically. Fuzzy associative memories are then applied. Finally, a defuzzification scheme is used to find the centroid location, and hence the signal status. Real-time analyses are carried out to evaluate the instantaneous signal status as well as the long-term results for the sensor set. Instantaneous signal validation results are used to compute a best estimate for the measured state variable. The long-term sensor validation method uses a frequency fuzzy variable to determine the signal condition over a specific period. To corroborate the methodology synthetic data representing various anomalies are analyzed with both the fuzzy logic technique and the parity space approach. (Author)

  7. Sensor Selection and Data Validation for Reliable Integrated System Health Management

    Science.gov (United States)

    Garg, Sanjay; Melcher, Kevin J.

    2008-01-01

    -select iteration, and the final selection analysis. The knowledge base required for productive use of S4 consists of system design information and heritage experience together with a focus on components with health implications. The sensor suite down-selection is an iterative process for identifying a group of sensors that provide good fault detection and isolation for targeted fault scenarios. In the final selection analysis, a statistical evaluation algorithm provides the final robustness test for each down-selected sensor suite. NASA GRC has developed an approach to sensor data qualification that applies empirical relationships, threshold detection techniques, and Bayesian belief theory to a network of sensors related by physics (i.e., analytical redundancy) in order to identify the failure of a given sensor within the network. This data quality validation approach extends the state-of-the-art, from red-lines and reasonableness checks that flag a sensor after it fails, to include analytical redundancy-based methods that can identify a sensor in the process of failing. The focus of this effort is on understanding the proper application of analytical redundancy-based data qualification methods for onboard use in monitoring Upper Stage sensors.

  8. A Fault-tolerable Control Scheme for an Open-frame Underwater Vehicle

    Directory of Open Access Journals (Sweden)

    Huang Hai

    2014-05-01

    Full Text Available Open-frame is one of the major types of structures of Remote Operated Vehicles (ROV because it is easy to place sensors and operations equipment onboard. Firstly, this paper designed a petri-based recurrent neural network (PRFNN to improve the robustness with response to nonlinear characteristics and strong disturbance of an open-frame underwater vehicle. A threshold has been set in the third layer to reduce the amount of calculations and regulate the training process. The whole network convergence is guaranteed with the selection of learning rate parameters. Secondly, a fault tolerance control (FTC scheme is established with the optimal allocation of thrust. Infinity-norm optimization has been combined with 2-norm optimization to construct a bi-criteria primal-dual neural network FTC scheme. In the experiments and simulation, PRFNN outperformed fuzzy neural networks in motion control, while bi-criteria optimization outperformed 2-norm optimization in FTC, which demonstrates that the FTC controller can improve computational efficiency, reduce control errors, and implement fault tolerable thrust allocation.

  9. Aero Engine Component Fault Diagnosis Using Multi-Hidden-Layer Extreme Learning Machine with Optimized Structure

    Directory of Open Access Journals (Sweden)

    Shan Pang

    2016-01-01

    Full Text Available A new aero gas turbine engine gas path component fault diagnosis method based on multi-hidden-layer extreme learning machine with optimized structure (OM-ELM was proposed. OM-ELM employs quantum-behaved particle swarm optimization to automatically obtain the optimal network structure according to both the root mean square error on training data set and the norm of output weights. The proposed method is applied to handwritten recognition data set and a gas turbine engine diagnostic application and is compared with basic ELM, multi-hidden-layer ELM, and two state-of-the-art deep learning algorithms: deep belief network and the stacked denoising autoencoder. Results show that, with optimized network structure, OM-ELM obtains better test accuracy in both applications and is more robust to sensor noise. Meanwhile it controls the model complexity and needs far less hidden nodes than multi-hidden-layer ELM, thus saving computer memory and making it more efficient to implement. All these advantages make our method an effective and reliable tool for engine component fault diagnosis tool.

  10. Quaternary Fault Lines

    Data.gov (United States)

    Department of Homeland Security — This data set contains locations and information on faults and associated folds in the United States that are believed to be sources of M>6 earthquakes during the...

  11. Finite Time Fault Tolerant Control for Robot Manipulators Using Time Delay Estimation and Continuous Nonsingular Fast Terminal Sliding Mode Control.

    Science.gov (United States)

    Van, Mien; Ge, Shuzhi Sam; Ren, Hongliang

    2016-04-28

    In this paper, a novel finite time fault tolerant control (FTC) is proposed for uncertain robot manipulators with actuator faults. First, a finite time passive FTC (PFTC) based on a robust nonsingular fast terminal sliding mode control (NFTSMC) is investigated. Be analyzed for addressing the disadvantages of the PFTC, an AFTC are then investigated by combining NFTSMC with a simple fault diagnosis scheme. In this scheme, an online fault estimation algorithm based on time delay estimation (TDE) is proposed to approximate actuator faults. The estimated fault information is used to detect, isolate, and accommodate the effect of the faults in the system. Then, a robust AFTC law is established by combining the obtained fault information and a robust NFTSMC. Finally, a high-order sliding mode (HOSM) control based on super-twisting algorithm is employed to eliminate the chattering. In comparison to the PFTC and other state-of-the-art approaches, the proposed AFTC scheme possess several advantages such as high precision, strong robustness, no singularity, less chattering, and fast finite-time convergence due to the combined NFTSMC and HOSM control, and requires no prior knowledge of the fault due to TDE-based fault estimation. Finally, simulation results are obtained to verify the effectiveness of the proposed strategy.

  12. Structural Load Analysis of a Wind Turbine under Pitch Actuator and Controller Faults

    International Nuclear Information System (INIS)

    Etemaddar, Mahmoud; Gao, Zhen; Moan, Torgeir

    2014-01-01

    In this paper, we investigate the characteristics of a wind turbine under blade pitch angle and shaft speed sensor faults as well as pitch actuator faults. A land-based NREL 5MW variable speed pitch reg- ulated wind turbine is considered as a reference. The conventional collective blade pitch angle controller strategy with independent pitch actuators control is used for load reduction. The wind turbine class is IEC-BII. The main purpose is to investigate the severity of end effects on structural loads and responses and consequently identify the high-risk components according to the type and amplitude of fault using a servo-aero-elastic simulation code, HAWC2. Both transient and steady state effects of faults are studied. Such information is useful for wind turbine fault detection and identification as well as system reliability analysis. Results show the effects of faults on wind turbine power output and responses. Pitch sensor faults mainly affects the vibration of shaft main bearing, while generator power and aerodynamic thrust are not changed significantly, due to independent pitch actuator control of three blades. Shaft speed sensor faults can seriously affect the generator power and aerodynamic thrust. Pitch actuator faults can result in fully pitching of the blade, and consequently rotor stops due to negative aerodynamic torque

  13. Dynamic Output Feedback Based Active Decentralized Fault-Tolerant Control for Reconfigurable Manipulator with Concurrent Failures

    Directory of Open Access Journals (Sweden)

    Yuanchun Li

    2015-01-01

    Full Text Available The goal of this paper is to describe an active decentralized fault-tolerant control (ADFTC strategy based on dynamic output feedback for reconfigurable manipulators with concurrent actuator and sensor failures. Consider each joint module of the reconfigurable manipulator as a subsystem, and treat the fault as the unknown input of the subsystem. Firstly, by virtue of linear matrix inequality (LMI technique, the decentralized proportional-integral observer (DPIO is designed to estimate and compensate the sensor fault online; hereafter, the compensated system model could be derived. Then, the actuator fault is estimated similarly by another DPIO using LMI as well, and the sufficient condition of the existence of H∞ fault-tolerant controller in the dynamic output feedback is presented for the compensated system model. Furthermore, the dynamic output feedback controller is presented based on the estimation of actuator fault to realize active fault-tolerant control. Finally, two 3-DOF reconfigurable manipulators with different configurations are employed to verify the effectiveness of the proposed scheme in simulation. The main advantages of the proposed scheme lie in that it can handle the concurrent faults act on the actuator and sensor on the same joint module, as well as there is no requirement of fault detection and isolation process; moreover, it is more feasible to the modularity of the reconfigurable manipulator.

  14. Detection and Identification of Loss of Efficiency Faults of Flight Actuators

    Directory of Open Access Journals (Sweden)

    Ossmann Daniel

    2015-03-01

    Full Text Available We propose linear parameter-varying (LPV model-based approaches to the synthesis of robust fault detection and diagnosis (FDD systems for loss of efficiency (LOE faults of flight actuators. The proposed methods are applicable to several types of parametric (or multiplicative LOE faults such as actuator disconnection, surface damage, actuator power loss or stall loads. For the detection of these parametric faults, advanced LPV-model detection techniques are proposed, which implicitly provide fault identification information. Fast detection of intermittent stall loads (seen as nuisances, rather than faults is important in enhancing the performance of various fault detection schemes dealing with large input signals. For this case, a dedicated fast identification algorithm is devised. The developed FDD systems are tested on a nonlinear actuator model which is implemented in a full nonlinear aircraft simulation model. This enables the validation of the FDD system’s detection and identification characteristics under realistic conditions.

  15. Robustness of Structural Systems

    DEFF Research Database (Denmark)

    Canisius, T.D.G.; Sørensen, John Dalsgaard; Baker, J.W.

    2007-01-01

    The importance of robustness as a property of structural systems has been recognised following several structural failures, such as that at Ronan Point in 1968,where the consequenceswere deemed unacceptable relative to the initiating damage. A variety of research efforts in the past decades have...... attempted to quantify aspects of robustness such as redundancy and identify design principles that can improve robustness. This paper outlines the progress of recent work by the Joint Committee on Structural Safety (JCSS) to develop comprehensive guidance on assessing and providing robustness in structural...... systems. Guidance is provided regarding the assessment of robustness in a framework that considers potential hazards to the system, vulnerability of system components, and failure consequences. Several proposed methods for quantifying robustness are reviewed, and guidelines for robust design...

  16. Robust multivariate analysis

    CERN Document Server

    J Olive, David

    2017-01-01

    This text presents methods that are robust to the assumption of a multivariate normal distribution or methods that are robust to certain types of outliers. Instead of using exact theory based on the multivariate normal distribution, the simpler and more applicable large sample theory is given.  The text develops among the first practical robust regression and robust multivariate location and dispersion estimators backed by theory.   The robust techniques  are illustrated for methods such as principal component analysis, canonical correlation analysis, and factor analysis.  A simple way to bootstrap confidence regions is also provided. Much of the research on robust multivariate analysis in this book is being published for the first time. The text is suitable for a first course in Multivariate Statistical Analysis or a first course in Robust Statistics. This graduate text is also useful for people who are familiar with the traditional multivariate topics, but want to know more about handling data sets with...

  17. Fault lubrication during earthquakes.

    Science.gov (United States)

    Di Toro, G; Han, R; Hirose, T; De Paola, N; Nielsen, S; Mizoguchi, K; Ferri, F; Cocco, M; Shimamoto, T

    2011-03-24

    The determination of rock friction at seismic slip rates (about 1 m s(-1)) is of paramount importance in earthquake mechanics, as fault friction controls the stress drop, the mechanical work and the frictional heat generated during slip. Given the difficulty in determining friction by seismological methods, elucidating constraints are derived from experimental studies. Here we review a large set of published and unpublished experiments (∼300) performed in rotary shear apparatus at slip rates of 0.1-2.6 m s(-1). The experiments indicate a significant decrease in friction (of up to one order of magnitude), which we term fault lubrication, both for cohesive (silicate-built, quartz-built and carbonate-built) rocks and non-cohesive rocks (clay-rich, anhydrite, gypsum and dolomite gouges) typical of crustal seismogenic sources. The available mechanical work and the associated temperature rise in the slipping zone trigger a number of physicochemical processes (gelification, decarbonation and dehydration reactions, melting and so on) whose products are responsible for fault lubrication. The similarity between (1) experimental and natural fault products and (2) mechanical work measures resulting from these laboratory experiments and seismological estimates suggests that it is reasonable to extrapolate experimental data to conditions typical of earthquake nucleation depths (7-15 km). It seems that faults are lubricated during earthquakes, irrespective of the fault rock composition and of the specific weakening mechanism involved.

  18. Vipava fault (Slovenia

    Directory of Open Access Journals (Sweden)

    Ladislav Placer

    2008-06-01

    Full Text Available During mapping of the already accomplished Razdrto – Senožeče section of motorway and geologic surveying of construction operations of the trunk road between Razdrto and Vipava in northwestern part of External Dinarides on the southwestern slope of Mt. Nanos, called Rebrnice, a steep NW-SE striking fault was recognized, situated between the Predjama and the Ra{a faults. The fault was named Vipava fault after the Vipava town. An analysis of subrecent gravitational slips at Rebrnice indicates that they were probably associated with the activity of this fault. Unpublished results of a repeated levelling line along the regional road passing across the Vipava fault zone suggest its possible present activity. It would be meaningful to verify this by appropriate geodetic measurements, and to study the actual gravitational slips at Rebrnice. The association between tectonics and gravitational slips in this and in similar extreme cases in the areas of Alps and Dinarides points at the need of complex studying of geologic proceses.

  19. Dedicated Filter for Robust Occupancy Grid Mapping

    Directory of Open Access Journals (Sweden)

    KS Nagla

    2015-03-01

    Full Text Available Sensor based perception of the environment is an emerging area of the mobile robot research where sensors play a pivotal role. For autonomous mobile robots, the fundamental requirement is the convergent of the range information in to high level internal representation. Internal representation in the form of occupancy grid is commonly used in autonomous mobile robots due to its various advantages. There are several sensors such as vision sensor, laser rage finder, and ultrasonic and infrared sensors etc. play roles in mapping. However the sensor information failure, sensor inaccuracies, noise, and slow response are the major causes of an error in the mapping. To improve the reliability of the mobile robot mapping multisensory data fusion is considered as an optimal solution. This paper presents a novel architecture of sensor fusion frame work in which a dedicated filter (DF is proposed to increase the robustness of the occupancy grid for indoor environment. The technique has been experimentally verified for different indoor test environments. The proposed configuration shows improvement in the occupancy grid with the implementation of dedicated filters.

  20. Double Fault Detection of Cone-Shaped Redundant IMUs Using Wavelet Transformation and EPSA

    Directory of Open Access Journals (Sweden)

    Wonhee Lee

    2014-02-01

    Full Text Available A model-free hybrid fault diagnosis technique is proposed to improve the performance of single and double fault detection and isolation. This is a model-free hybrid method which combines the extended parity space approach (EPSA with a multi-resolution signal decomposition by using a discrete wavelet transform (DWT. Conventional EPSA can detect and isolate single and double faults. The performance of fault detection and isolation is influenced by the relative size of noise and fault. In this paper; the DWT helps to cancel the high frequency sensor noise. The proposed technique can improve low fault detection and isolation probability by utilizing the EPSA with DWT. To verify the effectiveness of the proposed fault detection method Monte Carlo numerical simulations are performed for a redundant inertial measurement unit (RIMU.

  1. Fault diagnosis of a Wind Turbine Rotor using a Multi-blade Coordinate Framework

    DEFF Research Database (Denmark)

    Henriksen, Lars Christian; Niemann, Hans Henrik; Poulsen, Niels Kjølstad

    2012-01-01

    Fault diagnosis of a wind turbine rotor is considered. The faults considered are sensor faults and blades mounted with a pitch offset. A fault at a single blade will result in asymmetries in the rotor, which can be applied for fault diagnosis. The diagnosis is derived by using the multiblade...... coordinate (MBC) transformation also known as the Coleman transformation together with active fault diagnosis (AFD). This transforms the setup from rotating to fixed frame coordinates. The rotor speed acts as the auxiliary input for the active diagnosis. The applied method take the varying rotor speed...... into account. Operation at different mean wind speeds is examined and it is discussed how to exploit the findings acquired by the investigation of the various faults....

  2. Double Fault Detection of Cone-Shaped Redundant IMUs Using Wavelet Transformation and EPSA

    Science.gov (United States)

    Lee, Wonhee; Park, Chan Gook

    2014-01-01

    A model-free hybrid fault diagnosis technique is proposed to improve the performance of single and double fault detection and isolation. This is a model-free hybrid method which combines the extended parity space approach (EPSA) with a multi-resolution signal decomposition by using a discrete wavelet transform (DWT). Conventional EPSA can detect and isolate single and double faults. The performance of fault detection and isolation is influenced by the relative size of noise and fault. In this paper; the DWT helps to cancel the high frequency sensor noise. The proposed technique can improve low fault detection and isolation probability by utilizing the EPSA with DWT. To verify the effectiveness of the proposed fault detection method Monte Carlo numerical simulations are performed for a redundant inertial measurement unit (RIMU). PMID:24556675

  3. Fault morphology of the lyo Fault, the Median Tectonic Line Active Fault System

    OpenAIRE

    後藤, 秀昭

    1996-01-01

    In this paper, we investigated the various fault features of the lyo fault and depicted fault lines or detailed topographic map. The results of this paper are summarized as follows; 1) Distinct evidence of the right-lateral movement is continuously discernible along the lyo fault. 2) Active fault traces are remarkably linear suggesting that the angle of fault plane is high. 3) The lyo fault can be divided into four segments by jogs between left-stepping traces. 4) The mean slip rate is 1.3 ~ ...

  4. Fault diagnostics of dynamic system operation using a fault tree based method

    International Nuclear Information System (INIS)

    Hurdle, E.E.; Bartlett, L.M.; Andrews, J.D.

    2009-01-01

    For conventional systems, their availability can be considerably improved by reducing the time taken to restore the system to the working state when faults occur. Fault identification can be a significant proportion of the time taken in the repair process. Having diagnosed the problem the restoration of the system back to its fully functioning condition can then take place. This paper expands the capability of previous approaches to fault detection and identification using fault trees for application to dynamically changing systems. The technique has two phases. The first phase is modelling and preparation carried out offline. This gathers information on the effects that sub-system failure will have on the system performance. Causes of the sub-system failures are developed in the form of fault trees. The second phase is application. Sensors are installed on the system to provide information about current system performance from which the potential causes can be deduced. A simple system example is used to demonstrate the features of the method. To illustrate the potential for the method to deal with additional system complexity and redundancy, a section from an aircraft fuel system is used. A discussion of the results is provided.

  5. Reverse engineering of inductive fault current limiters

    Energy Technology Data Exchange (ETDEWEB)

    Pina, J M; Neves, M Ventim; Rodrigues, A L [Centre of Technology and Systems Faculdade de Ciencias e Tecnologia, Nova University of Lisbon Monte de Caparica, 2829-516 Caparica (Portugal); Suarez, P; Alvarez, A, E-mail: jmmp@fct.unl.p [' Benito Mahedero' Group of Electrical Applications of Superconductors Escuela de IngenierIas Industrials, University of Extremadura Avenida de Elvas s/n, 06006 Badajoz (Spain)

    2010-06-01

    The inductive fault current limiter is less compact and harder to scale to high voltage networks than the resistive one. Nevertheless, its simple construction and mechanical robustness make it attractive in low voltage grids. Thus, it might be an enabling technology for the advent of microgrids, low voltage networks with dispersed generation, controllable loads and energy storage. A new methodology for reverse engineering of inductive fault current limiters based on the independent analysis of iron cores and HTS cylinders is presented in this paper. Their electromagnetic characteristics are used to predict the devices' hysteresis loops and consequently their dynamic behavior. Previous models based on the separate analysis of the limiters' components were already derived, e.g. in transformer like equivalent models. Nevertheless, the assumptions usually made may limit these models' application, as shown in the paper. The proposed methodology obviates these limitations. Results are validated through simulations.

  6. Reverse engineering of inductive fault current limiters

    International Nuclear Information System (INIS)

    Pina, J M; Neves, M Ventim; Rodrigues, A L; Suarez, P; Alvarez, A

    2010-01-01

    The inductive fault current limiter is less compact and harder to scale to high voltage networks than the resistive one. Nevertheless, its simple construction and mechanical robustness make it attractive in low voltage grids. Thus, it might be an enabling technology for the advent of microgrids, low voltage networks with dispersed generation, controllable loads and energy storage. A new methodology for reverse engineering of inductive fault current limiters based on the independent analysis of iron cores and HTS cylinders is presented in this paper. Their electromagnetic characteristics are used to predict the devices' hysteresis loops and consequently their dynamic behavior. Previous models based on the separate analysis of the limiters' components were already derived, e.g. in transformer like equivalent models. Nevertheless, the assumptions usually made may limit these models' application, as shown in the paper. The proposed methodology obviates these limitations. Results are validated through simulations.

  7. Process plant alarm diagnosis using synthesised fault tree knowledge

    International Nuclear Information System (INIS)

    Trenchard, A.J.

    1990-01-01

    The development of computer based tools, to assist process plant operators in their task of fault/alarm diagnosis, has received much attention over the last twenty five years. More recently, with the emergence of Artificial Intelligence (AI) technology, the research activity in this subject area has heightened. As a result, there are a great variety of fault diagnosis methodologies, using many different approaches to represent the fault propagation behaviour of process plant. These range in complexity from steady state quantitative models to more abstract definitions of the relationships between process alarms. Unfortunately, very few of the techniques have been tried and tested on process plant and even fewer have been judged to be commercial successes. One of the outstanding problems still remains the time and effort required to understand and model the fault propagation behaviour of each considered process. This thesis describes the development of an experimental knowledge based system (KBS) to diagnose process plant faults, as indicated by process variable alarms. In an attempt to minimise the modelling effort, the KBS has been designed to infer diagnoses using a fault tree representation of the process behaviour, generated using an existing fault tree synthesis package (FAULTFINDER). The process is described to FAULTFINDER as a configuration of unit models, derived from a standard model library or by tailoring existing models. The resultant alarm diagnosis methodology appears to work well for hard (non-rectifying) faults, but is likely to be less robust when attempting to diagnose intermittent faults and transient behaviour. The synthesised fault trees were found to contain the bulk of the information required for the diagnostic task, however, this needed to be augmented with extra information in certain circumstances. (author)

  8. Risk-based fault detection using Self-Organizing Map

    International Nuclear Information System (INIS)

    Yu, Hongyang; Khan, Faisal; Garaniya, Vikram

    2015-01-01

    The complexity of modern systems is increasing rapidly and the dominating relationships among system variables have become highly non-linear. This results in difficulty in the identification of a system's operating states. In turn, this difficulty affects the sensitivity of fault detection and imposes a challenge on ensuring the safety of operation. In recent years, Self-Organizing Maps has gained popularity in system monitoring as a robust non-linear dimensionality reduction tool. Self-Organizing Map is able to capture non-linear variations of the system. Therefore, it is sensitive to the change of a system's states leading to early detection of fault. In this paper, a new approach based on Self-Organizing Map is proposed to detect and assess the risk of fault. In addition, probabilistic analysis is applied to characterize the risk of fault into different levels according to the hazard potential to enable a refined monitoring of the system. The proposed approach is applied on two experimental systems. The results from both systems have shown high sensitivity of the proposed approach in detecting and identifying the root cause of faults. The refined monitoring facilitates the determination of the risk of fault and early deployment of remedial actions and safety measures to minimize the potential impact of fault. - Highlights: • A new approach based on Self-Organizing Map is proposed to detect faults. • Integration of fault detection with risk assessment methodology. • Fault risk characterization into different levels to enable focused system monitoring

  9. Fault detection and fault tolerant control of a smart base isolation system with magneto-rheological damper

    International Nuclear Information System (INIS)

    Wang, Han; Song, Gangbing

    2011-01-01

    Fault detection and isolation (FDI) in real-time systems can provide early warnings for faulty sensors and actuator signals to prevent events that lead to catastrophic failures. The main objective of this paper is to develop FDI and fault tolerant control techniques for base isolation systems with magneto-rheological (MR) dampers. Thus, this paper presents a fixed-order FDI filter design procedure based on linear matrix inequalities (LMI). The necessary and sufficient conditions for the existence of a solution for detecting and isolating faults using the H ∞ formulation is provided in the proposed filter design. Furthermore, an FDI-filter-based fuzzy fault tolerant controller (FFTC) for a base isolation structure model was designed to preserve the pre-specified performance of the system in the presence of various unknown faults. Simulation and experimental results demonstrated that the designed filter can successfully detect and isolate faults from displacement sensors and accelerometers while maintaining excellent performance of the base isolation technology under faulty conditions

  10. Fault tolerant control of wind turbines using unknown input observers

    DEFF Research Database (Denmark)

    Odgaard, Peter Fogh; Stoustrup, Jakob

    2012-01-01

    This paper presents a scheme for accommodating faults in the rotor and generator speed sensors in a wind turbine. These measured values are important both for the wind turbine controller as well as the supervisory control of the wind turbine. The scheme is based on unknown input observers, which...

  11. Dynamic Fault Diagnosis for Nuclear Installation Using Probabilistic Approach

    International Nuclear Information System (INIS)

    Djoko Hari Nugroho; Deswandri; Ahmad Abtokhi; Darlis

    2003-01-01

    Probabilistic based fault diagnosis which represent the relationship between cause and consequence of the events for trouble shooting is developed in this research based on Bayesian Networks. Contribution of on-line data comes from sensors and system/component reliability in node cause is expected increasing the belief level of Bayesian Networks. (author)

  12. Diagnosis and Fault-tolerant Control for Ship Station Keeping

    DEFF Research Database (Denmark)

    Blanke, Mogens

    2005-01-01

    design for systems of high complexity, and also analyse the cases of cascaded or multiple faults. The paper takes as example a ship with two CP propellers, rudders and a bow thruster as actuators, and instrumentation with a suite of global position sensors, inertial navigation units and conventional gyro...

  13. Fault healing and earthquake spectra from stick slip sequences in the laboratory and on active faults

    Science.gov (United States)

    McLaskey, G. C.; Glaser, S. D.; Thomas, A.; Burgmann, R.

    2011-12-01

    Repeating earthquake sequences (RES) are thought to occur on isolated patches of a fault that fail in repeated stick-slip fashion. RES enable researchers to study the effect of variations in earthquake recurrence time and the relationship between fault healing and earthquake generation. Fault healing is thought to be the physical process responsible for the 'state' variable in widely used rate- and state-dependent friction equations. We analyze RES created in laboratory stick slip experiments on a direct shear apparatus instrumented with an array of very high frequency (1KHz - 1MHz) displacement sensors. Tests are conducted on the model material polymethylmethacrylate (PMMA). While frictional properties of this glassy polymer can be characterized with the rate- and state- dependent friction laws, the rate of healing in PMMA is higher than room temperature rock. Our experiments show that in addition to a modest increase in fault strength and stress drop with increasing healing time, there are distinct spectral changes in the recorded laboratory earthquakes. Using the impact of a tiny sphere on the surface of the test specimen as a known source calibration function, we are able to remove the instrument and apparatus response from recorded signals so that the source spectrum of the laboratory earthquakes can be accurately estimated. The rupture of a fault that was allowed to heal produces a laboratory earthquake with increased high frequency content compared to one produced by a fault which has had less time to heal. These laboratory results are supported by observations of RES on the Calaveras and San Andreas faults, which show similar spectral changes when recurrence time is perturbed by a nearby large earthquake. Healing is typically attributed to a creep-like relaxation of the material which causes the true area of contact of interacting asperity populations to increase with time in a quasi-logarithmic way. The increase in high frequency seismicity shown here

  14. Sensor Data Quality and Angular Rate Down-Selection Algorithms on SLS EM-1

    Science.gov (United States)

    Park, Thomas; Smith, Austin; Oliver, T. Emerson

    2018-01-01

    The NASA Space Launch System Block 1 launch vehicle is equipped with an Inertial Navigation System (INS) and multiple Rate Gyro Assemblies (RGA) that are used in the Guidance, Navigation, and Control (GN&C) algorithms. The INS provides the inertial position, velocity, and attitude of the vehicle along with both angular rate and specific force measurements. Additionally, multiple sets of co-located rate gyros supply angular rate data. The collection of angular rate data, taken along the launch vehicle, is used to separate out vehicle motion from flexible body dynamics. Since the system architecture uses redundant sensors, the capability was developed to evaluate the health (or validity) of the independent measurements. A suite of Sensor Data Quality (SDQ) algorithms is responsible for assessing the angular rate data from the redundant sensors. When failures are detected, SDQ will take the appropriate action and disqualify or remove faulted sensors from forward processing. Additionally, the SDQ algorithms contain logic for down-selecting the angular rate data used by the GNC software from the set of healthy measurements. This paper explores the trades and analyses that were performed in selecting a set of robust fault-detection algorithms included in the GN&C flight software. These trades included both an assessment of hardware-provided health and status data as well as an evaluation of different algorithms based on time-to-detection, type of failures detected, and probability of detecting false positives. We then provide an overview of the algorithms used for both fault-detection and measurement down selection. We next discuss the role of trajectory design, flexible-body models, and vehicle response to off-nominal conditions in setting the detection thresholds. Lastly, we present lessons learned from software integration and hardware-in-the-loop testing.

  15. Seismic anisotropy in the vicinity of the Alpine fault, New Zealand, estimated by seismic interferometry

    Science.gov (United States)

    Takagi, R.; Okada, T.; Yoshida, K.; Townend, J.; Boese, C. M.; Baratin, L. M.; Chamberlain, C. J.; Savage, M. K.

    2016-12-01

    We estimate shear wave velocity anisotropy in shallow crust near the Alpine fault using seismic interferometry of borehole vertical arrays. We utilized four borehole observations: two sensors are deployed in two boreholes of the Deep Fault Drilling Project in the hanging wall side, and the other two sites are located in the footwall side. Surface sensors deployed just above each borehole are used to make vertical arrays. Crosscorrelating rotated horizontal seismograms observed by the borehole and surface sensors, we extracted polarized shear waves propagating from the bottom to the surface of each borehole. The extracted shear waves show polarization angle dependence of travel time, indicating shear wave anisotropy between the two sensors. In the hanging wall side, the estimated fast shear wave directions are parallel to the Alpine fault. Strong anisotropy of 20% is observed at the site within 100 m from the Alpine fault. The hanging wall consists of mylonite and schist characterized by fault parallel foliation. In addition, an acoustic borehole imaging reveals fractures parallel to the Alpine fault. The fault parallel anisotropy suggest structural anisotropy is predominant in the hanging wall, demonstrating consistency of geological and seismological observations. In the footwall side, on the other hand, the angle between the fast direction and the strike of the Alpine fault is 33-40 degrees. Since the footwall is composed of granitoid that may not have planar structure, stress induced anisotropy is possibly predominant. The direction of maximum horizontal stress (SHmax) estimated by focal mechanisms of regional earthquakes is 55 degrees of the Alpine fault. Possible interpretation of the difference between the fast direction and SHmax direction is depth rotation of stress field near the Alpine fault. Similar depth rotation of stress field is also observed in the SAFOD borehole at the San Andreas fault.

  16. Signal processing for solar array monitoring, fault detection, and optimization

    CERN Document Server

    Braun, Henry; Spanias, Andreas

    2012-01-01

    Although the solar energy industry has experienced rapid growth recently, high-level management of photovoltaic (PV) arrays has remained an open problem. As sensing and monitoring technology continues to improve, there is an opportunity to deploy sensors in PV arrays in order to improve their management. In this book, we examine the potential role of sensing and monitoring technology in a PV context, focusing on the areas of fault detection, topology optimization, and performance evaluation/data visualization. First, several types of commonly occurring PV array faults are considered and detection algorithms are described. Next, the potential for dynamic optimization of an array's topology is discussed, with a focus on mitigation of fault conditions and optimization of power output under non-fault conditions. Finally, monitoring system design considerations such as type and accuracy of measurements, sampling rate, and communication protocols are considered. It is our hope that the benefits of monitoring presen...

  17. A Diagnostic System for Speed-Varying Motor Rotary Faults

    Directory of Open Access Journals (Sweden)

    Chwan-Lu Tseng

    2014-01-01

    Full Text Available This study proposed an intelligent rotary fault diagnostic system for motors. A sensorless rotational speed detection method and an improved dynamic structural neural network are used. Moreover, to increase the convergence speed of training, a terminal attractor method and a hybrid discriminant analysis are also adopted. The proposed method can be employed to detect the rotary frequencies of motors with varying speeds and can enhance the discrimination of motor faults. To conduct the experiments, this study used wireless sensor nodes to transmit vibration data and employed MATLAB to write codes for functional modules, including the signal processing, sensorless rotational speed estimation, neural network, and stochastic process control chart. Additionally, Visual Basic software was used to create an integrated human-machine interface. The experimental results regarding the test of equipment faults indicated that the proposed novel diagnostic system can effectively estimate rotational speeds and provide superior ability of motor fault discrimination with fast training convergence.

  18. Robustness of Structures

    DEFF Research Database (Denmark)

    Faber, Michael Havbro; Vrouwenvelder, A.C.W.M.; Sørensen, John Dalsgaard

    2011-01-01

    In 2005, the Joint Committee on Structural Safety (JCSS) together with Working Commission (WC) 1 of the International Association of Bridge and Structural Engineering (IABSE) organized a workshop on robustness of structures. Two important decisions resulted from this workshop, namely...... ‘COST TU0601: Robustness of Structures’ was initiated in February 2007, aiming to provide a platform for exchanging and promoting research in the area of structural robustness and to provide a basic framework, together with methods, strategies and guidelines enhancing robustness of structures...... the development of a joint European project on structural robustness under the COST (European Cooperation in Science and Technology) programme and the decision to develop a more elaborate document on structural robustness in collaboration between experts from the JCSS and the IABSE. Accordingly, a project titled...

  19. Robust Growth Determinants

    OpenAIRE

    Doppelhofer, Gernot; Weeks, Melvyn

    2011-01-01

    This paper investigates the robustness of determinants of economic growth in the presence of model uncertainty, parameter heterogeneity and outliers. The robust model averaging approach introduced in the paper uses a flexible and parsi- monious mixture modeling that allows for fat-tailed errors compared to the normal benchmark case. Applying robust model averaging to growth determinants, the paper finds that eight out of eighteen variables found to be significantly related to economic growth ...

  20. Robust Programming by Example

    OpenAIRE

    Bishop , Matt; Elliott , Chip

    2011-01-01

    Part 2: WISE 7; International audience; Robust programming lies at the heart of the type of coding called “secure programming”. Yet it is rarely taught in academia. More commonly, the focus is on how to avoid creating well-known vulnerabilities. While important, that misses the point: a well-structured, robust program should anticipate where problems might arise and compensate for them. This paper discusses one view of robust programming and gives an example of how it may be taught.