WorldWideScience

Sample records for thruster fault detection

  1. Predictive fault-tolerant control of an all-thruster satellite in 6-DOF motion via neural network model updating

    Science.gov (United States)

    Tavakoli, M. M.; Assadian, N.

    2018-03-01

    The problem of controlling an all-thruster spacecraft in the coupled translational-rotational motion in presence of actuators fault and/or failure is investigated in this paper. The nonlinear model predictive control approach is used because of its ability to predict the future behavior of the system. The fault/failure of the thrusters changes the mapping between the commanded forces to the thrusters and actual force/torque generated by the thruster system. Thus, the basic six degree-of-freedom kinetic equations are separated from this mapping and a set of neural networks are trained off-line to learn the kinetic equations. Then, two neural networks are attached to these trained networks in order to learn the thruster commands to force/torque mappings on-line. Different off-nominal conditions are modeled so that neural networks can detect any failure and fault, including scale factor and misalignment of thrusters. A simple model of the spacecraft relative motion is used in MPC to decrease the computational burden. However, a precise model by the means of orbit propagation including different types of perturbation is utilized to evaluate the usefulness of the proposed approach in actual conditions. The numerical simulation shows that this method can successfully control the all-thruster spacecraft with ON-OFF thrusters in different combinations of thruster fault and/or failure.

  2. Fault-Tolerant Region-Based Control of an Underwater Vehicle with Kinematically Redundant Thrusters

    Directory of Open Access Journals (Sweden)

    Zool H. Ismail

    2014-01-01

    Full Text Available This paper presents a new control approach for an underwater vehicle with a kinematically redundant thruster system. This control scheme is derived based on a fault-tolerant decomposition for thruster force allocation and a region control scheme for the tracking objective. Given a redundant thruster system, that is, six or more pairs of thrusters are used, the proposed redundancy resolution and region control scheme determine the number of thruster faults, as well as providing the reference thruster forces in order to keep the underwater vehicle within the desired region. The stability of the presented control law is proven in the sense of a Lyapunov function. Numerical simulations are performed with an omnidirectional underwater vehicle and the results of the proposed scheme illustrate the effectiveness in terms of optimizing the thruster forces.

  3. 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.

  4. 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...... in a standard setup and a synthesis method for fault detectors is given. Further, fault detection problems with both parametric faults and faults described by external input signals are also shortly considered....

  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. Thruster fault identification method for autonomous underwater vehicle using peak region energy and least square grey relational grade

    OpenAIRE

    Mingjun Zhang; Baoji Yin; Xing Liu; Jia Guo

    2015-01-01

    A novel thruster fault identification method for autonomous underwater vehicle is presented in this article. It uses the proposed peak region energy method to extract fault feature and uses the proposed least square grey relational grade method to estimate fault degree. The peak region energy method is developed from fusion feature modulus maximum method. It applies the fusion feature modulus maximum method to get fusion feature and then regards the maximum of peak region energy in the convol...

  7. Diagnosis and Fault-Tolerant Control for Thruster-Assisted Position Mooring System

    DEFF Research Database (Denmark)

    Nguyen, Trong Dong; Blanke, Mogens; Sørensen, Asgeir

    2007-01-01

    's pretension or line breaks will degrade the performance of the positioning of the vessel. Faults will be detected and isolated through a fault diagnosis procedure. When faults are detected, they can be accommodated through the control action in which only parameter of the controlled plant has to be updated...

  8. Arc fault detection system

    Science.gov (United States)

    Jha, K.N.

    1999-05-18

    An arc fault detection system for use on ungrounded or high-resistance-grounded power distribution systems is provided which can be retrofitted outside electrical switchboard circuits having limited space constraints. The system includes a differential current relay that senses a current differential between current flowing from secondary windings located in a current transformer coupled to a power supply side of a switchboard, and a total current induced in secondary windings coupled to a load side of the switchboard. When such a current differential is experienced, a current travels through a operating coil of the differential current relay, which in turn opens an upstream circuit breaker located between the switchboard and a power supply to remove the supply of power to the switchboard. 1 fig.

  9. Final Technical Report: PV Fault Detection Tool.

    Energy Technology Data Exchange (ETDEWEB)

    King, Bruce Hardison [Sandia National Lab. (SNL-NM), Albuquerque, NM (United States); Jones, Christian Birk [Sandia National Lab. (SNL-NM), Albuquerque, NM (United States)

    2015-12-01

    The PV Fault Detection Tool project plans to demonstrate that the FDT can (a) detect catastrophic and degradation faults and (b) identify the type of fault. This will be accomplished by collecting fault signatures using different instruments and integrating this information to establish a logical controller for detecting, diagnosing and classifying each fault.

  10. Static Decoupling in fault detection

    DEFF Research Database (Denmark)

    Niemann, Hans Henrik

    1998-01-01

    An algebraic approach is given for a design of a static residual weighting factor in connection with fault detection. A complete parameterization is given of the weighting factor which will minimize a given performance index......An algebraic approach is given for a design of a static residual weighting factor in connection with fault detection. A complete parameterization is given of the weighting factor which will minimize a given performance index...

  11. Row fault detection system

    Science.gov (United States)

    Archer, Charles Jens [Rochester, MN; Pinnow, Kurt Walter [Rochester, MN; Ratterman, Joseph D [Rochester, MN; Smith, Brian Edward [Rochester, MN

    2008-10-14

    An apparatus, program product and method checks for nodal faults in a row of nodes by causing each node in the row to concurrently communicate with its adjacent neighbor nodes in the row. The communications are analyzed to determine a presence of a faulty node or connection.

  12. Thruster fault diagnosis method based on Gaussian particle filter for autonomous underwater vehicles

    Directory of Open Access Journals (Sweden)

    Yu-shan Sun

    2016-05-01

    Full Text Available Autonomous Underwater Vehicles (AUVs generally work in complex marine environments. Any fault in AUVs may cause significant losses. Thus, system reliability and automatic fault diagnosis are important. To address the actuator failure of AUVs, a fault diagnosis method based on the Gaussian particle filter is proposed in this study. Six free-space motion equation mathematical models are established in accordance with the actuator configuration of AUVs. The value of the control (moment loss parameter is adopted on the basis of these models to represent underwater vehicle malfunction, and an actuator failure model is established. An improved Gaussian particle filtering algorithm is proposed and is used to estimate the AUV failure model and motion state. Bayes algorithm is employed to perform robot fault detection. The sliding window method is adopted for fault magnitude estimation. The feasibility and validity of the proposed method are verified through simulation experiments and experimental data.

  13. Fault Detection for Nonlinear Systems

    DEFF Research Database (Denmark)

    Stoustrup, Jakob; Niemann, H.H.

    1998-01-01

    The paper describes a general method for designing fault detection and isolation (FDI) systems for nonlinear processes. For a rich class of nonlinear systems, a nonlinear FDI system can be designed using convex optimization procedures. The proposed method is a natural extension of methods based o...

  14. Fault detection using (PI) observers

    DEFF Research Database (Denmark)

    Niemann, Hans Henrik; Stoustrup, J.; Shafai, B.

    The fault detection and isolation (FDI) problem in connection with Proportional Integral (PI) Observers is considered in this paper. A compact formulation of the FDI design problem using PI observers is given. An analysis of the FDI design problem is derived with respectt to the time domain...

  15. Fault detection using (PI) observers

    DEFF Research Database (Denmark)

    Niemann, Hans Henrik; Stoustrup, J.; Shafai, B.

    1997-01-01

    The fault detection and isolation (FDI) problem in connection with Proportional Integral (PI) Observers is considered in this paper. A compact formulation of the FDI design problem using PI observers is given. An analysis of the FDI design problem is derived with respectt to the time domain...

  16. Thruster fault identification method for autonomous underwater vehicle using peak region energy and least square grey relational grade

    Directory of Open Access Journals (Sweden)

    Mingjun Zhang

    2015-12-01

    Full Text Available A novel thruster fault identification method for autonomous underwater vehicle is presented in this article. It uses the proposed peak region energy method to extract fault feature and uses the proposed least square grey relational grade method to estimate fault degree. The peak region energy method is developed from fusion feature modulus maximum method. It applies the fusion feature modulus maximum method to get fusion feature and then regards the maximum of peak region energy in the convolution operation results of fusion feature as fault feature. The least square grey relational grade method is developed from grey relational analysis algorithm. It determines the fault degree interval by the grey relational analysis algorithm and then estimates fault degree in the interval by least square algorithm. Pool experiments of the experimental prototype are conducted to verify the effectiveness of the proposed methods. The experimental results show that the fault feature extracted by the peak region energy method is monotonic to fault degree while the one extracted by the fusion feature modulus maximum method is not. The least square grey relational grade method can further get an estimation result between adjacent standard fault degrees while the estimation result of the grey relational analysis algorithm is just one of the standard fault degrees.

  17. Wind turbine fault detection and fault tolerant control

    DEFF Research Database (Denmark)

    Odgaard, Peter Fogh; Johnson, Kathryn

    2013-01-01

    In this updated edition of a previous wind turbine fault detection and fault tolerant control challenge, we present a more sophisticated wind turbine model and updated fault scenarios to enhance the realism of the challenge and therefore the value of the solutions. This paper describes...... the challenge model and the requirements for challenge participants. In addition, it motivates many of the faults by citing publications that give field data from wind turbine control tests....

  18. Expert System Detects Power-Distribution Faults

    Science.gov (United States)

    Walters, Jerry L.; Quinn, Todd M.

    1994-01-01

    Autonomous Power Expert (APEX) computer program is prototype expert-system program detecting faults in electrical-power-distribution system. Assists human operators in diagnosing faults and deciding what adjustments or repairs needed for immediate recovery from faults or for maintenance to correct initially nonthreatening conditions that could develop into faults. Written in Lisp.

  19. Actuator Fault Detection and Diagnosis for Quadrotors

    NARCIS (Netherlands)

    Lu, P.; Van Kampen, E.J.; Yu, B.

    2014-01-01

    This paper presents a method for fault detection and diagnosis of actuator loss of effectiveness for a quadrotor helicopter. This paper not only considers the detection of the actuator loss of effectiveness faults, but also addresses the diagnosis of the faults. The detection and estimation of the

  20. Exact, almost and delayed fault detection

    DEFF Research Database (Denmark)

    Niemann, Hans Henrik; Saberi, Ali; Stoorvogel, Anton A.

    1999-01-01

    Considers the problem of fault detection and isolation while using zero or almost zero threshold. A number of different fault detection and isolation problems using exact or almost exact disturbance decoupling are formulated. Solvability conditions are given for the formulated design problems....... The l-step delayed fault detection problem is also considered for discrete-time systems....

  1. Fault detection and isolation for complex system

    Science.gov (United States)

    Jing, Chan Shi; Bayuaji, Luhur; Samad, R.; Mustafa, M.; Abdullah, N. R. H.; Zain, Z. M.; Pebrianti, Dwi

    2017-07-01

    Fault Detection and Isolation (FDI) is a method to monitor, identify, and pinpoint the type and location of system fault in a complex multiple input multiple output (MIMO) non-linear system. A two wheel robot is used as a complex system in this study. The aim of the research is to construct and design a Fault Detection and Isolation algorithm. The proposed method for the fault identification is using hybrid technique that combines Kalman filter and Artificial Neural Network (ANN). The Kalman filter is able to recognize the data from the sensors of the system and indicate the fault of the system in the sensor reading. Error prediction is based on the fault magnitude and the time occurrence of fault. Additionally, Artificial Neural Network (ANN) is another algorithm used to determine the type of fault and isolate the fault in the system.

  2. Active fault detection in MIMO systems

    DEFF Research Database (Denmark)

    Niemann, Hans Henrik; Poulsen, Niels Kjølstad

    2014-01-01

    The focus in this paper is on active fault detection (AFD) for MIMO systems with parametric faults. The problem of design of auxiliary inputs with respect to detection of parametric faults is investigated. An analysis of the design of auxiliary inputs is given based on analytic transfer functions...... from auxiliary input to residual outputs. The analysis is based on a singular value decomposition of these transfer functions Based on this analysis, it is possible to design auxiliary input as well as design of the associated residual vector with respect to every single parametric fault in the system...... such that it is possible to detect these faults....

  3. 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

  4. Applications of Fault Detection in Vibrating Structures

    Science.gov (United States)

    Eure, Kenneth W.; Hogge, Edward; Quach, Cuong C.; Vazquez, Sixto L.; Russell, Andrew; Hill, Boyd L.

    2012-01-01

    Structural fault detection and identification remains an area of active research. Solutions to fault detection and identification may be based on subtle changes in the time series history of vibration signals originating from various sensor locations throughout the structure. The purpose of this paper is to document the application of vibration based fault detection methods applied to several structures. Overall, this paper demonstrates the utility of vibration based methods for fault detection in a controlled laboratory setting and limitations of applying the same methods to a similar structure during flight on an experimental subscale aircraft.

  5. 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.

  6. Detecting Fan Faults in refrigerated Cabinets

    DEFF Research Database (Denmark)

    Thybo, C.; Rasmussen, B.D.; Izadi-Zamanabadi, Roozbeh

    2002-01-01

    Fault detection in supermarket refrigeration systems is an important topic due to both economic and food safety reasons. If faults can be detected and diagnosed before the system drifts outside the specified operational envelope, service costs can be reduced and in extreme cases the costly discar...

  7. Integration of control and fault detection

    DEFF Research Database (Denmark)

    Niemann, Hans Henrik; Stoustrup, J.

    The integrated design of control and fault detection is studied. The result of the analysis is that it is possible to separate the design of the controller and the filter for fault detection in the case where the nominal model can be assumed to be fairly accurate. In the uncertain case, however...

  8. Fault Detection for a Diesel Engine Actuator

    DEFF Research Database (Denmark)

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

    1995-01-01

    An electro-mechanical position servo is introduced as a benchmark for mode-based Fault Detection and Identification (FDI).......An electro-mechanical position servo is introduced as a benchmark for mode-based Fault Detection and Identification (FDI)....

  9. Controller modification applied for active fault detection

    DEFF Research Database (Denmark)

    Niemann, Hans Henrik; Stoustrup, Jakob; Poulsen, Niels Kjølstad

    2014-01-01

    This paper is focusing on active fault detection (AFD) for parametric faults in closed-loop systems. This auxiliary input applied for the fault detection will also disturb the external output and consequently reduce the performance of the controller. Therefore, only small auxiliary inputs are used...... the feedback controller with a minor effect on the external output in the fault free case. Further, in the faulty case, the signature of the auxiliary input can be optimized. This is obtained by using a band-pass filter for the YJBK parameter that is only effective in a small frequency range where...

  10. Aluminium Process Fault Detection and Diagnosis

    Directory of Open Access Journals (Sweden)

    Nazatul Aini Abd Majid

    2015-01-01

    Full Text Available The challenges in developing a fault detection and diagnosis system for industrial applications are not inconsiderable, particularly complex materials processing operations such as aluminium smelting. However, the organizing into groups of the various fault detection and diagnostic systems of the aluminium smelting process can assist in the identification of the key elements of an effective monitoring system. This paper reviews aluminium process fault detection and diagnosis systems and proposes a taxonomy that includes four key elements: knowledge, techniques, usage frequency, and results presentation. Each element is explained together with examples of existing systems. A fault detection and diagnosis system developed based on the proposed taxonomy is demonstrated using aluminium smelting data. A potential new strategy for improving fault diagnosis is discussed based on the ability of the new technology, augmented reality, to augment operators’ view of an industrial plant, so that it permits a situation-oriented action in real working environments.

  11. 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...... turbine fault detection and fault tolerant control benchmark model, in which one of the included faults results in a change in the gear box resonance frequency. This evaluation shows the potential of the proposed scheme to monitor the condition of wind turbine gear boxes in the existing control system....

  12. Fault Detection and Isolation for Spacecraft

    DEFF Research Database (Denmark)

    Jensen, Hans-Christian Becker; Wisniewski, Rafal

    2002-01-01

    This article realizes nonlinear Fault Detection and Isolation for actuators, given there is no measurement of the states in the actuators. The Fault Detection and Isolation of the actuators is instead based on angular velocity measurement of the spacecraft and knowledge about the dynamics...... of the satellite. The algorithms presented in this paper are based on a geometric approach to achieve nonlinear Fault Detection and Isolation. The proposed algorithms are tested in a simulation study and the pros and cons of the algorithms are discussed....

  13. Fault Detection and Isolation in Centrifugal Pumps

    DEFF Research Database (Denmark)

    Kallesøe, Carsten

    when they occur. Therefore, detection of faults, if possible in an early stage, and isolation of their causes are of great interest. Especially fault detection, which can be used for predictive maintenance, can decrease working expenses and increase the reliability of the application in which the pump...... is placed. The topic of this work is Fault Detection and Identification in centrifugal pumps. Different approaches are developed with special focus on robustness. Robustness with respect to disturbances, unknown parts of the system, and parameter variations are considered. All developed algorithms...

  14. Cell boundary fault detection system

    Science.gov (United States)

    Archer, Charles Jens [Rochester, MN; Pinnow, Kurt Walter [Rochester, MN; Ratterman, Joseph D [Rochester, MN; Smith, Brian Edward [Rochester, MN

    2009-05-05

    A method determines a nodal fault along the boundary, or face, of a computing cell. Nodes on adjacent cell boundaries communicate with each other, and the communications are analyzed to determine if a node or connection is faulty.

  15. 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....

  16. A fault detection and diagnosis in a PWR steam generator

    International Nuclear Information System (INIS)

    Park, Seung Yub

    1991-01-01

    The purpose of this study is to develop a fault detection and diagnosis scheme that can monitor process fault and instrument fault of a steam generator. The suggested scheme consists of a Kalman filter and two bias estimators. Method of detecting process and instrument fault in a steam generator uses the mean test on the residual sequence of Kalman filter, designed for the unfailed system, to make a fault decision. Once a fault is detected, two bias estimators are driven to estimate the fault and to discriminate process fault and instrument fault. In case of process fault, the fault diagnosis of outlet temperature, feed-water heater and main steam control valve is considered. In instrument fault, the fault diagnosis of steam generator's three instruments is considered. Computer simulation tests show that on-line prompt fault detection and diagnosis can be performed very successfully.(Author)

  17. Statistical fault detection in photovoltaic systems

    KAUST Repository

    Garoudja, Elyes

    2017-05-08

    Faults in photovoltaic (PV) systems, which can result in energy loss, system shutdown or even serious safety breaches, are often difficult to avoid. Fault detection in such systems is imperative to improve their reliability, productivity, safety and efficiency. Here, an innovative model-based fault-detection approach for early detection of shading of PV modules and faults on the direct current (DC) side of PV systems is proposed. This approach combines the flexibility, and simplicity of a one-diode model with the extended capacity of an exponentially weighted moving average (EWMA) control chart to detect incipient changes in a PV system. The one-diode model, which is easily calibrated due to its limited calibration parameters, is used to predict the healthy PV array\\'s maximum power coordinates of current, voltage and power using measured temperatures and irradiances. Residuals, which capture the difference between the measurements and the predictions of the one-diode model, are generated and used as fault indicators. Then, the EWMA monitoring chart is applied on the uncorrelated residuals obtained from the one-diode model to detect and identify the type of fault. Actual data from the grid-connected PV system installed at the Renewable Energy Development Center, Algeria, are used to assess the performance of the proposed approach. Results show that the proposed approach successfully monitors the DC side of PV systems and detects temporary shading.

  18. Fault Detection for Quantized Networked Control Systems

    Directory of Open Access Journals (Sweden)

    Wei-Wei Che

    2013-01-01

    Full Text Available The fault detection problem in the finite frequency domain for networked control systems with signal quantization is considered. With the logarithmic quantizer consideration, a quantized fault detection observer is designed by employing a performance index which is used to increase the fault sensitivity in finite frequency domain. The quantized measurement signals are dealt with by utilizing the sector bound method, in which the quantization error is treated as sector-bounded uncertainty. By using the Kalman-Yakubovich-Popov (GKYP Lemma, an iterative LMI-based optimization algorithm is developed for designing the quantized fault detection observer. And a numerical example is given to illustrate the effectiveness of the proposed method.

  19. 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.

  20. Fault detection based on microseismic events

    Science.gov (United States)

    Yin, Chen

    2017-09-01

    In unconventional reservoirs, small faults allow the flow of oil and gas as well as act as obstacles to exploration; for, (1) fracturing facilitates fluid migration, (2) reservoir flooding, and (3) triggering of small earthquakes. These small faults are not generally detected because of the low seismic resolution. However, such small faults are very active and release sufficient energy to initiate a large number of microseismic events (MEs) during hydraulic fracturing. In this study, we identified microfractures (MF) from hydraulic fracturing and natural small faults based on microseismicity characteristics, such as the time-space distribution, source mechanism, magnitude, amplitude, and frequency. First, I identified the mechanism of small faults and MF by reservoir stress analysis and calibrated the ME based on the microseismic magnitude. The dynamic characteristics (frequency and amplitude) of MEs triggered by natural faults and MF were analyzed; moreover, the geometry and activity types of natural fault and MF were grouped according to the source mechanism. Finally, the differences among time-space distribution, magnitude, source mechanism, amplitude, and frequency were used to differentiate natural faults and manmade fractures.

  1. 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.

  2. An Overview of Transmission Line Protection by Artificial Neural Network: Fault Detection, Fault Classification, Fault Location, and Fault Direction Discrimination

    Directory of Open Access Journals (Sweden)

    Anamika Yadav

    2014-01-01

    Full Text Available Contemporary power systems are associated with serious issues of faults on high voltage transmission lines. Instant isolation of fault is necessary to maintain the system stability. Protective relay utilizes current and voltage signals to detect, classify, and locate the fault in transmission line. A trip signal will be sent by the relay to a circuit breaker with the purpose of disconnecting the faulted line from the rest of the system in case of a disturbance for maintaining the stability of the remaining healthy system. This paper focuses on the studies of fault detection, fault classification, fault location, fault phase selection, and fault direction discrimination by using artificial neural networks approach. Artificial neural networks are valuable for power system applications as they can be trained with offline data. Efforts have been made in this study to incorporate and review approximately all important techniques and philosophies of transmission line protection reported in the literature till June 2014. This comprehensive and exhaustive survey will reduce the difficulty of new researchers to evaluate different ANN based techniques with a set of references of all concerned contributions.

  3. Fundamental problems in fault detection and identification

    DEFF Research Database (Denmark)

    Saberi, Ali; Stoorvogel, Anton A.; Sannuti, Peddapullaiah

    1999-01-01

    For certain fundamental problems in fault detection and identification, the necessary and sufficient conditions for their solvability are derived. These conditions are weaker than the ones found in the literature, since we do not assume any particular structure for the residual generator......For certain fundamental problems in fault detection and identification, the necessary and sufficient conditions for their solvability are derived. These conditions are weaker than the ones found in the literature, since we do not assume any particular structure for the residual generator...

  4. Fault detection and diagnosis of photovoltaic systems

    Science.gov (United States)

    Wu, Xing

    The rapid growth of the solar industry over the past several years has expanded the significance of photovoltaic (PV) systems. One of the primary aims of research in building-integrated PV systems is to improve the performance of the system's efficiency, availability, and reliability. Although much work has been done on technological design to increase a photovoltaic module's efficiency, there is little research so far on fault diagnosis for PV systems. Faults in a PV system, if not detected, may not only reduce power generation, but also threaten the availability and reliability, effectively the "security" of the whole system. In this paper, first a circuit-based simulation baseline model of a PV system with maximum power point tracking (MPPT) is developed using MATLAB software. MATLAB is one of the most popular tools for integrating computation, visualization and programming in an easy-to-use modeling environment. Second, data collection of a PV system at variable surface temperatures and insolation levels under normal operation is acquired. The developed simulation model of PV system is then calibrated and improved by comparing modeled I-V and P-V characteristics with measured I--V and P--V characteristics to make sure the simulated curves are close to those measured values from the experiments. Finally, based on the circuit-based simulation model, a PV model of various types of faults will be developed by changing conditions or inputs in the MATLAB model, and the I--V and P--V characteristic curves, and the time-dependent voltage and current characteristics of the fault modalities will be characterized for each type of fault. These will be developed as benchmark I-V or P-V, or prototype transient curves. If a fault occurs in a PV system, polling and comparing actual measured I--V and P--V characteristic curves with both normal operational curves and these baseline fault curves will aid in fault diagnosis.

  5. PV Systems Reliability Final Technical Report: Ground Fault Detection

    Energy Technology Data Exchange (ETDEWEB)

    Lavrova, Olga [Sandia National Lab. (SNL-NM), Albuquerque, NM (United States); Flicker, Jack David [Sandia National Lab. (SNL-NM), Albuquerque, NM (United States); Johnson, Jay [Sandia National Lab. (SNL-NM), Albuquerque, NM (United States)

    2016-01-01

    We have examined ground faults in PhotoVoltaic (PV) arrays and the efficacy of fuse, current detection (RCD), current sense monitoring/relays (CSM), isolation/insulation (Riso) monitoring, and Ground Fault Detection and Isolation (GFID) using simulations based on a Simulation Program with Integrated Circuit Emphasis SPICE ground fault circuit model, experimental ground faults installed on real arrays, and theoretical equations.

  6. Fault Detection for Shipboard Monitoring and Decision Support Systems

    DEFF Research Database (Denmark)

    Lajic, Zoran; Nielsen, Ulrik Dam

    2009-01-01

    In this paper a basic idea of a fault-tolerant monitoring and decision support system will be explained. Fault detection 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 detection...... will be presented for a containership with a real decision support system onboard. All possible faults can be simulated and detected using residuals and the generalized likelihood ratio (GLR) algorithm....

  7. All row, planar fault detection system

    Science.gov (United States)

    Archer, Charles Jens; Pinnow, Kurt Walter; Ratterman, Joseph D; Smith, Brian Edward

    2013-07-23

    An apparatus, program product and method for detecting nodal faults may simultaneously cause designated nodes of a cell to communicate with all nodes adjacent to each of the designated nodes. Furthermore, all nodes along the axes of the designated nodes are made to communicate with their adjacent nodes, and the communications are analyzed to determine if a node or connection is faulty.

  8. Fast fault detection for power distribution systems

    OpenAIRE

    Öhrström, Magnus

    2003-01-01

    The main topic of this licentiate thesis is fast faultdetection. The thesis summaries the work performed in theproject“Fast fault detection for distributionsystems”. In the first chapters of the thesis the term“fast”is used in a general manner. The term is laterdefined based upon considerations and conclusions made in thefirst chapters and then related to a specific time. To be able to understand and appreciate why fast faultdetection is necessary, power system faults and theirconsequences ar...

  9. Nonlinear Actuator Fault Detection and Isolation for a VTOL aircraft

    NARCIS (Netherlands)

    De Persis, Claudio; De Santis, Raffaella; Isidori, Alberto

    2001-01-01

    The recently introduced geometric approach to the nonlinear fault detection and isolation problem is used in this paper to detect actuator faults for the vertical takeoff and landing aircraft. The approach leads to a filter which, by processing the outputs of the plant, detects the faults and

  10. Detection of ''beading faults'' in welded tubes

    International Nuclear Information System (INIS)

    Mondot, J.

    In the steel tube industry the word ''beading'' refers to a highly localised leak affecting the welded zone. During the pneumatic test its flow rate is generally very low no more than a few thousandths of a mm 3 /second. Detection of such a fault by this test is consequently slow, and those which are choked or at the limit of leakage may escape detection. For greater safety, the tube technician is now using non-destructive testing methods such as eddy-currents and ultrasonics [fr

  11. A New Fault-tolerant Switched Reluctance Motor with reliable fault detection capability

    DEFF Research Database (Denmark)

    Lu, Kaiyuan

    2014-01-01

    For reliable fault detection, often, search coils are used in many fault-tolerant drives. The search coils occupy extra slot space. They are normally open-circuited and are not used for torque production. This degrades the motor performance, increases the cost and manufacture complexity. A new...... Fault-Tolerant Switched Reluctance (FTSR) motor is proposed in this paper. A unique feature of this special design is that it allows use of the unexcited phase coils as search coils for fault detection. Therefore this new motor has all the advantages of using search coils for reliable fault detection...

  12. Faults in clays their detection and properties

    International Nuclear Information System (INIS)

    Baldi, G.; Carabelli, E.; Chiantore, V.; Colombo, P.F.; Gruszka, A.; Pensieri, R.; Superbo, S.; Gera, F.

    1991-01-01

    The 'Faults in clays project', a cooperative research effort between Ismes and Enea of Italy and BGS and Exeter University of the UK, has been aimed at assessing and improving the resolution capability of some high resolution geophysical techniques for the detection of discontinuities in clay formations. All Ismes activities have been carried out in Italy: they consisted in the search of one or more sites - faulted clay formations - suitable for the execution of geophysical and geotechnical investigations, in the execution of such tests and in additional geological surveys and laboratory (geotechnical and geochemical) testing. The selected sites were two quarries in plio-pleistocenic clay formations in central Italy where faults had been observed. The greatest part of the research work has been carried out in the Orte site where also two 90 m boreholes have been drilled and cored. Geophysical work at Orte consisted of vertical electrical soundings (VESs) and horizontal electrical lines (HELs), four high resolution seismic reflection lines, and in-hole and cross-hole logs. Laboratory activities were geotechnical characterization and permeability tests, and measurements of disequilibrium in the uranium decay series. At Narni, where Exeter University sampled soil gases for geochemical analyses, the geophysical work consisted in a geo-electrical survey (five VESs and two HELs), and in two high resolution reflection seismic lines. Additional investigations included a structural geology survey. The main conclusion of the research is that current geophysical techniques do not have a resolution capacity sufficient to detect the existence and determine the characteristics of faults in deep homogeneous clay formations

  13. FUZZY FAULT DETECTION FOR PERMANENT MAGNET SYNCHRONOUS GENERATOR

    Directory of Open Access Journals (Sweden)

    N. Selvaganesan

    2011-07-01

    Full Text Available Faults in engineering systems are difficult to avoid and may result in serious consequences. Effective fault detection and diagnosis can improve system reliability and avoid expensive maintenance. In this paper fuzzy system based fault detection scheme for permanent magnet synchronous generator is proposed. The sequence current components like positive and negative sequence currents are used as fault indicators and given as inputs to fuzzy fault detector. Also, the fuzzy inference system is created and rule base is evaluated, relating the sequence current component to the type of faults. These rules are fired for specific changes in sequence current component and the faults are detected. The feasibility of the proposed scheme for permanent magnet synchronous generator is demonstrated for different types of fault under various operating conditions using MATLAB/Simulink.

  14. Implementation of a model based fault detection and diagnosis technique for actuation faults of the SSME

    Science.gov (United States)

    Duyar, A.; Guo, T.-H.; Merrill, W.; Musgrave, J.

    1991-01-01

    In a previous study, Guo, Merrill and Duyar, 1990, reported a conceptual development of a fault detection and diagnosis system for actuation faults of the Space Shuttle main engine. This study, which is a continuation of the previous work, implements the developed fault detection and diagnosis scheme for the real time actuation fault diagnosis of the Space Shuttle Main Engine. The scheme will be used as an integral part of an intelligent control system demonstration experiment at NASA Lewis. The diagnosis system utilizes a model based method with real time identification and hypothesis testing for actuation, sensor, and performance degradation faults.

  15. Fault Analysis and Detection in Microgrids with High PV Penetration

    Energy Technology Data Exchange (ETDEWEB)

    El Khatib, Mohamed [Sandia National Lab. (SNL-NM), Albuquerque, NM (United States); Hernandez Alvidrez, Javier [Sandia National Lab. (SNL-NM), Albuquerque, NM (United States); Ellis, Abraham [Sandia National Lab. (SNL-NM), Albuquerque, NM (United States)

    2017-05-01

    In this report we focus on analyzing current-controlled PV inverters behaviour under faults in order to develop fault detection schemes for microgrids with high PV penetration. Inverter model suitable for steady state fault studies is presented and the impact of PV inverters on two protection elements is analyzed. The studied protection elements are superimposed quantities based directional element and negative sequence directional element. Additionally, several non-overcurrent fault detection schemes are discussed in this report for microgrids with high PV penetration. A detailed time-domain simulation study is presented to assess the performance of the presented fault detection schemes under different microgrid modes of operation.

  16. 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....

  17. 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.

  18. Experimental Fault Detection and Accomodation for an Agricultural Mobile Robot

    DEFF Research Database (Denmark)

    Østergaard, Kasper Zinck; Vinther, D.; Bisgaard, Morten

    2005-01-01

    This paper presents a systematic procedure to achieve fault tolerant capability for a four-wheel driven, four-wheel steered mobile robot moving in outdoor terrain. The procedure is exemplified through the paper by applying on a compass module. Detailed methods for fault detection and fault...

  19. Nuclear power plant sensor fault detection using singular value

    Indian Academy of Sciences (India)

    The validation process consists of two steps: (i) residual generation and (ii) fault detection by residual evaluation.Singular value decomposition (SVD) and Euclidean distance (ED) methods are used to generate the residual and evaluate the fault on the residual space, respectively. This paper claims that SVD-based fault ...

  20. Automated vehicle for railway track fault detection

    Science.gov (United States)

    Bhushan, M.; Sujay, S.; Tushar, B.; Chitra, P.

    2017-11-01

    For the safety reasons, railroad tracks need to be inspected on a regular basis for detecting physical defects or design non compliances. Such track defects and non compliances, if not detected in a certain interval of time, may eventually lead to severe consequences such as train derailments. Inspection must happen twice weekly by a human inspector to maintain safety standards as there are hundreds and thousands of miles of railroad track. But in such type of manual inspection, there are many drawbacks that may result in the poor inspection of the track, due to which accidents may cause in future. So to avoid such errors and severe accidents, this automated system is designed.Such a concept would surely introduce automation in the field of inspection process of railway track and can help to avoid mishaps and severe accidents due to faults in the track.

  1. High Resolution Seismic Reflection Survey for Coal Mine: fault detection

    Science.gov (United States)

    Khukhuudei, M.; Khukhuudei, U.

    2014-12-01

    High Resolution Seismic Reflection (HRSR) methods will become a more important tool to help unravel structures hosting mineral deposits at great depth for mine planning and exploration. Modern coal mining requires certainly about geological faults and structural features. This paper focuses on 2D Seismic section mapping results from an "Zeegt" lignite coal mine in the "Mongol Altai" coal basin, which required the establishment of major structure for faults and basement. HRSR method was able to detect subsurface faults associated with the major fault system. We have used numerical modeling in an ideal, noise free environment with homogenous layering to detect of faults. In a coal mining setting where the seismic velocity of the high ranges from 3000m/s to 3600m/s and the dominant seismic frequency is 100Hz, available to locate faults with a throw of 4-5m. Faults with displacements as seam thickness detected down to several hundred meter beneath the surface.

  2. 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...... results show that the proposed method detects different fault scenarios of wind turbines under the stochastic external condition....

  3. Wavelet Packet based Detection of Surface Faults on Compact Discs

    DEFF Research Database (Denmark)

    Odgaard, Peter Fogh; Stoustrup, Jakob; Wickerhauser, Mladen Victor

    2006-01-01

    In this paper the detection of faults on the surface of a compact disc is addressed. Surface faults like scratches and fingerprints disturb the on-line measurement of the pick-up position relative to the track. This is critical since the pick-up is focused on and tracked at the information track...... based on these measurements. A precise detection of the surface fault is a prerequisite to a correct handling of the faults in order to protect the pick-up of the compact disc player from audible track losses. The actual fault handling which is addressed in other publications can be carried out...... by the use of dedicated filters adapted to remove the faults from the measurements. In this paper detection using wavelet packet filters is demonstrated. The filters are designed using the joint best basis method. Detection using these filters shows a distinct improvement compared to detection using ordinary...

  4. Planetary Gearbox Fault Detection Using Vibration Separation Techniques

    Science.gov (United States)

    Lewicki, David G.; LaBerge, Kelsen E.; Ehinger, Ryan T.; Fetty, Jason

    2011-01-01

    Studies were performed to demonstrate the capability to detect planetary gear and bearing faults in helicopter main-rotor transmissions. The work supported the Operations Support and Sustainment (OSST) program with the U.S. Army Aviation Applied Technology Directorate (AATD) and Bell Helicopter Textron. Vibration data from the OH-58C planetary system were collected on a healthy transmission as well as with various seeded-fault components. Planetary fault detection algorithms were used with the collected data to evaluate fault detection effectiveness. Planet gear tooth cracks and spalls were detectable using the vibration separation techniques. Sun gear tooth cracks were not discernibly detectable from the vibration separation process. Sun gear tooth spall defects were detectable. Ring gear tooth cracks were only clearly detectable by accelerometers located near the crack location or directly across from the crack. Enveloping provided an effective method for planet bearing inner- and outer-race spalling fault detection.

  5. 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.

  6. Current Signature Analysis as Diagnosis Media for Incipient Fault Detection

    Directory of Open Access Journals (Sweden)

    MIHET-POPA, L.

    2007-11-01

    Full Text Available This paper focuses on the experimental investigation for incipient fault detection and fault detection methods existing in the literature, using Wound Rotor Induction Machine (WRIM. Three main experiments (one for stator phase unbalance, one for rotor phase unbalance and one for turn-to-turn faults have been performed to study the electrical behavior of the WRIM. The article aims to provide further documentation for an advanced condition monitoring system, in order to avoid undesirable operating conditions and to detect and diagnose incipient electrical faults. A description of the measurement system and experimental investigation are presented and stator and rotor currents spectrum of the WRIM are analyzed.

  7. 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.

  8. Rapid detection of small oscillation faults via deterministic learning.

    Science.gov (United States)

    Wang, Cong; Chen, Tianrui

    2011-08-01

    Detection of small faults is one of the most important and challenging tasks in the area of fault diagnosis. In this paper, we present an approach for the rapid detection of small oscillation faults based on a recently proposed deterministic learning (DL) theory. The approach consists of two phases: the training phase and the test phase. In the training phase, the system dynamics underlying normal and fault oscillations are locally accurately approximated through DL. The obtained knowledge of system dynamics is stored in constant radial basis function (RBF) networks. In the diagnosis phase, rapid detection is implemented. Specially, a bank of estimators are constructed using the constant RBF neural networks to represent the training normal and fault modes. By comparing the set of estimators with the test monitored system, a set of residuals are generated, and the average L(1) norms of the residuals are taken as the measure of the differences between the dynamics of the monitored system and the dynamics of the training normal mode and oscillation faults. The occurrence of a test oscillation fault can be rapidly detected according to the smallest residual principle. A rigorous analysis of the performance of the detection scheme is also given. The novelty of the paper lies in that the modeling uncertainty and nonlinear fault functions are accurately approximated and then the knowledge is utilized to achieve rapid detection of small oscillation faults. Simulation studies are included to demonstrate the effectiveness of the approach.

  9. Model Based Fault Detection in a Centrifugal Pump Application

    DEFF Research Database (Denmark)

    Kallesøe, Carsten; Cocquempot, Vincent; Izadi-Zamanabadi, Roozbeh

    2006-01-01

    A model based approach for fault detection in a centrifugal pump, driven by an induction motor, is proposed in this paper. The fault detection algorithm is derived using a combination of structural analysis, observer design and Analytical Redundancy Relation (ARR) design. Structural considerations...

  10. An Improved Wavelet‐Based Multivariable Fault Detection Scheme

    KAUST Repository

    Harrou, Fouzi

    2017-07-06

    Data observed from environmental and engineering processes are usually noisy and correlated in time, which makes the fault detection more difficult as the presence of noise degrades fault detection quality. Multiscale representation of data using wavelets is a powerful feature extraction tool that is well suited to denoising and decorrelating time series data. In this chapter, we combine the advantages of multiscale partial least squares (MSPLSs) modeling with those of the univariate EWMA (exponentially weighted moving average) monitoring chart, which results in an improved fault detection system, especially for detecting small faults in highly correlated, multivariate data. Toward this end, we applied EWMA chart to the output residuals obtained from MSPLS model. It is shown through simulated distillation column data the significant improvement in fault detection can be obtained by using the proposed methods as compared to the use of the conventional partial least square (PLS)‐based Q and EWMA methods and MSPLS‐based Q method.

  11. Fault detection and fault-tolerant control for nonlinear systems

    CERN Document Server

    Li, Linlin

    2016-01-01

    Linlin Li addresses the analysis and design issues of observer-based FD and FTC for nonlinear systems. The author analyses the existence conditions for the nonlinear observer-based FD systems to gain a deeper insight into the construction of FD systems. Aided by the T-S fuzzy technique, she recommends different design schemes, among them the L_inf/L_2 type of FD systems. The derived FD and FTC approaches are verified by two benchmark processes. Contents Overview of FD and FTC Technology Configuration of Nonlinear Observer-Based FD Systems Design of L2 nonlinear Observer-Based FD Systems Design of Weighted Fuzzy Observer-Based FD Systems FTC Configurations for Nonlinear Systems< Application to Benchmark Processes Target Groups Researchers and students in the field of engineering with a focus on fault diagnosis and fault-tolerant control fields The Author Dr. Linlin Li completed her dissertation under the supervision of Prof. Steven X. Ding at the Faculty of Engineering, University of Duisburg-Essen, Germany...

  12. 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. The current observer is constructed by using only voltage signals as inputs. The fault detection algorithm is based on the current observer, in which an adaptive threshold and different fault duration times are considered. The performance of the proposed observer, improved 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...

  13. Bearing Fault Detection in Induction Motor-Gearbox Drivetrain

    International Nuclear Information System (INIS)

    Cibulka, Jaroslav; Ebbesen, Morten K; Robbersmyr, Kjell G

    2012-01-01

    The main contribution in the hereby presented paper is to investigate the fault detection capability of a motor current signature analysis by expanding its scope to include the gearbox, and not only the induction motor. Detecting bearing faults outside the induction motor through the stator current analysis represents an interesting alternative to traditional vibration analysis. Bearing faults cause changes in the stator current spectrum that can be used for fault diagnosis purposes. A time-domain simulation of the drivetrain model is developed. The drivetrain system consists of a loaded single stage gearbox driven by a line-fed induction motor. Three typical bearing faults in the gearbox are addressed, i.e. defects in the outer raceway, the inner raceway, and the rolling element. The interaction with the fault is modelled by means of kinematical and mechanical relations. The fault region is modelled in order to achieve gradual loss and gain of contact. A bearing fault generates an additional torque component that varies at the specific bearing defect frequency. The presented dynamic electromagnetic dq-model of an induction motor is adjusted for diagnostic purpose and considers such torque variations. The bearing fault is detected as a phase modulation of the stator current sine wave at the expected bearing defect frequency.

  14. Expert system structures for fault detection in spaceborne power systems

    Science.gov (United States)

    Watson, Karan; Russell, B. Don; Hackler, Irene

    1988-01-01

    This paper presents an architecture for an expert system structure suitable for use with power system fault detection algorithms. The system described is not for the purpose of reacting to faults which have occurred, but rather for the purpose of performing on-line diagnostics and parameter evaluation to determine potential or incipient fault conditions. The system is also designed to detect high impedance or arcing faults which cannot be detected by conventional protection devices. This system is part of an overall monitoring computer hierarchy which would provide a full evaluation of the status of the power system and react to both incipient and catastrophic faults. An approximate hardware structure is suggested and software requirements are discussed. Modifications to CLIPS software, to capitalize on features offered by expert systems, are presented. It is suggested that such a system would have significant advantages over existing protection philosophy.

  15. Multi Objective Design Techiques applied to Fault Detection and Isolation

    DEFF Research Database (Denmark)

    Niemann, Hans Henrik; Stoustrup, Jakob

    1998-01-01

    Various methods for design of fault detectors by using multi objective techniques are presented in this paper. The advantages by using multi objective design methods will be shown. The design methods will be compared to standard fault detections and isolation (FDI) design methods. The FDI problem...

  16. An application of LTR design in fault detection

    DEFF Research Database (Denmark)

    Niemann, Hans Henrik

    1998-01-01

    The fault detection and isolation (FDI) problem is considered in this paper. The FDI problem is formulated as a filter design problem, where the faults in the system is estimated and the disturbance acting on the system is rejected. It turns out that the filter design problem can be considered as...

  17. Fault Detection and Load Distribution for the Wind Farm Challenge

    DEFF Research Database (Denmark)

    Borchersen, Anders Bech; Larsen, Jesper Abildgaard; Stoustrup, Jakob

    2014-01-01

    In this paper a fault detection system and a fault tolerant controller for a wind farm model is designed and tested. The wind farm model is taken from the wind farm challenge which is a public available challenge where a wind farm consisting of nine turbines is proposed. The goal of the challenge...

  18. All-to-all sequenced fault detection system

    Science.gov (United States)

    Archer, Charles Jens; Pinnow, Kurt Walter; Ratterman, Joseph D.; Smith, Brian Edward

    2010-11-02

    An apparatus, program product and method enable nodal fault detection by sequencing communications between all system nodes. A master node may coordinate communications between two slave nodes before sequencing to and initiating communications between a new pair of slave nodes. The communications may be analyzed to determine the nodal fault.

  19. Fault Detection via Stability Analysis for the Hybrid Control Unit of HEVs

    OpenAIRE

    Kyogun Chang; Yoon Bok Lee

    2011-01-01

    Fault detection determines faultexistence and detecting time. This paper discusses two layered fault detection methods to enhance the reliability and safety. Two layered fault detection methods consist of fault detection methods of component level controllers and system level controllers. Component level controllers detect faults by using limit checking, model-based detection, and data-driven detection and system level controllers execute detection by stability analysis w...

  20. 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

  1. Fault detection of a benchmark wind turbine using interval analysis

    DEFF Research Database (Denmark)

    Tabatabaeipour, Seyed Mojtaba; Odgaard, Peter Fogh; Bak, Thomas

    2012-01-01

    This paper investigates a state estimation set- membership approach for fault detection of a benchmark wind turbine. The main challenges in the benchmark are high noise on the wind speed measurement and the nonlinearities in the aerodynamic torque such that the overall model of the turbine is non...... of the measurement with a closed set that is computed based on the past measurements and a model of the system. If the measurement is not consistent with this set, a fault is detected. The result demonstrates effectiveness of the method for fault detection of the benchmark wind turbine....

  2. Power plant fault detection using artificial neural network

    Science.gov (United States)

    Thanakodi, Suresh; Nazar, Nazatul Shiema Moh; Joini, Nur Fazriana; Hidzir, Hidzrin Dayana Mohd; Awira, Mohammad Zulfikar Khairul

    2018-02-01

    The fault that commonly occurs in power plants is due to various factors that affect the system outage. There are many types of faults in power plants such as single line to ground fault, double line to ground fault, and line to line fault. The primary aim of this paper is to diagnose the fault in 14 buses power plants by using an Artificial Neural Network (ANN). The Multilayered Perceptron Network (MLP) that detection trained utilized the offline training methods such as Gradient Descent Backpropagation (GDBP), Levenberg-Marquardt (LM), and Bayesian Regularization (BR). The best method is used to build the Graphical User Interface (GUI). The modelling of 14 buses power plant, network training, and GUI used the MATLAB software.

  3. Fault Management: Degradation Signature Detection, Modeling, and Processing Project

    Data.gov (United States)

    National Aeronautics and Space Administration — Fault to Failure Progression (FFP) signature modeling and processing is a new method for applying condition-based signal data to detect degradation, to identify...

  4. Fault Management: Degradation Signature Detection, Modeling, and Processing, Phase I

    Data.gov (United States)

    National Aeronautics and Space Administration — Fault to Failure Progression (FFP) signature modeling and processing is a new method for applying condition-based signal data to detect degradation, to identify...

  5. Fault Detection and Correction for the Solar Dynamics Observatory Attitude Control System

    Science.gov (United States)

    Starin, Scott R.; Vess, Melissa F.; Kenney, Thomas M.; Maldonado, Manuel D.; Morgenstern, Wendy M.

    2007-01-01

    The Solar Dynamics Observatory is an Explorer-class mission that will launch in early 2009. The spacecraft will operate in a geosynchronous orbit, sending data 24 hours a day to a devoted ground station in White Sands, New Mexico. It will carry a suite of instruments designed to observe the Sun in multiple wavelengths at unprecedented resolution. The Atmospheric Imaging Assembly includes four telescopes with focal plane CCDs that can image the full solar disk in four different visible wavelengths. The Extreme-ultraviolet Variability Experiment will collect time-correlated data on the activity of the Sun's corona. The Helioseismic and Magnetic Imager will enable study of pressure waves moving through the body of the Sun. The attitude control system on Solar Dynamics Observatory is responsible for four main phases of activity. The physical safety of the spacecraft after separation must be guaranteed. Fine attitude determination and control must be sufficient for instrument calibration maneuvers. The mission science mode requires 2-arcsecond control according to error signals provided by guide telescopes on the Atmospheric Imaging Assembly, one of the three instruments to be carried. Lastly, accurate execution of linear and angular momentum changes to the spacecraft must be provided for momentum management and orbit maintenance. In thsp aper, single-fault tolerant fault detection and correction of the Solar Dynamics Observatory attitude control system is described. The attitude control hardware suite for the mission is catalogued, with special attention to redundancy at the hardware level. Four reaction wheels are used where any three are satisfactory. Four pairs of redundant thrusters are employed for orbit change maneuvers and momentum management. Three two-axis gyroscopes provide full redundancy for rate sensing. A digital Sun sensor and two autonomous star trackers provide two-out-of-three redundancy for fine attitude determination. The use of software to maximize

  6. Soft Computing Application in Fault Detection of Induction Motor

    International Nuclear Information System (INIS)

    Konar, P.; Puhan, P. S.; Chattopadhyay, P. Dr.

    2010-01-01

    The paper investigates the effectiveness of different patter classifier like Feed Forward Back Propagation (FFBPN), Radial Basis Function (RBF) and Support Vector Machine (SVM) for detection of bearing faults in Induction Motor. The steady state motor current with Park's Transformation has been used for discrimination of inner race and outer race bearing defects. The RBF neural network shows very encouraging results for multi-class classification problems and is hoped to set up a base for incipient fault detection of induction motor. SVM is also found to be a very good fault classifier which is highly competitive with RBF.

  7. 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.

  8. Fault detection and isolation in processes involving induction machines

    Energy Technology Data Exchange (ETDEWEB)

    Zell, K.; Medvedev, A. [Control Engineering Group, Luleaa University of Technology, Luleaa (Sweden)

    1997-12-31

    A model-based technique for fault detection and isolation in electro-mechanical systems comprising induction machines is introduced. Two coupled state observers, one for the induction machine and another for the mechanical load, are used to detect and recognize fault-specific behaviors (fault signatures) from the real-time measurements of the rotor angular velocity and terminal voltages and currents. Practical applicability of the method is verified in full-scale experiments with a conveyor belt drive at SSAB, Luleaa Works. (orig.) 3 refs.

  9. Fault detection of gearbox using time-frequency method

    Science.gov (United States)

    Widodo, A.; Satrijo, Dj.; Prahasto, T.; Haryanto, I.

    2017-04-01

    This research deals with fault detection and diagnosis of gearbox by using vibration signature. In this work, fault detection and diagnosis are approached by employing time-frequency method, and then the results are compared with cepstrum analysis. Experimental work has been conducted for data acquisition of vibration signal thru self-designed gearbox test rig. This test-rig is able to demonstrate normal and faulty gearbox i.e., wears and tooth breakage. Three accelerometers were used for vibration signal acquisition from gearbox, and optical tachometer was used for shaft rotation speed measurement. The results show that frequency domain analysis using fast-fourier transform was less sensitive to wears and tooth breakage condition. However, the method of short-time fourier transform was able to monitor the faults in gearbox. Wavelet Transform (WT) method also showed good performance in gearbox fault detection using vibration signal after employing time synchronous averaging (TSA).

  10. Fault detection by surface seismic scanning tunneling macroscope: Field test

    KAUST Repository

    Hanafy, Sherif M.

    2014-08-05

    The seismic scanning tunneling macroscope (SSTM) is proposed for detecting the presence of near-surface impedance anomalies and faults. Results with synthetic data are consistent with theory in that scatterers closer to the surface provide brighter SSTM profiles than those that are deeper. The SSTM profiles show superresolution detection if the scatterers are in the near-field region of the recording line. The field data tests near Gulf of Aqaba, Haql, KSA clearly show the presence of the observable fault scarp, and identify the subsurface presence of the hidden faults indicated in the tomograms. Superresolution detection of the fault is achieved, even when the 35 Hz data are lowpass filtered to the 5-10 Hz band.

  11. Observer Based Detection of Sensor Faults in Wind Turbines

    DEFF Research Database (Denmark)

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

    2009-01-01

    An observer based scheme is proposed to detect sensor faults in wind  turbines. In the example used for the proposed scheme the wind turbine  drive train is considered. A model of the drive train is used to  design the observer, and in this model the wind speed is an important  input, however......, 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....

  12. Multi-directional fault detection system

    Science.gov (United States)

    Archer, Charles Jens; Pinnow, Kurt Walter; Ratterman, Joseph D.; Smith, Brian Edward

    2010-06-29

    An apparatus, program product and method checks for nodal faults in a group of nodes comprising a center node and all adjacent nodes. The center node concurrently communicates with the immediately adjacent nodes in three dimensions. The communications are analyzed to determine a presence of a faulty node or connection.

  13. Multi-directional fault detection system

    Science.gov (United States)

    Archer, Charles Jens [Rochester, MN; Pinnow, Kurt Walter [Rochester, MN; Ratterman, Joseph D [Rochester, MN; Smith, Brian Edward [Rochester, MN

    2009-03-17

    An apparatus, program product and method checks for nodal faults in a group of nodes comprising a center node and all adjacent nodes. The center node concurrently communicates with the immediately adjacent nodes in three dimensions. The communications are analyzed to determine a presence of a faulty node or connection.

  14. Fault Tolerant Position-mooring Control for Offshore Vessels

    DEFF Research Database (Denmark)

    Blanke, Mogens; Nguyen, Trong Dong

    2018-01-01

    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......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...

  15. 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...

  16. FaultBuster: data driven fault detection and diagnosis for industrial systems

    DEFF Research Database (Denmark)

    Bergantino, Nicola; Caponetti, Fabio; Longhi, Sauro

    2009-01-01

    . Multivariate statistical models based on principal components are used to detect abnormal situations. Tailored to alarms, a probabilistic inference engine process the fault evidences to output the most probable diagnosis. Results from the DX 09 Diagnostic Challenge shown strong detection properties, while...

  17. Development of a morphological convolution operator for bearing fault detection

    Science.gov (United States)

    Li, Yifan; Liang, Xihui; Liu, Weiwei; Wang, Yan

    2018-05-01

    This paper presents a novel signal processing scheme, namely morphological convolution operator (MCO) lifted morphological undecimated wavelet (MUDW), for rolling element bearing fault detection. In this scheme, a MCO is first designed to fully utilize the advantage of the closing & opening gradient operator and the closing-opening & opening-closing gradient operator for feature extraction as well as the merit of excellent denoising characteristics of the convolution operator. The MCO is then introduced into MUDW for the purpose of improving the fault detection ability of the reported MUDWs. Experimental vibration signals collected from a train wheelset test rig and the bearing data center of Case Western Reserve University are employed to evaluate the effectiveness of the proposed MCO lifted MUDW on fault detection of rolling element bearings. The results show that the proposed approach has a superior performance in extracting fault features of defective rolling element bearings. In addition, comparisons are performed between two reported MUDWs and the proposed MCO lifted MUDW. The MCO lifted MUDW outperforms both of them in detection of outer race faults and inner race faults of rolling element bearings.

  18. Fault detection and reliability, knowledge based and other approaches

    International Nuclear Information System (INIS)

    Singh, M.G.; Hindi, K.S.; Tzafestas, S.G.

    1987-01-01

    These proceedings are split up into four major parts in order to reflect the most significant aspects of reliability and fault detection as viewed at present. The first part deals with knowledge-based systems and comprises eleven contributions from leading experts in the field. The emphasis here is primarily on the use of artificial intelligence, expert systems and other knowledge-based systems for fault detection and reliability. The second part is devoted to fault detection of technological systems and comprises thirteen contributions dealing with applications of fault detection techniques to various technological systems such as gas networks, electric power systems, nuclear reactors and assembly cells. The third part of the proceedings, which consists of seven contributions, treats robust, fault tolerant and intelligent controllers and covers methodological issues as well as several applications ranging from nuclear power plants to industrial robots to steel grinding. The fourth part treats fault tolerant digital techniques and comprises five contributions. Two papers, one on reactor noise analysis, the other on reactor control system design, are indexed separately. (author)

  19. Fault Detection and Isolation and Fault Tolerant Control of Wind Turbines Using Set-Valued Observers

    DEFF Research Database (Denmark)

    Casau, Pedro; Rosa, Paulo Andre Nobre; Tabatabaeipour, Seyed Mojtaba

    2012-01-01

    and Isolation (FDI) and Fault Tolerant Control (FTC) of wind turbines, by taking advantage of the recent advances in SVO theory for model invalidation. A simple wind turbine model is presented along with possible faulty scenarios. The FDI algorithm is built on top of the described model, taking into account......Research on wind turbine Operations & Maintenance (O&M) procedures is critical to the expansion of Wind Energy Conversion systems (WEC). In order to reduce O&M costs and increase the lifespan of the turbine, we study the application of Set-Valued Observers (SVO) to the problem of Fault Detection...... process disturbances, uncertainty and sensor noise. The FTC strategy takes advantage of the proposed FDI algorithm, enabling the controller reconfiguration shortly after fault events. Additionally, a robust controller is designed so as to increase the wind turbine's performance during low severity faults...

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

    Energy Technology Data Exchange (ETDEWEB)

    Cheung, Howard; Braun, James E.

    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.

  1. Evaluation of Wind Farm Controller based Fault Detection and Isolation

    DEFF Research Database (Denmark)

    Odgaard, Peter Fogh; Shafiei, Seyed Ehsan

    2015-01-01

    In the process of lowering cost of energy of power generated by wind turbines, some focus has been drawn towards fault detection and isolation and as well as fault tolerant control of wind turbines with the purpose of increasing reliability and availability of the wind turbines. Most modern wind...... turbine projects are organized in wind farms, in which controllers are available at wind farm level as well in the individual wind turbines. Different information are available at these levels, and the information is as well sampled with different sample frequencies. A benchmark model for wind farm fault...... detection and isolation and fault tolerant control has previously been proposed. Based on this model, and international competition on wind farm FDI was organized. The contributions were presented at the IFAC World Congress 2014. In this paper the top three contributions to this competition are shortly...

  2. 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...

  3. A Generalized Machine Fault Detection Method Using Unified Change Detection

    Science.gov (United States)

    2014-10-02

    faults are used for the demonstration. The fault types include gear tooth cracks in simple gearboxes; non-uniform gear tooth wear and vane pump...the speed-dependent pulse amplitude, which may cause the misalignment of averaged signals by more than one sample point. Taking gear tooth cracking ...as an example fault type, we will start with two actual signals acquired in a gear tooth crack propagation test conducted at the Defence Science

  4. Detecting Faults In High-Voltage Transformers

    Science.gov (United States)

    Blow, Raymond K.

    1988-01-01

    Simple fixture quickly shows whether high-voltage transformer has excessive voids in dielectric materials and whether high-voltage lead wires too close to transformer case. Fixture is "go/no-go" indicator; corona appears if transformer contains such faults. Nests in wire mesh supported by cap of clear epoxy. If transformer has defects, blue glow of corona appears in mesh and is seen through cap.

  5. Soft-Fault Detection Technologies Developed for Electrical Power Systems

    Science.gov (United States)

    Button, Robert M.

    2004-01-01

    The NASA Glenn Research Center, partner universities, and defense contractors are working to develop intelligent power management and distribution (PMAD) technologies for future spacecraft and launch vehicles. The goals are to provide higher performance (efficiency, transient response, and stability), higher fault tolerance, and higher reliability through the application of digital control and communication technologies. It is also expected that these technologies will eventually reduce the design, development, manufacturing, and integration costs for large, electrical power systems for space vehicles. The main focus of this research has been to incorporate digital control, communications, and intelligent algorithms into power electronic devices such as direct-current to direct-current (dc-dc) converters and protective switchgear. These technologies, in turn, will enable revolutionary changes in the way electrical power systems are designed, developed, configured, and integrated in aerospace vehicles and satellites. Initial successes in integrating modern, digital controllers have proven that transient response performance can be improved using advanced nonlinear control algorithms. One technology being developed includes the detection of "soft faults," those not typically covered by current systems in use today. Soft faults include arcing faults, corona discharge faults, and undetected leakage currents. Using digital control and advanced signal analysis algorithms, we have shown that it is possible to reliably detect arcing faults in high-voltage dc power distribution systems (see the preceding photograph). Another research effort has shown that low-level leakage faults and cable degradation can be detected by analyzing power system parameters over time. This additional fault detection capability will result in higher reliability for long-lived power systems such as reusable launch vehicles and space exploration missions.

  6. Automated Fault Detection for DIII-D Tokamak Experiments

    Energy Technology Data Exchange (ETDEWEB)

    Walker, M.L.; Scoville, J.T.; Johnson, R.D.; Hyatt, A.W.; Lee, J.

    1999-11-01

    An automated fault detection software system has been developed and was used during 1999 DIII-D plasma operations. The Fault Identification and Communication System (FICS) executes automatically after every plasma discharge to check dozens of subsystems for proper operation and communicates the test results to the tokamak operator. This system is now used routinely during DIII-D operations and has led to an increase in tokamak productivity.

  7. 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....

  8. Generic, scalable and decentralized fault detection for robot swarms.

    Science.gov (United States)

    Tarapore, Danesh; Christensen, Anders Lyhne; Timmis, Jon

    2017-01-01

    Robot swarms are large-scale multirobot systems with decentralized control which means that each robot acts based only on local perception and on local coordination with neighboring robots. The decentralized approach to control confers number of potential benefits. In particular, inherent scalability and robustness are often highlighted as key distinguishing features of robot swarms compared with systems that rely on traditional approaches to multirobot coordination. It has, however, been shown that swarm robotics systems are not always fault tolerant. To realize the robustness potential of robot swarms, it is thus essential to give systems the capacity to actively detect and accommodate faults. In this paper, we present a generic fault-detection system for robot swarms. We show how robots with limited and imperfect sensing capabilities are able to observe and classify the behavior of one another. In order to achieve this, the underlying classifier is an immune system-inspired algorithm that learns to distinguish between normal behavior and abnormal behavior online. Through a series of experiments, we systematically assess the performance of our approach in a detailed simulation environment. In particular, we analyze our system's capacity to correctly detect robots with faults, false positive rates, performance in a foraging task in which each robot exhibits a composite behavior, and performance under perturbations of the task environment. Results show that our generic fault-detection system is robust, that it is able to detect faults in a timely manner, and that it achieves a low false positive rate. The developed fault-detection system has the potential to enable long-term autonomy for robust multirobot systems, thus increasing the usefulness of robots for a diverse repertoire of upcoming applications in the area of distributed intelligent automation.

  9. Gear fault detection using customized multiwavelet lifting schemes

    Science.gov (United States)

    Yuan, Jing; He, Zhengjia; Zi, Yanyang

    2010-07-01

    Fault symptoms of running gearboxes must be detected as early as possible to avoid serious accidents. Diverse advanced methods are developed for this challenging task. However, for multiwavelet transforms, the fixed basis functions independent of the input dynamic response signals will possibly reduce the accuracy of fault diagnosis. Meanwhile, for multiwavelet denoising technique, the universal threshold denoising tends to overkill important but weak features in gear fault diagnosis. To overcome the shortcoming, a novel method incorporating customized (i.e., signal-based) multiwavelet lifting schemes with sliding window denoising is proposed in this paper. On the basis of Hermite spline interpolation, various vector prediction and update operators with the desirable properties of biorthogonality, symmetry, short support and vanishing moments are constructed. The customized lifting-based multiwavelets for feature matching are chosen by the minimum entropy principle. Due to the periodic characteristics of gearbox vibration signals, sliding window denoising favorable to retain valuable information as much as possible is employed to extract and identify the fault features in gearbox signals. The proposed method is applied to simulation experiments, gear fault diagnosis and normal gear detection to testify the efficiency and reliability. The results show that the method involving the selection of appropriate basis functions and the proper feature extraction technique could act as an effective and promising tool for gear fault detection.

  10. Fault-weighted quantification method of fault detection coverage through fault mode and effect analysis in digital I&C systems

    Energy Technology Data Exchange (ETDEWEB)

    Cho, Jaehyun; Lee, Seung Jun, E-mail: sjlee420@unist.ac.kr; Jung, Wondea

    2017-05-15

    Highlights: • We developed the fault-weighted quantification method of fault detection coverage. • The method has been applied to specific digital reactor protection system. • The unavailability of the module had 20-times difference with the traditional method. • Several experimental tests will be effectively prioritized using this method. - Abstract: The one of the most outstanding features of a digital I&C system is the use of a fault-tolerant technique. With an awareness regarding the importance of thequantification of fault detection coverage of fault-tolerant techniques, several researches related to the fault injection method were developed and employed to quantify a fault detection coverage. In the fault injection method, each injected fault has a different importance because the frequency of realization of every injected fault is different. However, there have been no previous studies addressing the importance and weighting factor of each injected fault. In this work, a new method for allocating the weighting to each injected fault using the failure mode and effect analysis data was proposed. For application, the fault-weighted quantification method has also been applied to specific digital reactor protection system to quantify the fault detection coverage. One of the major findings in an application was that we may estimate the unavailability of the specific module in digital I&C systems about 20-times smaller than real value when we use a traditional method. The other finding was that we can also classify the importance of the experimental case. Therefore, this method is expected to not only suggest an accurate quantification procedure of fault-detection coverage by weighting the injected faults, but to also contribute to an effective fault injection experiment by sorting the importance of the failure categories.

  11. 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...

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

    Indian Academy of Sciences (India)

    In this paper, a method is proposed to detect and identify any degradation of sensor performance. The validation process consists of two steps: (i) residual generation and (ii) fault detection by residual evaluation.Singular value decomposition (SVD) and Euclidean distance (ED) methods are used to generate the residual ...

  13. Stochastic Change Detection based on an Active Fault Diagnosis Approach

    DEFF Research Database (Denmark)

    Poulsen, Niels Kjølstad; Niemann, Hans Henrik

    2007-01-01

    The focus in this paper is on stochastic change detection applied in connection with active fault diagnosis (AFD). An auxiliary input signal is applied in AFD. This signal injection in the system will in general allow to obtain a fast change detection/isolation by considering the output or an error...

  14. Aircraft Fault Detection and Classification Using Multi-Level Immune Learning Detection

    Science.gov (United States)

    Wong, Derek; Poll, Scott; KrishnaKumar, Kalmanje

    2005-01-01

    This work is an extension of a recently developed software tool called MILD (Multi-level Immune Learning Detection), which implements a negative selection algorithm for anomaly and fault detection that is inspired by the human immune system. The immunity-based approach can detect a broad spectrum of known and unforeseen faults. We extend MILD by applying a neural network classifier to identify the pattern of fault detectors that are activated during fault detection. Consequently, MILD now performs fault detection and identification of the system under investigation. This paper describes the application of MILD to detect and classify faults of a generic transport aircraft augmented with an intelligent flight controller. The intelligent control architecture is designed to accommodate faults without the need to explicitly identify them. Adding knowledge about the existence and type of a fault will improve the handling qualities of a degraded aircraft and impact tactical and strategic maneuvering decisions. In addition, providing fault information to the pilot is important for maintaining situational awareness so that he can avoid performing an action that might lead to unexpected behavior - e.g., an action that exceeds the remaining control authority of the damaged aircraft. We discuss the detection and classification results of simulated failures of the aircraft's control system and show that MILD is effective at determining the problem with low false alarm and misclassification rates.

  15. Optimization of Second Fault Detection Thresholds to Maximize Mission POS

    Science.gov (United States)

    Anzalone, Evan

    2018-01-01

    In order to support manned spaceflight safety requirements, the Space Launch System (SLS) has defined program-level requirements for key systems to ensure successful operation under single fault conditions. To accommodate this with regards to Navigation, the SLS utilizes an internally redundant Inertial Navigation System (INS) with built-in capability to detect, isolate, and recover from first failure conditions and still maintain adherence to performance requirements. The unit utilizes multiple hardware- and software-level techniques to enable detection, isolation, and recovery from these events in terms of its built-in Fault Detection, Isolation, and Recovery (FDIR) algorithms. Successful operation is defined in terms of sufficient navigation accuracy at insertion while operating under worst case single sensor outages (gyroscope and accelerometer faults at launch). In addition to first fault detection and recovery, the SLS program has also levied requirements relating to the capability of the INS to detect a second fault, tracking any unacceptable uncertainty in knowledge of the vehicle's state. This detection functionality is required in order to feed abort analysis and ensure crew safety. Increases in navigation state error and sensor faults can drive the vehicle outside of its operational as-designed environments and outside of its performance envelope causing loss of mission, or worse, loss of crew. The criteria for operation under second faults allows for a larger set of achievable missions in terms of potential fault conditions, due to the INS operating at the edge of its capability. As this performance is defined and controlled at the vehicle level, it allows for the use of system level margins to increase probability of mission success on the operational edges of the design space. Due to the implications of the vehicle response to abort conditions (such as a potentially failed INS), it is important to consider a wide range of failure scenarios in terms of

  16. Data driven fault detection and isolation: a wind turbine scenario

    Directory of Open Access Journals (Sweden)

    Rubén Francisco Manrique Piramanrique

    2015-04-01

    Full Text Available One of the greatest drawbacks in wind energy generation is the high maintenance cost associated to mechanical faults. This problem becomes more evident in utility scale wind turbines, where the increased size and nominal capacity comes with additional problems associated with structural vibrations and aeroelastic effects in the blades. Due to the increased operation capability, it is imperative to detect system degradation and faults in an efficient manner, maintaining system integrity, reliability and reducing operation costs. This paper presents a comprehensive comparison of four different Fault Detection and Isolation (FDI filters based on “Data Driven” (DD techniques. In order to enhance FDI performance, a multi-level strategy is used where:  the first level detects the occurrence of any given fault (detection, while  the second identifies the source of the fault (isolation. Four different DD classification techniques (namely Support Vector Machines, Artificial Neural Networks, K Nearest Neighbors and Gaussian Mixture Models were studied and compared for each of the proposed classification levels. The best strategy at each level could be selected to build the final data driven FDI system. The performance of the proposed scheme is evaluated on a benchmark model of a commercial wind turbine. 

  17. 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.

  18. 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...

  19. Implementation of a model based fault detection and diagnosis for actuation faults of the Space Shuttle main engine

    Science.gov (United States)

    Duyar, A.; Guo, T.-H.; Merrill, W.; Musgrave, J.

    1992-01-01

    In a previous study, Guo, Merrill and Duyar, 1990, reported a conceptual development of a fault detection and diagnosis system for actuation faults of the space shuttle main engine. This study, which is a continuation of the previous work, implements the developed fault detection and diagnosis scheme for the real time actuation fault diagnosis of the space shuttle main engine. The scheme will be used as an integral part of an intelligent control system demonstration experiment at NASA Lewis. The diagnosis system utilizes a model based method with real time identification and hypothesis testing for actuation, sensor, and performance degradation faults.

  20. Nondestructive detection system of faults in fuses using radioisotope

    International Nuclear Information System (INIS)

    Goncalves, D.

    1973-01-01

    A system is developed to show the viability of non-destructive detection of the faults of explosive safety fuses which are manufactured by Fabrica da Estrela do Ministerio do Exercito. The faults are detected by an ion-chamber based on the variation of the intensity of the beta particles that penetrate the fuse which passes through a collimator. The beta particles are emitted by Strontium-90 + Yttrium-90 encapsulated in either stainless steel or aluminum. The concept of 'bucking Voltage' is applied to differentiate electronically the signal generated by the ion-chamber. (author)

  1. Detecting Faults By Use Of Hidden Markov Models

    Science.gov (United States)

    Smyth, Padhraic J.

    1995-01-01

    Frequency of false alarms reduced. Faults in complicated dynamic system (e.g., antenna-aiming system, telecommunication network, or human heart) detected automatically by method of automated, continuous monitoring. Obtains time-series data by sampling multiple sensor outputs at discrete intervals of t and processes data via algorithm determining whether system in normal or faulty state. Algorithm implements, among other things, hidden first-order temporal Markov model of states of system. Mathematical model of dynamics of system not needed. Present method is "prior" method mentioned in "Improved Hidden-Markov-Model Method of Detecting Faults" (NPO-18982).

  2. Development of Eddy Current Technique for the Detection of Stress Corrosion Cracking in Space Shuttle Primary Reaction Control Thrusters

    Science.gov (United States)

    Wincheski, Buzz; Simpson, John; Koshti, Ajay

    2006-01-01

    A recent identification of stress corrosion cracking in the Space Shuttle Primary Reaction Control System (PRCS) thrusters triggered an extensive nondestructive evaluation effort to develop techniques capable of identifying such damage on installed shuttle hardware. As a part of this effort, specially designed eddy current probes inserted into the acoustic cavity were explored for the detection of such flaws and for evaluation of the remaining material between the crack tip and acoustic cavity. The technique utilizes two orthogonal eddy current probes which are scanned under stepper motor control in the acoustic cavity to identify cracks hidden with as much as 0.060 remaining wall thickness to the cavity. As crack growth rates in this area have been determined to be very slow, such an inspection provides a large safety margin for continued operation of the critical shuttle hardware. Testing has been performed on thruster components with both actual and fabricated defects. This paper will review the design and performance of the developed eddy current inspection system. Detection of flaws as a function of remaining wall thickness will be presented along with the proposed system configuration for depot level or on-vehicle inspection capabilities.

  3. Rotors fault detection using vibration methods

    Directory of Open Access Journals (Sweden)

    Andrzej GRZADZIELA

    2009-01-01

    Full Text Available Ships’ propulsion plant usually works in a hard environment caused by static forces and permanent dynamic loads. Basic elements of propulsion systems are rotation machines like gas turbine engines, gear boxes, propulsion shafts etc. Another loads coming from technological faults of rotation machines like misalignment, unbalancing or resonance. Exciding of tolerated values of shaft alignments or unbalancing can cause a damage of radial and thrust bearings in relative short time. Similar situation is occurred when the mode or modes of rotors natural resonances are in the range of operational speed. The paper compares three methods of calculating and recognizing modes of rotors’ natural frequencies using laboratory model of rotational machine. Results of FEM modeling, modal hammers measurements and synchronous vibration measurement show that free stop-down process is an interesting area for the vibration diagnosing of rotational machines.

  4. 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.

  5. Bayesian fault detection and isolation using Field Kalman Filter

    Science.gov (United States)

    Baranowski, Jerzy; Bania, Piotr; Prasad, Indrajeet; Cong, Tian

    2017-12-01

    Fault detection and isolation is crucial for the efficient operation and safety of any industrial process. There is a variety of methods from all areas of data analysis employed to solve this kind of task, such as Bayesian reasoning and Kalman filter. In this paper, the authors use a discrete Field Kalman Filter (FKF) to detect and recognize faulty conditions in a system. The proposed approach, devised for stochastic linear systems, allows for analysis of faults that can be expressed both as parameter and disturbance variations. This approach is formulated for the situations when the fault catalog is known, resulting in the algorithm allowing estimation of probability values. Additionally, a variant of algorithm with greater numerical robustness is presented, based on computation of logarithmic odds. Proposed algorithm operation is illustrated with numerical examples, and both its merits and limitations are critically discussed and compared with traditional EKF.

  6. Fault Detection in Coal Mills used in Power Plants

    DEFF Research Database (Denmark)

    Odgaard, Peter Fogh; Mataji, Babak

    2006-01-01

    In order to achieve high performance and efficiency of coal-fired power plants, it is highly important to control the coal flow into the furnace in the power plant. This means suppression of disturbances and force the coal mill to deliver the required coal flow, as well as monitor the coal mill...... in order to detect faults in the coal mill when they emerge. This paper deals with the second objective. Based on a simple dynamic model of the energy balance a residual is formed for the coal mill. An optimal unknown input observer is designed to estimate this residual. The estimated residual is following...... tested on measured data of a fault in a coal mill, it can hereby be concluded that this residual is very useful for detecting faults in the coal mill....

  7. Fuzzy model-based observers for fault detection in CSTR.

    Science.gov (United States)

    Ballesteros-Moncada, Hazael; Herrera-López, Enrique J; Anzurez-Marín, Juan

    2015-11-01

    Under the vast variety of fuzzy model-based observers reported in the literature, what would be the properone to be used for fault detection in a class of chemical reactor? In this study four fuzzy model-based observers for sensor fault detection of a Continuous Stirred Tank Reactor were designed and compared. The designs include (i) a Luenberger fuzzy observer, (ii) a Luenberger fuzzy observer with sliding modes, (iii) a Walcott-Zak fuzzy observer, and (iv) an Utkin fuzzy observer. A negative, an oscillating fault signal, and a bounded random noise signal with a maximum value of ±0.4 were used to evaluate and compare the performance of the fuzzy observers. The Utkin fuzzy observer showed the best performance under the tested conditions. Copyright © 2015 ISA. Published by Elsevier Ltd. All rights reserved.

  8. 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

    , namely freeze-over/dirty built-up, is considered for one heat transfer coefficient between the inside air and the evaporator surface. For fault detection purpose, the fault residual is generated through a KF and then evaluated through CUSUM method. All fault scenarios can be detected clearly. For fault...

  9. 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 we...

  10. Fault detection in reciprocating compressor valves under varying load conditions

    Science.gov (United States)

    Pichler, Kurt; Lughofer, Edwin; Pichler, Markus; Buchegger, Thomas; Klement, Erich Peter; Huschenbett, Matthias

    2016-03-01

    This paper presents a novel approach for detecting cracked or broken reciprocating compressor valves under varying load conditions. The main idea is that the time frequency representation of vibration measurement data will show typical patterns depending on the fault state. The problem is to detect these patterns reliably. For the detection task, we make a detour via the two dimensional autocorrelation. The autocorrelation emphasizes the patterns and reduces noise effects. This makes it easier to define appropriate features. After feature extraction, classification is done using logistic regression and support vector machines. The method's performance is validated by analyzing real world measurement data. The results will show a very high detection accuracy while keeping the false alarm rates at a very low level for different compressor loads, thus achieving a load-independent method. The proposed approach is, to our best knowledge, the first automated method for reciprocating compressor valve fault detection that can handle varying load conditions.

  11. Fault Detection in Surface PMSM with Applications to Heavy Hybrid Vehicles

    OpenAIRE

    Johnson, Scott; Meyer, Richard T; DeCarlo, Raymond A.; Pekarek, Steve

    2016-01-01

    This report explores detecting inter-turn short circuit (ITSC) faults in surface permanent magnet synchronous machines (SPMSM). ITSC faults are caused by electrical insulation failures in the stator windings and can lead to shorts to ground and even fires. This report proposes methods for detecting these faults using a moving horizon observer (MHO) to reduce the chance of electrical shocks and fires. Specifically, this report constructs a MHO for ITSC fault detection in SPMSM. ITSC fault t...

  12. Optimal input design for fault detection and diagnosis

    DEFF Research Database (Denmark)

    Sadegh, Payman; Madsen, Henrik; Holst, J.

    1995-01-01

    In the paper, the design of optimal input signals for detection and diagnosis in a stochastic dynamical system is investigated. The design is based on maximization of Kullback measure between the model under fault and the model under normal operation conditions. It is established that the optimal...

  13. Fault detection for nonlinear systems - A standard problem approach

    DEFF Research Database (Denmark)

    Stoustrup, Jakob; Niemann, Hans Henrik

    1998-01-01

    The paper describes a general method for designing (nonlinear) fault detection and isolation (FDI) systems for nonlinear processes. For a rich class of nonlinear systems, a nonlinear FDI system can be designed using convex optimization procedures. The proposed method is a natural extension of met...

  14. 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...

  15. Fault Detection Using the Zero Crossing Rate | Osuagwu | Nigerian ...

    African Journals Online (AJOL)

    A method of fault detection based on the zero crossing rate of the signal, Z1, and the zero crossing rate of the first order difference signal. Z2, is presented. It is shown that the parameter pair (Z1, Z2) possesses adequate discriminating potential to classify a signature as good or defective. The parameter pair also carries ...

  16. Use of Sparse Principal Component Analysis (SPCA) for Fault Detection

    DEFF Research Database (Denmark)

    Gajjar, Shriram; Kulahci, Murat; Palazoglu, Ahmet

    2016-01-01

    Principal component analysis (PCA) has been widely used for data dimension reduction and process fault detection. However, interpreting the principal components and the outcomes of PCA-based monitoring techniques is a challenging task since each principal component is a linear combination of the ...

  17. An application of LTR design in fault detection

    DEFF Research Database (Denmark)

    Niemann, Hans Henrik

    1998-01-01

    The fault detection and isolation (FDI) problem is considered in this paper. The FDI problem is formulated as a filter design problem, where the faults in the system is estimated and the disturbance acting on the system is rejected. It turns out that the filter design problem can be considered...... as a standard Loop Transfer Recovery (LTR) design problem. As a consequence of the connection between LTR and FDI design, it is shown in an example how the LQG/LTR design method for full order and a proportional-integral observer can be applied with advantages in connection with FDI....

  18. Fault Detection and Performance Monitoring in PV Systems

    OpenAIRE

    Vasco Brogueira Andrade

    2017-01-01

    Given the exponential growth of the PV sector in recent years and the market?s overall need for new PV monitoring solutions, this dissertation aims at creating an automatic fault detection tool for PV systems, more specifically for shading and soiling situations. By detecting deviations in the measured PV systems? data patterns, this tool aims at providing essential information for the deployment of the right maintenance strategy for each situation.

  19. Observer Based Fault Detection and Moisture Estimating in Coal Mill

    DEFF Research Database (Denmark)

    Odgaard, Peter Fogh; Mataji, Babak

    2008-01-01

    In this paper an observer-based method for detecting faults and estimating moisture content in the coal in coal mills is presented. Handling of faults and operation under special conditions, such as high moisture content in the coal, are of growing importance due to the increasing...... requirements to the general performance of power plants. Detection  of faults and moisture content estimation are consequently of high interest in the handling of the problems caused by faults and moisture content. The coal flow out of the mill is the obvious variable to monitor, when detecting non-intended drops in the coal...... flow out of the coal mill. However, this variable is not measurable. Another estimated variable is the moisture content, which is only "measurable" during steady-state operations of the coal mill. Instead, this paper suggests a method where these unknown variables are estimated based on a simple energy...

  20. Battery Fault Detection with Saturating Transformers

    Science.gov (United States)

    Davies, Francis J. (Inventor); Graika, Jason R. (Inventor)

    2013-01-01

    A battery monitoring system utilizes a plurality of transformers interconnected with a battery having a plurality of battery cells. Windings of the transformers are driven with an excitation waveform whereupon signals are responsively detected, which indicate a health of the battery. In one embodiment, excitation windings and sense windings are separately provided for the plurality of transformers such that the excitation waveform is applied to the excitation windings and the signals are detected on the sense windings. In one embodiment, the number of sense windings and/or excitation windings is varied to permit location of underperforming battery cells utilizing a peak voltage detector.

  1. Statistical Feature Extraction for Fault Locations in Nonintrusive Fault Detection of Low Voltage Distribution Systems

    Directory of Open Access Journals (Sweden)

    Hsueh-Hsien Chang

    2017-04-01

    Full Text Available This paper proposes statistical feature extraction methods combined with artificial intelligence (AI approaches for fault locations in non-intrusive single-line-to-ground fault (SLGF detection of low voltage distribution systems. The input features of the AI algorithms are extracted using statistical moment transformation for reducing the dimensions of the power signature inputs measured by using non-intrusive fault monitoring (NIFM techniques. The data required to develop the network are generated by simulating SLGF using the Electromagnetic Transient Program (EMTP in a test system. To enhance the identification accuracy, these features after normalization are given to AI algorithms for presenting and evaluating in this paper. Different AI techniques are then utilized to compare which identification algorithms are suitable to diagnose the SLGF for various power signatures in a NIFM system. The simulation results show that the proposed method is effective and can identify the fault locations by using non-intrusive monitoring techniques for low voltage distribution systems.

  2. Nonlinear observer based fault detection and isolation for a momentum wheel

    DEFF Research Database (Denmark)

    Jensen, Hans-Christian Becker; Wisniewski, Rafal

    2001-01-01

    This article realizes nonlinear Fault Detection and Isolation for a momentum wheel. The Fault Detection and Isolation is based on a Failure Mode and Effect Analysis, which states which faults might occur and can be detected. The algorithms presented in this paper are based on a geometric approach...... toachieve nonlinear Fault Detection and Isolation. The proposed algorithms are tested in a simulation study and the pros and cons of the algorithm are discussed....

  3. PCB Fault Detection Using Image Processing

    Science.gov (United States)

    Nayak, Jithendra P. R.; Anitha, K.; Parameshachari, B. D., Dr.; Banu, Reshma, Dr.; Rashmi, P.

    2017-08-01

    The importance of the Printed Circuit Board inspection process has been magnified by requirements of the modern manufacturing environment where delivery of 100% defect free PCBs is the expectation. To meet such expectations, identifying various defects and their types becomes the first step. In this PCB inspection system the inspection algorithm mainly focuses on the defect detection using the natural images. Many practical issues like tilt of the images, bad light conditions, height at which images are taken etc. are to be considered to ensure good quality of the image which can then be used for defect detection. Printed circuit board (PCB) fabrication is a multidisciplinary process, and etching is the most critical part in the PCB manufacturing process. The main objective of Etching process is to remove the exposed unwanted copper other than the required circuit pattern. In order to minimize scrap caused by the wrongly etched PCB panel, inspection has to be done in early stage. However, all of the inspections are done after the etching process where any defective PCB found is no longer useful and is simply thrown away. Since etching process costs 0% of the entire PCB fabrication, it is uneconomical to simply discard the defective PCBs. In this paper a method to identify the defects in natural PCB images and associated practical issues are addressed using Software tools and some of the major types of single layer PCB defects are Pattern Cut, Pin hole, Pattern Short, Nick etc., Therefore the defects should be identified before the etching process so that the PCB would be reprocessed. In the present approach expected to improve the efficiency of the system in detecting the defects even in low quality images

  4. Nonlinear observer based fault detection and isolation for a momentum wheel

    DEFF Research Database (Denmark)

    Jensen, Hans-Christian Becker; Wisniewski, Rafal

    2001-01-01

    This article realizes nonlinear Fault Detection and Isolation for a momentum wheel. The Fault Detection and Isolation is based on a Failure Mode and Effect Analysis, which states which faults might occur and can be detected. The algorithms presented in this paper are based on a geometric approach...

  5. Application of Hermitian wavelet to crack fault detection in gearbox

    Science.gov (United States)

    Li, Hui; Zhang, Yuping; Zheng, Haiqi

    2011-05-01

    The continuous wavelet transform enables one to look at the evolution in the time scale joint representation plane. This advantage makes it very suitable for the detection of singularity generated by localized defects in the mechanical system. However, most of the applications of the continuous wavelet transform have widely focused on the use of Morlet wavelet transform. The complex Hermitian wavelet is constructed based on the first and the second derivatives of the Gaussian function to detect signal singularities. The Fourier spectrum of Hermitian wavelet is real; therefore, Hermitian wavelet does not affect the phase of a signal in the complex domain. This gives a desirable ability to extract the singularity characteristic of a signal precisely. In this study, Hermitian wavelet is used to diagnose the gear localized crack fault. The simulative and experimental results show that Hermitian wavelet can extract the transients from strong noise signals and can effectively diagnose the localized gear fault.

  6. Integration of Fault Detection and Isolation with Control Using Neuro-fuzzy Scheme

    Directory of Open Access Journals (Sweden)

    A. Asokan

    2009-10-01

    Full Text Available In this paper an algorithms is developed for fault diagnosis and fault tolerant control strategy for nonlinear systems subjected to an unknown time-varying fault. At first, the design of fault diagnosis scheme is performed using model based fault detection technique. The neuro-fuzzy chi-square scheme is applied for fault detection and isolation. The fault magnitude and time of occurrence of fault is obtained through neuro-fuzzy chi-square scheme. The estimated magnitude of the fault magnitude is normalized and used by the feed-forward control algorithm to make appropriate changes in the manipulated variable to keep the controlled variable near its set value. The feed-forward controller acts along with feed-back controller to control the multivariable system. The performance of the proposed scheme is applied to a three- tank process for various types of fault inputs to show the effectiveness of the proposed approach.

  7. Microelectrospray Thrusters

    Science.gov (United States)

    Dankanich, John; Demmons, Nate; Marrese-Reading, Colleen; Lozano, Paulo

    2015-01-01

    Propulsion technology is often a critical enabling technology for space missions. NASA is investing in technologies to enable high value missions with very small spacecraft, even CubeSats. However, these nanosatellites currently lack any appreciable propulsion capability. CubeSats are typically deployed and tumble or drift without any ability to transfer to higher value orbits, perform orbit maintenance, or perform de-orbit. Larger spacecraft can also benefit from high precision attitude control systems. Existing practices include reaction wheels with lifetime concerns and system level complexity. Microelectrospray thrusters will provide new propulsion capabilities to address these mission needs. Electric propulsion is an approach to accelerate propellant to very high exhaust velocities through the use of electrical power. Typical propulsion systems are limited to the combustion energy available in the chemical bonds of the fuel and then acceleration through a converging diverging nozzle. However, electric propulsion can accelerate propellant to ten times higher velocities and therefore increase momentum transfer efficiency, or essentially, increase the fuel economy. Fuel efficiency of thrusters is proportional to the exhaust velocity and referred to as specific impulse (Isp). The state-of-the-art (SOA) for CubeSats is cold gas propulsion with an Isp of 50-80 s. The Space Shuttle main engine demonstrated a specific impulse of 450 s. The target Isp for the Mars Exploration Program (MEP) systems is >1,500 s. This propellant efficiency can enable a 1-kg, 10-cm cube to transfer from low-Earth orbit to interplanetary space with only 200 g of propellant. In September 2013, NASA's Game Changing Development program competitively awarded three teams with contracts to develop MEP systems from Technology Readiness Level-3 (TRL-3), experimental concept, to TRL-5, system validation in a relevant environment. The project is planned for 18 months of system development. Due to the

  8. 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.

  9. Using Order Tracking Analysis Method to Detect the Angle Faults of Blades on Wind Turbine

    DEFF Research Database (Denmark)

    Li, Pengfei; Hu, Weihao; Liu, Juncheng

    2016-01-01

    The angle faults of blades on wind turbines are usually included in the set angle fault and the pitch angle fault. They are occupied with a high proportion in all wind turbine faults. Compare with the traditional fault detection methods, using order tracking analysis method to detect angle faults...... has many advantages, such as easy implementation and high system reliability. Because of using Power Spectral Density method (PSD) or Fast Fourier Transform (FFT) method cannot get clear fault characteristic frequencies, this kind of faults should be detected by an effective method. This paper...... proposes a novel method of using order tracking analysis to analyze the signal of input aerodynamic torque which is received by hub. After the analyzed process, the fault characteristic frequency could be extracted by the analyzed signals and compared with the signals from normal operating conditions...

  10. Fault Detection of Wind Turbines with Uncertain Parameters

    DEFF Research Database (Denmark)

    Tabatabaeipour, Seyed Mojtaba; Odgaard, Peter Fogh; Bak, Thomas

    2012-01-01

    on the torque coefficient. High noise on the wind speed measurement, nonlinearities in the aerodynamic torque and uncertainties on the parameters make fault detection a challenging problem. We use an effective wind speed estimator to reduce the noise on the wind speed measurements. A set-membership approach...... is detected. For representation of these sets we use zonotopes and for modeling of uncertainties we use matrix zonotopes, which yields a computationally efficient algorithm. The method is applied to the wind turbine benchmark problem without and with uncertainties. The result demonstrates the effectiveness...

  11. A distributed fault-detection and diagnosis system using on-line parameter estimation

    Science.gov (United States)

    Guo, T.-H.; Merrill, W.; Duyar, A.

    1991-01-01

    The development of a model-based fault-detection and diagnosis system (FDD) is reviewed. The system can be used as an integral part of an intelligent control system. It determines the faults of a system from comparison of the measurements of the system with a priori information represented by the model of the system. The method of modeling a complex system is described and a description of diagnosis models which include process faults is presented. There are three distinct classes of fault modes covered by the system performance model equation: actuator faults, sensor faults, and performance degradation. A system equation for a complete model that describes all three classes of faults is given. The strategy for detecting the fault and estimating the fault parameters using a distributed on-line parameter identification scheme is presented. A two-step approach is proposed. The first step is composed of a group of hypothesis testing modules, (HTM) in parallel processing to test each class of faults. The second step is the fault diagnosis module which checks all the information obtained from the HTM level, isolates the fault, and determines its magnitude. The proposed FDD system was demonstrated by applying it to detect actuator and sensor faults added to a simulation of the Space Shuttle Main Engine. The simulation results show that the proposed FDD system can adequately detect the faults and estimate their magnitudes.

  12. Active-Varying Sampling-Based Fault Detection Filter Design for Networked Control Systems

    Directory of Open Access Journals (Sweden)

    Yu-Long Wang

    2014-01-01

    Full Text Available This paper is concerned with fault detection filter design for continuous-time networked control systems considering packet dropouts and network-induced delays. The active-varying sampling period method is introduced to establish a new discretized model for the considered networked control systems. The mutually exclusive distribution characteristic of packet dropouts and network-induced delays is made full use of to derive less conservative fault detection filter design criteria. Compared with the fault detection filter design adopting a constant sampling period, the proposed active-varying sampling-based fault detection filter design can improve the sensitivity of the residual signal to faults and shorten the needed time for fault detection. The simulation results illustrate the merits and effectiveness of the proposed fault detection filter design.

  13. 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.

  14. 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.

  15. 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

  16. 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)

  17. Incipient fault detection and power system protection for spaceborne systems

    Science.gov (United States)

    Russell, B. Don; Hackler, Irene M.

    1987-01-01

    A program was initiated to study the feasibility of using advanced terrestrial power system protection techniques for spacecraft power systems. It was designed to enhance and automate spacecraft power distribution systems in the areas of safety, reliability and maintenance. The proposed power management/distribution system is described as well as security assessment and control, incipient and low current fault detection, and the proposed spaceborne protection system. It is noted that the intelligent remote power controller permits the implementation of digital relaying algorithms with both adaptive and programmable characteristics.

  18. Neural adaptive observer-based sensor and actuator fault detection in nonlinear systems: Application in UAV.

    Science.gov (United States)

    Abbaspour, Alireza; Aboutalebi, Payam; Yen, Kang K; Sargolzaei, Arman

    2017-03-01

    A new online detection strategy is developed to detect faults in sensors and actuators of unmanned aerial vehicle (UAV) systems. In this design, the weighting parameters of the Neural Network (NN) are updated by using the Extended Kalman Filter (EKF). Online adaptation of these weighting parameters helps to detect abrupt, intermittent, and incipient faults accurately. We apply the proposed fault detection system to a nonlinear dynamic model of the WVU YF-22 unmanned aircraft for its evaluation. The simulation results show that the new method has better performance in comparison with conventional recurrent neural network-based fault detection strategies. Copyright © 2016 ISA. Published by Elsevier Ltd. All rights reserved.

  19. FAULT DETECTION AND LOCALIZATION IN MOTORCYCLES BASED ON THE CHAIN CODE OF PSEUDOSPECTRA AND ACOUSTIC SIGNALS

    Directory of Open Access Journals (Sweden)

    B. S. Anami

    2013-06-01

    Full Text Available Vehicles produce sound signals with varying temporal and spectral properties under different working conditions. These sounds are indicative of the condition of the engine. Fault diagnosis is a significantly difficult task in geographically remote places where expertise is scarce. Automated fault diagnosis can assist riders to assess the health condition of their vehicles. This paper presents a method for fault detection and location in motorcycles based on the chain code of the pseudospectra and Mel-frequency cepstral coefficient (MFCC features of acoustic signals. The work comprises two stages: fault detection and fault location. The fault detection stage uses the chain code of the pseudospectrum as a feature vector. If the motorcycle is identified as faulty, the MFCCs of the same sample are computed and used as features for fault location. Both stages employ dynamic time warping for the classification of faults. Five types of faults in motorcycles are considered in this work. Observed classification rates are over 90% for the fault detection stage and over 94% for the fault location stage. The work identifies other interesting applications in the development of acoustic fingerprints for fault diagnosis of machinery, tuning of musical instruments, medical diagnosis, etc.

  20. Fault Detection and Isolation Using Analytical Redundancy Relations for the Ship Propulsion Benchmark

    DEFF Research Database (Denmark)

    Izadi-Zamanabadi, Roozbeh

    The prime objective of Fault-tolerant Control (FTC) systems is to handle faults and discrepancies using appropriate accommodation policies. The issue of obtaining information about various parameters and signals, which have to be monitored for fault detection purposes, becomes a rigorous task wit...

  1. 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.

  2. Computational Effective Fault Detection by Means of Signature Functions.

    Directory of Open Access Journals (Sweden)

    Przemyslaw Baranski

    Full Text Available The paper presents a computationally effective method for fault detection. A system's responses are measured under healthy and ill conditions. These signals are used to calculate so-called signature functions that create a signal space. The current system's response is projected into this space. The signal location in this space easily allows to determine the fault. No classifier such as a neural network, hidden Markov models, etc. is required. The advantage of this proposed method is its efficiency, as computing projections amount to calculating dot products. Therefore, this method is suitable for real-time embedded systems due to its simplicity and undemanding processing capabilities which permit the use of low-cost hardware and allow rapid implementation. The approach performs well for systems that can be considered linear and stationary. The communication presents an application, whereby an industrial process of moulding is supervised. The machine is composed of forms (dies whose alignment must be precisely set and maintained during the work. Typically, the process is stopped periodically to manually control the alignment. The applied algorithm allows on-line monitoring of the device by analysing the acceleration signal from a sensor mounted on a die. This enables to detect failures at an early stage thus prolonging the machine's life.

  3. Stochastic Resonance algorithms to enhance damage detection in bearing faults

    Directory of Open Access Journals (Sweden)

    Castiglione Roberto

    2015-01-01

    Full Text Available Stochastic Resonance is a phenomenon, studied and mainly exploited in telecommunication, which permits the amplification and detection of weak signals by the assistance of noise. The first papers on this technique are dated early 80 s and were developed to explain the periodically recurrent ice ages. Other applications mainly concern neuroscience, biology, medicine and obviously signal analysis and processing. Recently, some researchers have applied the technique for detecting faults in mechanical systems and bearings. In this paper, we try to better understand the conditions of applicability and which is the best algorithm to be adopted for these purposes. In fact, to get the methodology profitable and efficient to enhance the signal spikes due to fault in rings and balls/rollers of bearings, some parameters have to be properly selected. This is a problem since in system identification this procedure should be as blind as possible. Two algorithms are analysed: the first exploits classical SR with three parameters mutually dependent, while the other uses Woods-Saxon potential, with three parameters yet but holding a different meaning. The comparison of the performances of the two algorithms and the optimal choice of their parameters are the scopes of this paper. Algorithms are tested on simulated and experimental data showing an evident capacity of increasing the signal to noise ratio.

  4. Fault detection in processes represented by PLS models using an EWMA control scheme

    KAUST Repository

    Harrou, Fouzi

    2016-10-20

    Fault detection is important for effective and safe process operation. Partial least squares (PLS) has been used successfully in fault detection for multivariate processes with highly correlated variables. However, the conventional PLS-based detection metrics, such as the Hotelling\\'s T and the Q statistics are not well suited to detect small faults because they only use information about the process in the most recent observation. Exponentially weighed moving average (EWMA), however, has been shown to be more sensitive to small shifts in the mean of process variables. In this paper, a PLS-based EWMA fault detection method is proposed for monitoring processes represented by PLS models. The performance of the proposed method is compared with that of the traditional PLS-based fault detection method through a simulated example involving various fault scenarios that could be encountered in real processes. The simulation results clearly show the effectiveness of the proposed method over the conventional PLS method.

  5. Fault Detection, Isolation, and Accommodation for LTI Systems Based on GIMC Structure

    Directory of Open Access Journals (Sweden)

    D. U. Campos-Delgado

    2008-01-01

    Full Text Available In this contribution, an active fault-tolerant scheme that achieves fault detection, isolation, and accommodation is developed for LTI systems. Faults and perturbations are considered as additive signals that modify the state or output equations. The accommodation scheme is based on the generalized internal model control architecture recently proposed for fault-tolerant control. In order to improve the performance after a fault, the compensation is considered in two steps according with a fault detection and isolation algorithm. After a fault scenario is detected, a general fault compensator is activated. Finally, once the fault is isolated, a specific compensator is introduced. In this setup, multiple faults could be treated simultaneously since their effect is additive. Design strategies for a nominal condition and under model uncertainty are presented in the paper. In addition, performance indices are also introduced to evaluate the resulting fault-tolerant scheme for detection, isolation, and accommodation. Hard thresholds are suggested for detection and isolation purposes, meanwhile, adaptive ones are considered under model uncertainty to reduce the conservativeness. A complete simulation evaluation is carried out for a DC motor setup.

  6. Faults detection approach using PCA and SOM algorithm in PMSG-WT system

    Directory of Open Access Journals (Sweden)

    Mohamed Lamine FADDA

    2016-07-01

    Full Text Available In this paper, a new approach for faults detection in observable data system wind turbine - permanent magnet synchronous generator (WT-PMSG, the studying objective, illustrate the combination (SOM-PCA to build Multi-local-PCA models faults detection in system (WT-PMSG, the performance of the method suggested to faults detection in system data, finding good results in simulation experiment.

  7. Onboard Generic Fault Detection Algorithm Development and Demonstration for VTOL sUAS, Phase I

    Data.gov (United States)

    National Aeronautics and Space Administration — In the proposed SBIR study, Empirical Systems Aerospace, Inc. (ESAero) will develop a fault detection and identification avionics system implementing a generic...

  8. A Novel Arc Fault Detector for Early Detection of Electrical Fires.

    Science.gov (United States)

    Yang, Kai; Zhang, Rencheng; Yang, Jianhong; Liu, Canhua; Chen, Shouhong; Zhang, Fujiang

    2016-04-09

    Arc faults can produce very high temperatures and can easily ignite combustible materials; thus, they represent one of the most important causes of electrical fires. The application of arc fault detection, as an emerging early fire detection technology, is required by the National Electrical Code to reduce the occurrence of electrical fires. However, the concealment, randomness and diversity of arc faults make them difficult to detect. To improve the accuracy of arc fault detection, a novel arc fault detector (AFD) is developed in this study. First, an experimental arc fault platform is built to study electrical fires. A high-frequency transducer and a current transducer are used to measure typical load signals of arc faults and normal states. After the common features of these signals are studied, high-frequency energy and current variations are extracted as an input eigenvector for use by an arc fault detection algorithm. Then, the detection algorithm based on a weighted least squares support vector machine is designed and successfully applied in a microprocessor. Finally, an AFD is developed. The test results show that the AFD can detect arc faults in a timely manner and interrupt the circuit power supply before electrical fires can occur. The AFD is not influenced by cross talk or transient processes, and the detection accuracy is very high. Hence, the AFD can be installed in low-voltage circuits to monitor circuit states in real-time to facilitate the early detection of electrical fires.

  9. A Novel Arc Fault Detector for Early Detection of Electrical Fires

    Science.gov (United States)

    Yang, Kai; Zhang, Rencheng; Yang, Jianhong; Liu, Canhua; Chen, Shouhong; Zhang, Fujiang

    2016-01-01

    Arc faults can produce very high temperatures and can easily ignite combustible materials; thus, they represent one of the most important causes of electrical fires. The application of arc fault detection, as an emerging early fire detection technology, is required by the National Electrical Code to reduce the occurrence of electrical fires. However, the concealment, randomness and diversity of arc faults make them difficult to detect. To improve the accuracy of arc fault detection, a novel arc fault detector (AFD) is developed in this study. First, an experimental arc fault platform is built to study electrical fires. A high-frequency transducer and a current transducer are used to measure typical load signals of arc faults and normal states. After the common features of these signals are studied, high-frequency energy and current variations are extracted as an input eigenvector for use by an arc fault detection algorithm. Then, the detection algorithm based on a weighted least squares support vector machine is designed and successfully applied in a microprocessor. Finally, an AFD is developed. The test results show that the AFD can detect arc faults in a timely manner and interrupt the circuit power supply before electrical fires can occur. The AFD is not influenced by cross talk or transient processes, and the detection accuracy is very high. Hence, the AFD can be installed in low-voltage circuits to monitor circuit states in real-time to facilitate the early detection of electrical fires. PMID:27070618

  10. Method for detecting an open-switch fault in a grid-connected NPC inverter system

    DEFF Research Database (Denmark)

    Choi, Ui-Min; Jeong, Hae-Gwang; Lee, Kyo-Beum

    2012-01-01

    This paper proposes a fault-detection method for an open-switch fault in the switches of grid-connected neutral-point-clamped inverter systems. The proposed method can not only detect the fault condition but also identify the location of the faulty switch. In the proposed method, which is designed...... by incorporating a simple switching control in the conventional method, the fault condition is detected on the basis of the radius of the Concordia current pattern, and the location of the faulty switch can be identified. By using the proposed method, it is possible to detect the open-switch fault and identify...... the faulty switch within two fundamental periods, without using additional sensors or performing complex calculations. Simulations and experiments are carried out to confirm the reliability of the proposed fault-detection method....

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

    Directory of Open Access Journals (Sweden)

    Yunji Li

    2018-01-01

    Full Text Available 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.

  12. 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.

  13. 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...

  14. Fault detection of planetary gearboxes using new diagnostic parameters

    Science.gov (United States)

    Lei, Yaguo; Kong, Detong; Lin, Jing; Zuo, Ming J.

    2012-05-01

    Planetary gearboxes are commonly used in modern industry because of their large transmission ratio and strong load-bearing capacity. They generally work under heavy load and tough working environment and therefore their key components including sun gear, planet gears, ring gear, etc are subject to severe pitting and fatigue crack. Planetary gearboxes significantly differ from fixed-axis gearboxes and exhibit unique behavior, which invalidates the use of the diagnostic parameters developed and suitable for fixed-axis gearboxes. Therefore, there is a need to develop parameters specifically for detecting and diagnosing faults of planetary gearboxes. In this study, two diagnostic parameters are proposed based on the examination of the vibration characteristics of planetary gearboxes in both time and frequency domains. One is the root mean square of the filtered signal (FRMS) and the other is the normalized summation of positive amplitudes of the difference spectrum between the unknown signal and the healthy signal (NSDS). To test the proposed diagnostic parameters, we conducted experiments on a planetary gearbox test rig with sun gear faults including a cracked tooth and a pitted tooth. The vibration signals were measured under different motor speeds. The proposed parameters are compared with the existing parameters reported in the literature. The comparison results show the proposed diagnostic parameters perform better than others.

  15. Research on Fault Detection System of Power Equipment Based on UV and Infrared Image

    Science.gov (United States)

    Lu, Qiyu; Ding, Kun

    2017-09-01

    UV corona on power system can reflect the location of the fault and the severity of the fault, the traditional UV and infrared detection equipment can only use the band and the visible light band image of the power system fault detection. In this paper, a power system fault detection system based on ultraviolet and infrared dual-band images is designed. The principle of UV imaging detection and image fusion are introduced respectively. The software of the host computer is written by MFC. The software can acquire both ultraviolet and infrared, the two images are fused using the image fusion algorithm based on edge detection and cross correlation and the highest point temperature is plotted. Experiments show that the system can detect the failure of power equipment in time, and has a certain practical value, which puts forward a new idea for fault detection of power equipment.

  16. 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.

  17. Fault Detection Coverage Quantification of Automatic Test Functions of Digital I and C System in NPPs

    International Nuclear Information System (INIS)

    Choi, Jong Gyun; Lee, Seung Jun; Hur, Seop; Lee, Young Jun; Jang, Seung Cheol

    2011-01-01

    Recently, analog instrument and control (I and C) systems in nuclear power plants (NPPs) have been replaced with digital systems for safer and more efficient operations. Digital I and C systems have adopted various fault-tolerant techniques that help the system correctly and safely perform the specific required functions in spite of the presence of faults. Each fault-tolerant technique has a different inspection period from real-time monitoring to monthly testing. The range covered by each fault-tolerant technique is also different. The digital I and C system, therefore, adopts multiple barriers consisting of various fault-tolerant techniques to increase total fault detection coverage. Even though these fault-tolerant techniques are adopted to ensure and improve the safety of a system, their effects have not been properly considered yet in most PSA models. Therefore, it is necessary to develop an evaluation method that can describe these features of a digital I and C system. Several issues must be considered in the fault coverage estimation of a digital I and C system, and two of them were handled in this work. The first is to quantify the fault coverage of each fault-tolerant technique implemented in the system, and the second is to exclude the duplicated effect of fault-tolerant techniques implemented simultaneously at each level of the system's hierarchy, as a fault occurring in a system might be detected by one or more fault-tolerant techniques. For this work, fault injection experiment was used to obtain the exact relations between faults and multiple barriers of fault-tolerant techniques. This experiment was applied to a bistable processor (BP) of a reactor protection system

  18. Verification of a Novel Method of Detecting Faults in Medium-Voltage Systems with Covered Conductors

    Directory of Open Access Journals (Sweden)

    Mišák Stanislav

    2017-06-01

    Full Text Available This paper describes the use of new methods of detecting faults in medium-voltage overhead lines built of covered conductors. The methods mainly address such faults as falling of a conductor, contacting a conductor with a tree branch, or falling a tree branch across three phases of a medium-voltage conductor. These faults cannot be detected by current digital relay protection systems. Therefore, a new system that can detect the above mentioned faults was developed. After having tested its operation, the system has already been implemented to protect mediumvoltage overhead lines built of covered conductors.

  19. On Line Current Monitoring and Application of a Residual Method for Eccentricity Fault Detection

    Directory of Open Access Journals (Sweden)

    METATLA, A.

    2011-02-01

    Full Text Available This work concerns the monitoring and diagnosis of faults in induction motors. We develop an approach based on residual analysis of stator currents to detect and diagnose faults eccentricity static, dynamic and mixed in three phase induction motor. To simulate the behavior of motor failure, a model is proposed based on the approach of magnetically coupled coils. The simulation results show the importance of the approach applied for the detection and diagnosis of fault in three phase induction motor.

  20. Analytical Model-based Fault Detection and Isolation in Control Systems

    DEFF Research Database (Denmark)

    Vukic, Z.; Ozbolt, H.; Blanke, M.

    1998-01-01

    The paper gives an introduction and an overview of the field of fault detection and isolation for control systems. The summary of analytical (quantitative model-based) methodds and their implementation are presented. The focus is given to mthe analytical model-based fault-detection and fault diag...... diagnosis methods, often viewed as the classical or deterministic ones. Emphasis is placed on the algorithms suitable for ship automation, unmanned underwater vehicles, and other systems of automatic control....

  1. 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.

  2. New algorithm to detect modules in a fault tree for a PSA

    International Nuclear Information System (INIS)

    Jung, Woo Sik

    2015-01-01

    A module or independent subtree is a part of a fault tree whose child gates or basic events are not repeated in the remaining part of the fault tree. Modules are necessarily employed in order to reduce the computational costs of fault tree quantification. This paper presents a new linear time algorithm to detect modules of large fault trees. The size of cut sets can be substantially reduced by replacing independent subtrees in a fault tree with super-components. Chatterjee and Birnbaum developed properties of modules, and demonstrated their use in the fault tree analysis. Locks expanded the concept of modules to non-coherent fault trees. Independent subtrees were manually identified while coding a fault tree for computer analysis. However, nowadays, the independent subtrees are automatically identified by the fault tree solver. A Dutuit and Rauzy (DR) algorithm to detect modules of a fault tree for coherent or non-coherent fault tree was proposed in 1996. It has been well known that this algorithm quickly detects modules since it is a linear time algorithm. The new algorithm minimizes computational memory and quickly detects modules. Furthermore, it can be easily implemented into industry fault tree solvers that are based on traditional Boolean algebra, binary decision diagrams (BDDs), or Zero-suppressed BDDs. The new algorithm employs only two scalar variables in Eqs. to that are volatile information. After finishing the traversal and module detection of each node, the volatile information is destroyed. Thus, the new algorithm does not employ any other additional computational memory and operations. It is recommended that this method be implemented into fault tree solvers for efficient probabilistic safety assessment (PSA) of nuclear power plants

  3. A set based probabilistic approach to threshold design for optimal fault detection

    NARCIS (Netherlands)

    Rostampour Samarin, V.; Ferrari, R.; Keviczky, T.; Sun, J.; Jiang, Z.-P.

    2017-01-01

    Traditional deterministic robust fault detection threshold designs, such as the norm-based or limit-checking method, are plagued by high conservativeness, which leads to poor fault detection performance. On one side they are ill-suited at tightly bounding the healthy residuals of uncertain

  4. A Spectrum Detection Approach for Bearing Fault Signal Based on Spectral Kurtosis

    Directory of Open Access Journals (Sweden)

    Yunfeng Li

    2017-01-01

    Full Text Available According to the similarity between Morlet wavelet and fault signal and the sensitive characteristics of spectral kurtosis for the impact signal, a new wavelet spectrum detection approach based on spectral kurtosis for bearing fault signal is proposed. This method decreased the band-pass filter range and reduced the wavelet window width significantly. As a consequence, the bearing fault signal was detected adaptively, and time-frequency characteristics of the fault signal can be extracted accurately. The validity of this method was verified by the identifications of simulated shock signal and test bearing fault signal. The method provides a new understanding of wavelet spectrum detection based on spectral kurtosis for rolling element bearing fault signal.

  5. 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.

  6. DWT based bearing fault detection in induction motor using noise cancellation

    Directory of Open Access Journals (Sweden)

    K.C. Deekshit Kompella

    2016-12-01

    Full Text Available This paper presents an approach to detect the bearing faults experienced by induction machine using motor current signature analysis (MCSA. At the incipient stage of bearing fault, the current signature analysis has shown poor performance due to domination of pre fault components in the stator current. Therefore, in this paper domination of pre fault components is suppressed using noise cancellation by Wiener filter. The spectral analysis is carried out using discrete wavelet transform (DWT. The fault severity is estimated by calculating fault indexing parameter of wavelet coefficients. It is further proposed that, the fault indexing parameter of power spectral density (PSD based wavelet coefficients gives better results. The proposed method is examined using simulation and experiment on 2.2 kW test bed.

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

    Science.gov (United States)

    Kim, Pyung Soo

    2017-04-01

    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.

  8. Fault detection of a Five-Phase Permanent-Magnet Machine

    DEFF Research Database (Denmark)

    Bianchini, Claudio; Matzen, Torben N.; Bianchi, Nicola

    2008-01-01

    The paper focuses on the fault detection of a five-phase Permanent-Magnet (PM) machine. This machine has been de-signed for fault tolerant applications, and it is characterised by a mutual inductance equal to zero and a high self inductance, with the purpose to limit the short circuit current....... The effects of a limited number of short-circuited turns were investigated by theoretical and Finite Element (FE) analysis, and then a procedure for fault detection has been proposed, focusing on the severity of the fault (i.e. the number of short-circuited turns and the related current)....

  9. From experiment to design -- Fault characterization and detection in parallel computer systems using computational accelerators

    Science.gov (United States)

    Yim, Keun Soo

    This dissertation summarizes experimental validation and co-design studies conducted to optimize the fault detection capabilities and overheads in hybrid computer systems (e.g., using CPUs and Graphics Processing Units, or GPUs), and consequently to improve the scalability of parallel computer systems using computational accelerators. The experimental validation studies were conducted to help us understand the failure characteristics of CPU-GPU hybrid computer systems under various types of hardware faults. The main characterization targets were faults that are difficult to detect and/or recover from, e.g., faults that cause long latency failures (Ch. 3), faults in dynamically allocated resources (Ch. 4), faults in GPUs (Ch. 5), faults in MPI programs (Ch. 6), and microarchitecture-level faults with specific timing features (Ch. 7). The co-design studies were based on the characterization results. One of the co-designed systems has a set of source-to-source translators that customize and strategically place error detectors in the source code of target GPU programs (Ch. 5). Another co-designed system uses an extension card to learn the normal behavioral and semantic execution patterns of message-passing processes executing on CPUs, and to detect abnormal behaviors of those parallel processes (Ch. 6). The third co-designed system is a co-processor that has a set of new instructions in order to support software-implemented fault detection techniques (Ch. 7). The work described in this dissertation gains more importance because heterogeneous processors have become an essential component of state-of-the-art supercomputers. GPUs were used in three of the five fastest supercomputers that were operating in 2011. Our work included comprehensive fault characterization studies in CPU-GPU hybrid computers. In CPUs, we monitored the target systems for a long period of time after injecting faults (a temporally comprehensive experiment), and injected faults into various types of

  10. Single Cathode Ion Thruster

    Data.gov (United States)

    National Aeronautics and Space Administration — Objective is to design an electrostatic ion thruster that is more efficient, simpler, and lower cost than the current gridded ion thruster. Initial objective is to...

  11. Early Oscillation Detection for DC/DC Converter Fault Diagnosis

    Science.gov (United States)

    Wang, Bright L.

    2011-01-01

    The electrical power system of a spacecraft plays a very critical role for space mission success. Such a modern power system may contain numerous hybrid DC/DC converters both inside the power system electronics (PSE) units and onboard most of the flight electronics modules. One of the faulty conditions for DC/DC converter that poses serious threats to mission safety is the random occurrence of oscillation related to inherent instability characteristics of the DC/DC converters and design deficiency of the power systems. To ensure the highest reliability of the power system, oscillations in any form shall be promptly detected during part level testing, system integration tests, flight health monitoring, and on-board fault diagnosis. The popular gain/phase margin analysis method is capable of predicting stability levels of DC/DC converters, but it is limited only to verification of designs and to part-level testing on some of the models. This method has to inject noise signals into the control loop circuitry as required, thus, interrupts the DC/DC converter's normal operation and increases risks of degrading and damaging the flight unit. A novel technique to detect oscillations at early stage for flight hybrid DC/DC converters was developed.

  12. 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)

  13. POD Model Reconstruction for Gray-Box Fault Detection

    Science.gov (United States)

    Park, Han; Zak, Michail

    2007-01-01

    Proper orthogonal decomposition (POD) is the mathematical basis of a method of constructing low-order mathematical models for the "gray-box" fault-detection algorithm that is a component of a diagnostic system known as beacon-based exception analysis for multi-missions (BEAM). POD has been successfully applied in reducing computational complexity by generating simple models that can be used for control and simulation for complex systems such as fluid flows. In the present application to BEAM, POD brings the same benefits to automated diagnosis. BEAM is a method of real-time or offline, automated diagnosis of a complex dynamic system.The gray-box approach makes it possible to utilize incomplete or approximate knowledge of the dynamics of the system that one seeks to diagnose. In the gray-box approach, a deterministic model of the system is used to filter a time series of system sensor data to remove the deterministic components of the time series from further examination. What is left after the filtering operation is a time series of residual quantities that represent the unknown (or at least unmodeled) aspects of the behavior of the system. Stochastic modeling techniques are then applied to the residual time series. The procedure for detecting abnormal behavior of the system then becomes one of looking for statistical differences between the residual time series and the predictions of the stochastic model.

  14. 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.

  15. Fiber Bragg Grating sensor for fault detection in radial and network transmission lines.

    Science.gov (United States)

    Moghadas, Amin A; Shadaram, Mehdi

    2010-01-01

    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.

  16. A first approach on fault detection and isolation for cardiovascular anomalies detection

    KAUST Repository

    Ledezma, Fernando

    2015-07-01

    In this paper, we use an extended version of the cardiovascular system\\'s state space model presented by [1] and propose a fault detection and isolation methodology to study the problem of detecting cardiovascular anomalies that can originate from variations in physiological parameters and deviations in the performance of the heart\\'s mitral and aortic valves. An observer-based approach is discussed as the basis of the method. The approach contemplates a bank of Extended Kalman Filters to achieve joint estimation of the model\\'s states and parameters and to detect malfunctions in the valves\\' performance. © 2015 American Automatic Control Council.

  17. A statistical-based approach for fault detection and diagnosis in a photovoltaic system

    KAUST Repository

    Garoudja, Elyes

    2017-07-10

    This paper reports a development of a statistical approach for fault detection and diagnosis in a PV system. Specifically, the overarching goal of this work is to early detect and identify faults on the DC side of a PV system (e.g., short-circuit faults; open-circuit faults; and partial shading faults). Towards this end, we apply exponentially-weighted moving average (EWMA) control chart on the residuals obtained from the one-diode model. Such a choice is motivated by the greater sensitivity of EWMA chart to incipient faults and its low-computational cost making it easy to implement in real time. Practical data from a 3.2 KWp photovoltaic plant located within an Algerian research center is used to validate the proposed approach. Results show clearly the efficiency of the developed method in monitoring PV system status.

  18. Wind Turbine Fault Detection based on Artificial Neural Network Analysis of SCADA Data

    DEFF Research Database (Denmark)

    Herp, Jürgen; S. Nadimi, Esmaeil

    2015-01-01

    Slowly developing faults in wind turbine can, when not detected and fixed on time, cause severe damage and downtime. We are proposing a fault detection method based on Artificial Neural Networks (ANN) and the recordings from Supervisory Control and Data Acquisition (SCADA) systems installed in wind...... farms. We establish a model for the normal behaviour of a wind turbine from considered fault-free data and test the proposed model on further data. We show that ANN can be used for early fault detection in wind turbines monitoring. Concerning vibrational levels in x and y directions we base our fault...... detection upon a generalized-likelihood-test. An upper and a lower control bounds are established for x and y respectively, given a minimum false alarm probability η based on the statistical characteristics of the data....

  19. An energy kurtosis demodulation technique for signal denoising and bearing fault detection

    International Nuclear Information System (INIS)

    Wang, Wilson; Lee, Hewen

    2013-01-01

    Rolling element bearings are commonly used in rotary machinery. Reliable bearing fault detection techniques are very useful in industries for predictive maintenance operations. Bearing fault detection still remains a very challenging task especially when defects occur on rotating bearing components because the fault-related features are non-stationary in nature. In this work, an energy kurtosis demodulation (EKD) technique is proposed for bearing fault detection especially for non-stationary signature analysis. The proposed EKD technique firstly denoises the signal by using a maximum kurtosis deconvolution filter to counteract the effect of signal transmission path so as to highlight defect-associated impulses. Next, the denoised signal is modulated over several frequency bands; a novel signature integration strategy is proposed to enhance feature characteristics. The effectiveness of the proposed EKD fault detection technique is verified by a series of experimental tests corresponding to different bearing conditions. (paper)

  20. A Novel Approach for Eccentricity Fault Detection in Squirrel Cage Induction Motors

    Directory of Open Access Journals (Sweden)

    Mehdi Ahmadi

    2013-01-01

    Full Text Available In this paper, static eccentricity fault detection in induction motors is studied. Two dimensional finite element method (2D-FEM is used for faultless and eccentric condition modeling in induction motors. Also current and speed signals are compared in two experimental and simulation cases for model validating. For fault detection, fast Fourier transform is used at first. In this method, high order harmonics with small amplitude can alarms the fault occurrence. For this reason, the fault detection process is difficult.To overcome these drawbacks, it is suggested that two test coils contrive around the air-gap. So, any changes in air-gap can be detected easily. Moreover this test coils are used in open circuit case. So, these test coils do not effect on motor dynamics. Also, the results show that modulated voltage can be alarm the fault occurrence, type and percent well.

  1. Fault detection of Tennessee Eastman process based on topological features and SVM

    Science.gov (United States)

    Zhao, Huiyang; Hu, Yanzhu; Ai, Xinbo; Hu, Yu; Meng, Zhen

    2018-03-01

    Fault detection in industrial process is a popular research topic. Although the distributed control system(DCS) has been introduced to monitor the state of industrial process, it still cannot satisfy all the requirements for fault detection of all the industrial systems. In this paper, we proposed a novel method based on topological features and support vector machine(SVM), for fault detection of industrial process. The proposed method takes global information of measured variables into account by complex network model and predicts whether a system has generated some faults or not by SVM. The proposed method can be divided into four steps, i.e. network construction, network analysis, model training and model testing respectively. Finally, we apply the model to Tennessee Eastman process(TEP). The results show that this method works well and can be a useful supplement for fault detection of industrial process.

  2. Application of a Fault Detection and Isolation System on a Rotary Machine

    Directory of Open Access Journals (Sweden)

    Silvia M. Zanoli

    2013-01-01

    Full Text Available The paper illustrates the design and the implementation of a Fault Detection and Isolation (FDI system to a rotary machine like a multishaft centrifugal compressor. A model-free approach, that is, the Principal Component Analysis (PCA, has been employed to solve the fault detection issue. For the fault isolation purpose structured residuals have been adopted while an adaptive threshold has been designed in order to detect and to isolate the faults. To prove the goodness of the proposed FDI system, historical data of a nitrogen centrifugal compressor employed in a refinery plant are considered. Tests results show that detection and isolation of single as well as multiple faults are successfully achieved.

  3. Gear-box fault detection using time-frequency based methods

    DEFF Research Database (Denmark)

    Odgaard, Peter Fogh; Stoustrup, Jakob

    2015-01-01

    Gear-box fault monitoring and detection is important for optimization of power generation and availability of wind turbines. The current industrial approach is to use condition monitoring systems, which runs in parallel with the wind turbine control system, using expensive additional sensors...... in the gear-box resonance frequency can be detected. Two different time–frequency based approaches are presented in this paper. One is a filter based approach and the other is based on a Karhunen–Loeve basis. Both of them detect the gear-box fault with an acceptable detection delay of maximum 100s, which...... is neglectable compared with the fault developing time....

  4. 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.

  5. Multiple fault detection and diagnosis in a gas turbine using nonlinear principal component analysis and structured residuals

    OpenAIRE

    Rincon-Charris, Amilcar; Quevedo Casín, Joseba Jokin

    2013-01-01

    Multiple fault detection and diagnosis is a challenging problem because the number of candidates grows exponentially in the number of faults. In add ition, multiple faults in dynamic systems may be hard to detect, because they can mask or compensate each other’s effects. This paper presents the study of the detection and diagnosis of multiple faults in a SR-30 Gas Turbine using nonlinear principal component analys is as the detection method and structured residua...

  6. Sensor Fault Detection and Diagnosis Simulation of a Helicopter Engine in an Intelligent Control Framework

    Science.gov (United States)

    Litt, Jonathan; Kurtkaya, Mehmet; Duyar, Ahmet

    1994-01-01

    This paper presents an application of a fault detection and diagnosis scheme for the sensor faults of a helicopter engine. The scheme utilizes a model-based approach with real time identification and hypothesis testing which can provide early detection, isolation, and diagnosis of failures. It is an integral part of a proposed intelligent control system with health monitoring capabilities. The intelligent control system will allow for accommodation of faults, reduce maintenance cost, and increase system availability. The scheme compares the measured outputs of the engine with the expected outputs of an engine whose sensor suite is functioning normally. If the differences between the real and expected outputs exceed threshold values, a fault is detected. The isolation of sensor failures is accomplished through a fault parameter isolation technique where parameters which model the faulty process are calculated on-line with a real-time multivariable parameter estimation algorithm. The fault parameters and their patterns can then be analyzed for diagnostic and accommodation purposes. The scheme is applied to the detection and diagnosis of sensor faults of a T700 turboshaft engine. Sensor failures are induced in a T700 nonlinear performance simulation and data obtained are used with the scheme to detect, isolate, and estimate the magnitude of the faults.

  7. Reliable fault detection and diagnosis of photovoltaic systems based on statistical monitoring approaches

    KAUST Repository

    Harrou, Fouzi

    2017-09-18

    This study reports the development of an innovative fault detection and diagnosis scheme to monitor the direct current (DC) side of photovoltaic (PV) systems. Towards this end, we propose a statistical approach that exploits the advantages of one-diode model and those of the univariate and multivariate exponentially weighted moving average (EWMA) charts to better detect faults. Specifically, we generate array\\'s residuals of current, voltage and power using measured temperature and irradiance. These residuals capture the difference between the measurements and the predictions MPP for the current, voltage and power from the one-diode model, and use them as fault indicators. Then, we apply the multivariate EWMA (MEWMA) monitoring chart to the residuals to detect faults. However, a MEWMA scheme cannot identify the type of fault. Once a fault is detected in MEWMA chart, the univariate EWMA chart based on current and voltage indicators is used to identify the type of fault (e.g., short-circuit, open-circuit and shading faults). We applied this strategy to real data from the grid-connected PV system installed at the Renewable Energy Development Center, Algeria. Results show the capacity of the proposed strategy to monitors the DC side of PV systems and detects partial shading.

  8. Adapting plant measurement data to improve hardware fault detection performance in pressurised water reactors

    International Nuclear Information System (INIS)

    Cilliers, A.C.; Mulder, E.J.

    2012-01-01

    Highlights: ► Attempt was to use available resources at a nuclear plant in a value added fashion. ► Includes plant measurement data and plant training and engineering simulator capabilities. ► Solving the fault masking effect by the distributed control systems in the plant. ► Modelling the effect of inaccuracies in plant models used in the simulators. ► Combination of above resulted in the development of a deterministic fault identifications system. -- Abstract: With the fairly recent adoption of digital control and instrumentation systems in the nuclear industry a lot of research now focus on the development expert fault identification systems. The fault identification systems enable detecting early onset faults of fault causes which allows maintenance planning on the equipment showing signs of deterioration or failure. This includes valve and leaks and small cracks in steam generator tubes usually detected by means of ultrasonic inspection. Detecting faults early during transient operation in NPPs is problematic due to the absence of a reliable reference to compare plant measurements with during transients. The distributed application of control systems operating independently to keep the plant operating within the safe operating boundaries complicates the problem since the control systems would not only operate to reduce the effect of transient disturbances but fault disturbances as well. This paper provides a method to adapt the plant measurements that isolates the control actions on the fault and re-introduces it into the measurement data, thereby improving plant diagnostic performance.

  9. Detection of arc fault based on frequency constrained independent component analysis

    Science.gov (United States)

    Yang, Kai; Zhang, Rencheng; Xu, Renhao; Chen, Yongzhi; Yang, Jianhong; Chen, Shouhong

    2015-02-01

    Arc fault is one of the main reasons of electrical fires. As a result of weakness, randomness and cross talk of arc faults, very few of methods have been successfully used to protect loads from all arc faults in low-voltage circuits. Therefore, a novel detection method is developed for detection of arc faults. The method is based on frequency constrained independent component analysis. In the process of the method derivation, a band-pass filter was introduced as a constraint condition to separate independent components of mixed signals. In the process of the independent component separations, although the fault mixed signals were under the conditions of the strong background noise and the frequency aliasing, the effective high frequency components of arc faults could be separated by frequency constrained independent component analysis. Based on the separated components, the power spectrums of them were calculated to classify the normal and the arc fault conditions. The validity of the developed method was verified by using an arc fault experimental platform set up. The results show that arc faults of nine typical electrical loads are successfully detected based on frequency constrained independent component analysis.

  10. Aircraft Fault Detection Using Real-Time Frequency Response Estimation

    Science.gov (United States)

    Grauer, Jared A.

    2016-01-01

    A real-time method for estimating time-varying aircraft frequency responses from input and output measurements was demonstrated. The Bat-4 subscale airplane was used with NASA Langley Research Center's AirSTAR unmanned aerial flight test facility to conduct flight tests and collect data for dynamic modeling. Orthogonal phase-optimized multisine inputs, summed with pilot stick and pedal inputs, were used to excite the responses. The aircraft was tested in its normal configuration and with emulated failures, which included a stuck left ruddervator and an increased command path latency. No prior knowledge of a dynamic model was used or available for the estimation. The longitudinal short period dynamics were investigated in this work. Time-varying frequency responses and stability margins were tracked well using a 20 second sliding window of data, as compared to a post-flight analysis using output error parameter estimation and a low-order equivalent system model. This method could be used in a real-time fault detection system, or for other applications of dynamic modeling such as real-time verification of stability margins during envelope expansion tests.

  11. Model-based fault detection for generator cooling system in wind turbines using SCADA data

    DEFF Research Database (Denmark)

    Borchersen, Anders Bech; Kinnaert, Michel

    2016-01-01

    In this work, an early fault detection system for the generator cooling of wind turbines is presented and tested. It relies on a hybrid model of the cooling system. The parameters of the generator model are estimated by an extended Kalman filter. The estimated parameters are then processed...... by an appropriate statistical change detection algorithm in order to detect faults in the cooling system. To validate the method, it has been tested on 3 years of historical data from 43 turbines. During the testing period, 16 faults occurred; 15 of these were detected by the developed method, and one false alarm...... was issued. This is an improvement compared with the current system that gives 15 detections and more than 10 false alarms. In some cases, the method detects the fault a long time before the turbine reports an alarm. A further advantage of the method is that it is based on supervisory control and data...

  12. Transient Monitoring Function–Based Fault Detection for Inverter-Interfaced Microgrids

    DEFF Research Database (Denmark)

    Sadeghkhani, Iman; Esmail Hamedani Golshan, Mohamad; Mehrizi-Sani, Ali

    2018-01-01

    of the inverter current waveform using a transient monitoring function (TMF). To enhance the ability of the proposed fault detection scheme, an auxiliary control system is employed in addition to the main control system of the inverter. The proposed scheme can also differentiate asymmetrical and symmetrical fault......One of the major challenges in protection of the inverter-interfaced islanded microgrids is their limited fault current level. This degrades the performance of traditional overcurrent protection schemes. This paper proposes a fault detection strategy based on monitoring the transient response...... conditions from normal load switching events and is effective for various inverter topologies (i.e., three/four-leg), main current limiting strategies, and all reference frames of the multi-loop control system. The merits of the proposed fault detection scheme are demonstrated through several time...

  13. Automatic Channel Fault Detection on a Small Animal APD-Based Digital PET Scanner

    Science.gov (United States)

    Charest, Jonathan; Beaudoin, Jean-François; Cadorette, Jules; Lecomte, Roger; Brunet, Charles-Antoine; Fontaine, Réjean

    2014-10-01

    Avalanche photodiode (APD) based positron emission tomography (PET) scanners show enhanced imaging capabilities in terms of spatial resolution and contrast due to the one to one coupling and size of individual crystal-APD detectors. However, to ensure the maximal performance, these PET scanners require proper calibration by qualified scanner operators, which can become a cumbersome task because of the huge number of channels they are made of. An intelligent system (IS) intends to alleviate this workload by enabling a diagnosis of the observational errors of the scanner. The IS can be broken down into four hierarchical blocks: parameter extraction, channel fault detection, prioritization and diagnosis. One of the main activities of the IS consists in analyzing available channel data such as: normalization coincidence counts and single count rates, crystal identification classification data, energy histograms, APD bias and noise thresholds to establish the channel health status that will be used to detect channel faults. This paper focuses on the first two blocks of the IS: parameter extraction and channel fault detection. The purpose of the parameter extraction block is to process available data on individual channels into parameters that are subsequently used by the fault detection block to generate the channel health status. To ensure extensibility, the channel fault detection block is divided into indicators representing different aspects of PET scanner performance: sensitivity, timing, crystal identification and energy. Some experiments on a 8 cm axial length LabPET scanner located at the Sherbrooke Molecular Imaging Center demonstrated an erroneous channel fault detection rate of 10% (with a 95% confidence interval (CI) of [9, 11]) which is considered tolerable. Globally, the IS achieves a channel fault detection efficiency of 96% (CI: [95, 97]), which proves that many faults can be detected automatically. Increased fault detection efficiency would be

  14. Fault detection and diagnosis for compliance monitoring in international supply chains

    NARCIS (Netherlands)

    Wang, Yuxin; Tian, Yifu; Teixeira, André; Hulstijn, Joris; Tan, Yao-Hua

    Currently international supply chains are facing risks concerning faults in compliance, such as altering shipping documentations, fictitious inventory, and inter-company manipulations. In this paper a method to detect and diagnose fault scenarios regarding customs compliance in supply chains is

  15. 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.

  16. Stator fault detection for multi-phase machines with multiple reference frames transformation

    DEFF Research Database (Denmark)

    Bianchini, Claudio; Fornasiero, Emanuele; Matzen, T.N.

    2009-01-01

    The paper focuses on a new diagnostic index for fault detection of a five-phase permanent-magnet machine. This machine has been designed for fault tolerant applications, and it is characterized by a mutual inductance equal to zero and a high self inductance, in order to limit the short-circuit cu...

  17. Comparative analysis of neural network and regression based condition monitoring approaches for wind turbine fault detection

    DEFF Research Database (Denmark)

    Schlechtingen, Meik; Santos, Ilmar

    2011-01-01

    This paper presents the research results of a comparison of three different model based approaches for wind turbine fault detection in online SCADA data, by applying developed models to five real measured faults and anomalies. The regression based model as the simplest approach to build a normal...

  18. Usage of Fault Detection Isolation & Recovery (FDIR) in Constellation (CxP) Launch Operations

    Science.gov (United States)

    Ferrell, Rob; Lewis, Mark; Perotti, Jose; Oostdyk, Rebecca; Spirkovska, Lilly; Hall, David; Brown, Barbara

    2010-01-01

    This paper will explore the usage of Fault Detection Isolation & Recovery (FDIR) in the Constellation Exploration Program (CxP), in particular Launch Operations at Kennedy Space Center (KSC). NASA's Exploration Technology Development Program (ETDP) is currently funding a project that is developing a prototype FDIR to demonstrate the feasibility of incorporating FDIR into the CxP Ground Operations Launch Control System (LCS). An architecture that supports multiple FDIR tools has been formulated that will support integration into the CxP Ground Operation's Launch Control System (LCS). In addition, tools have been selected that provide fault detection, fault isolation, and anomaly detection along with integration between Flight and Ground elements.

  19. Fault Detection and Diagnosis System in Process industry Based on Big Data and WeChat

    Directory of Open Access Journals (Sweden)

    Sun Zengqiang

    2017-01-01

    Full Text Available The fault detection and diagnosis information in process industry can be received, anytime and anywhere, based on bigdata and WeChat with mobile phone, which got rid of constraints that can only check Distributed Control System (DCS in the central control room or look over in office. Then, fault detection, diagnosis information sharing can be provided, and what’s more, fault detection alarm range, code and inform time can be personalized. The pressure of managers who worked on process industry can be release with the mobile information system.

  20. A Novel Approach to Detect Faults Occurring During Power Swings by Abrupt Change of Impedance Trajectory

    DEFF Research Database (Denmark)

    Khodaparast, Jalal; Khederzadeh, M.; Silva, Filipe Miguel Faria da

    2017-01-01

    The main purpose of power swing blocking is to distinguish faults from power swings. However, faults occurred during a power swing should still be detected and cleared promptly. This paper proposes an index based on detecting abrupt jump of impedance trajectory by utilization of the predicting...... of Taylor expansion is used to decrease the corrugation effect of impedance estimation and increase the reliability of the proposed method. Furthermore, in order to increase the selectivity of the proposed method, the proposed index is armed with phase comparison logic to detect internal faults...

  1. 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.

  2. 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.

  3. In-flight Fault Detection and Isolation in Aircraft Flight Control Systems

    Science.gov (United States)

    Azam, Mohammad; Pattipati, Krishna; Allanach, Jeffrey; Poll, Scott; Patterson-Hine, Ann

    2005-01-01

    In this paper we consider the problem of test design for real-time fault detection and isolation (FDI) in the flight control system of fixed-wing aircraft. We focus on the faults that are manifested in the control surface elements (e.g., aileron, elevator, rudder and stabilizer) of an aircraft. For demonstration purposes, we restrict our focus on the faults belonging to nine basic fault classes. The diagnostic tests are performed on the features extracted from fifty monitored system parameters. The proposed tests are able to uniquely isolate each of the faults at almost all severity levels. A neural network-based flight control simulator, FLTZ(Registered TradeMark), is used for the simulation of various faults in fixed-wing aircraft flight control systems for the purpose of FDI.

  4. Incipient Fault Detection and Isolation of Field Devices in Nuclear Power Systems Using Principal Component Analysis

    International Nuclear Information System (INIS)

    Kaistha, Nitin; Upadhyaya, Belle R.

    2001-01-01

    An integrated method for the detection and isolation of incipient faults in common field devices, such as sensors and actuators, using plant operational data is presented. The approach is based on the premise that data for normal operation lie on a surface and abnormal situations lead to deviations from the surface in a particular way. Statistically significant deviations from the surface result in the detection of faults, and the characteristic directions of deviations are used for isolation of one or more faults from the set of typical faults. Principal component analysis (PCA), a multivariate data-driven technique, is used to capture the relationships in the data and fit a hyperplane to the data. The fault direction for each of the scenarios is obtained using the singular value decomposition on the state and control function prediction errors, and fault isolation is then accomplished from projections on the fault directions. This approach is demonstrated for a simulated pressurized water reactor steam generator system and for a laboratory process control system under single device fault conditions. Enhanced fault isolation capability is also illustrated by incorporating realistic nonlinear terms in the PCA data matrix

  5. Construction and selection of lifting-based multiwavelets for mechanical fault detection

    Science.gov (United States)

    Yuan, Jing; He, Zhengjia; Zi, Yanyang; Wei, Ying

    2013-11-01

    The essence of wavelet transforms is a similar measurement between the signal and the wavelet basis functions. Thus, the construction and selection of the proper wavelet basis functions similar to the fault feature and possessing good properties such as vanishing moments have vital importance to the effective fault diagnosis. In this paper, the construction of lifting-based adaptive multiwavelets with various vanishing moments and the selection rules for different mechanical fault detection are proposed. On the basis of the fixed cubic Hermite multiwavelets, lifting schemes are adopted to construct new changeable multiwavelets with diverse vanishing moments. Then, the defined local spectral entropy minimization rules are proposed to determine the optimum multiwavelets providing the proper vanishing moments, classified into the typical shaft faults, gear faults and rolling bearing faults. The proposed method is applied to incipient fault diagnosis of rolling bearing and gearbox fault diagnosis of rolling mill to verify its effectiveness and feasibility in comparison with different wavelet transforms and spectral kurtosis. The results show that the proposed method can act as a promising tool for mechanical fault detection.

  6. Metric Learning Method Aided Data-Driven Design of Fault Detection Systems

    Directory of Open Access Journals (Sweden)

    Guoyang Yan

    2014-01-01

    Full Text Available Fault detection is fundamental to many industrial applications. With the development of system complexity, the number of sensors is increasing, which makes traditional fault detection methods lose efficiency. Metric learning is an efficient way to build the relationship between feature vectors with the categories of instances. In this paper, we firstly propose a metric learning-based fault detection framework in fault detection. Meanwhile, a novel feature extraction method based on wavelet transform is used to obtain the feature vector from detection signals. Experiments on Tennessee Eastman (TE chemical process datasets demonstrate that the proposed method has a better performance when comparing with existing methods, for example, principal component analysis (PCA and fisher discriminate analysis (FDA.

  7. Observer-Based and Regression Model-Based Detection of Emerging Faults in Coal Mills

    DEFF Research Database (Denmark)

    Odgaard, Peter Fogh; Lin, Bao; Jørgensen, Sten Bay

    2006-01-01

    In order to improve the reliability of power plants it is important to detect fault as fast as possible. Doing this it is interesting to find the most efficient method. Since modeling of large scale systems is time consuming it is interesting to compare a model-based method with data driven ones....... In this paper three different fault detection approaches are compared using a example of a coal mill, where a fault emerges. The compared methods are based on: an optimal unknown input observer, static and dynamic regression model-based detections. The conclusion on the comparison is that observer-based scheme...... detects the fault 13 samples earlier than the dynamic regression model-based method, and that the static regression based method is not usable due to generation of far too many false detections....

  8. DYNAMIC SOFTWARE TESTING MODELS WITH PROBABILISTIC PARAMETERS FOR FAULT DETECTION AND ERLANG DISTRIBUTION FOR FAULT RESOLUTION DURATION

    Directory of Open Access Journals (Sweden)

    A. D. Khomonenko

    2016-07-01

    Full Text Available Subject of Research.Software reliability and test planning models are studied taking into account the probabilistic nature of error detection and discovering. Modeling of software testing enables to plan the resources and final quality at early stages of project execution. Methods. Two dynamic models of processes (strategies are suggested for software testing, using error detection probability for each software module. The Erlang distribution is used for arbitrary distribution approximation of fault resolution duration. The exponential distribution is used for approximation of fault resolution discovering. For each strategy, modified labeled graphs are built, along with differential equation systems and their numerical solutions. The latter makes it possible to compute probabilistic characteristics of the test processes and states: probability states, distribution functions for fault detection and elimination, mathematical expectations of random variables, amount of detected or fixed errors. Evaluation of Results. Probabilistic characteristics for software development projects were calculated using suggested models. The strategies have been compared by their quality indexes. Required debugging time to achieve the specified quality goals was calculated. The calculation results are used for time and resources planning for new projects. Practical Relevance. The proposed models give the possibility to use the reliability estimates for each individual module. The Erlang approximation removes restrictions on the use of arbitrary time distribution for fault resolution duration. It improves the accuracy of software test process modeling and helps to take into account the viability (power of the tests. With the use of these models we can search for ways to improve software reliability by generating tests which detect errors with the highest probability.

  9. System for detecting and limiting electrical ground faults within electrical devices

    Science.gov (United States)

    Gaubatz, Donald C.

    1990-01-01

    An electrical ground fault detection and limitation system for employment with a nuclear reactor utilizing a liquid metal coolant. Elongate electromagnetic pumps submerged within the liquid metal coolant and electrical support equipment experiencing an insulation breakdown occasion the development of electrical ground fault current. Without some form of detection and control, these currents may build to damaging power levels to expose the pump drive components to liquid metal coolant such as sodium with resultant undesirable secondary effects. Such electrical ground fault currents are detected and controlled through the employment of an isolated power input to the pumps and with the use of a ground fault control conductor providing a direct return path from the affected components to the power source. By incorporating a resistance arrangement with the ground fault control conductor, the amount of fault current permitted to flow may be regulated to the extent that the reactor may remain in operation until maintenance may be performed, notwithstanding the existence of the fault. Monitors such as synchronous demodulators may be employed to identify and evaluate fault currents for each phase of a polyphase power, and control input to the submerged pump and associated support equipment.

  10. Detection of Eccentricity Faults in Five-Phase Ferrite-PM Assisted Synchronous Reluctance Machines

    Directory of Open Access Journals (Sweden)

    Carlos López-Torres

    2017-05-01

    Full Text Available Air gap eccentricity faults in five-phase ferrite-assisted synchronous reluctance motors (fPMa-SynRMs tend to distort the magnetic flux in the air gap, which in turn affects the spectral content of both the stator currents and the ZSVC (zero-sequence voltage component. However, there is a lack of research dealing with the topic of fault diagnosis in multi-phase PMa-SynRMs, and in particular, those focused on detecting eccentricity faults. An analysis of the spectral components of the line currents and the ZSVC allows the development of fault diagnosis algorithms to detect eccentricity faults. The effect of the operating conditions is also analyzed, since this paper shows that it has a non-negligible impact on the effectivity and sensitivity of the diagnosis based on an analysis of the stator currents and the ZSVC. To this end, different operating conditions are analyzed. The paper also evaluates the influence of the operating conditions on the harmonic content of the line currents and the ZSVC, and determines the most suitable operating conditions to enhance the sensitivity of the analyzed methods. Finally, fault indicators employed to detect eccentricity faults, which are based on the spectral content of the stator currents and the ZSVC, are derived and their performance is assessed. The approach presented in this work may be useful for developing fault diagnosis strategies based on the acquisition and subsequent analysis and interpretation of the spectral content of the line currents and the ZSVC.

  11. Fault Detection and Diagnosis in Process Data Using Support Vector Machines

    Directory of Open Access Journals (Sweden)

    Fang Wu

    2014-01-01

    Full Text Available For the complex industrial process, it has become increasingly challenging to effectively diagnose complicated faults. In this paper, a combined measure of the original Support Vector Machine (SVM and Principal Component Analysis (PCA is provided to carry out the fault classification, and compare its result with what is based on SVM-RFE (Recursive Feature Elimination method. RFE is used for feature extraction, and PCA is utilized to project the original data onto a lower dimensional space. PCA T2, SPE statistics, and original SVM are proposed to detect the faults. Some common faults of the Tennessee Eastman Process (TEP are analyzed in terms of the practical system and reflections of the dataset. PCA-SVM and SVM-RFE can effectively detect and diagnose these common faults. In RFE algorithm, all variables are decreasingly ordered according to their contributions. The classification accuracy rate is improved by choosing a reasonable number of features.

  12. System and method for motor fault detection using stator current noise cancellation

    Science.gov (United States)

    Zhou, Wei; Lu, Bin; Nowak, Michael P.; Dimino, Steven A.

    2010-12-07

    A system and method for detecting incipient mechanical motor faults by way of current noise cancellation is disclosed. The system includes a controller configured to detect indicia of incipient mechanical motor faults. The controller further includes a processor programmed to receive a baseline set of current data from an operating motor and define a noise component in the baseline set of current data. The processor is also programmed to acquire at least on additional set of real-time operating current data from the motor during operation, redefine the noise component present in each additional set of real-time operating current data, and remove the noise component from the operating current data in real-time to isolate any fault components present in the operating current data. The processor is then programmed to generate a fault index for the operating current data based on any isolated fault components.

  13. System and method for bearing fault detection using stator current noise cancellation

    Science.gov (United States)

    Zhou, Wei; Lu, Bin; Habetler, Thomas G.; Harley, Ronald G.; Theisen, Peter J.

    2010-08-17

    A system and method for detecting incipient mechanical motor faults by way of current noise cancellation is disclosed. The system includes a controller configured to detect indicia of incipient mechanical motor faults. The controller further includes a processor programmed to receive a baseline set of current data from an operating motor and define a noise component in the baseline set of current data. The processor is also programmed to repeatedly receive real-time operating current data from the operating motor and remove the noise component from the operating current data in real-time to isolate any fault components present in the operating current data. The processor is then programmed to generate a fault index for the operating current data based on any isolated fault components.

  14. Weak fault detection and health degradation monitoring using customized standard multiwavelets

    Science.gov (United States)

    Yuan, Jing; Wang, Yu; Peng, Yizhen; Wei, Chenjun

    2017-09-01

    Due to the nonobvious symptoms contaminated by a large amount of background noise, it is challenging to beforehand detect and predictively monitor the weak faults for machinery security assurance. Multiwavelets can act as adaptive non-stationary signal processing tools, potentially viable for weak fault diagnosis. However, the signal-based multiwavelets suffer from such problems as the imperfect properties missing the crucial orthogonality, the decomposition distortion impossibly reflecting the relationships between the faults and signatures, the single objective optimization and independence for fault prognostic. Thus, customized standard multiwavelets are proposed for weak fault detection and health degradation monitoring, especially the weak fault signature quantitative identification. First, the flexible standard multiwavelets are designed using the construction method derived from scalar wavelets, seizing the desired properties for accurate detection of weak faults and avoiding the distortion issue for feature quantitative identification. Second, the multi-objective optimization combined three dimensionless indicators of the normalized energy entropy, normalized singular entropy and kurtosis index is introduced to the evaluation criterions, and benefits for selecting the potential best basis functions for weak faults without the influence of the variable working condition. Third, an ensemble health indicator fused by the kurtosis index, impulse index and clearance index of the original signal along with the normalized energy entropy and normalized singular entropy by the customized standard multiwavelets is achieved using Mahalanobis distance to continuously monitor the health condition and track the performance degradation. Finally, three experimental case studies are implemented to demonstrate the feasibility and effectiveness of the proposed method. The results show that the proposed method can quantitatively identify the fault signature of a slight rub on

  15. 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.

  16. Fault Detection for Network Control Systems with Multiple Communication Delays and Stochastic Missing Measurements

    Directory of Open Access Journals (Sweden)

    Jie Zhang

    2014-01-01

    Full Text Available This paper is concerned with fault detection problem for a class of network control systems (NCSs with multiple communication delays and stochastic missing measurements. The missing measurement phenomenon occurs in a random way and the occurrence probability for each measurement output is governed by an individual random variable. Besides, the multiple communication delay phenomenon reflects that networked control systems have different communication delays when the signals are transferred via different channels. We aim to design a fault detection filter so that the overall fault detection dynamics is exponentially stable in the mean square. By constructing proper Lyapunov-Krasovskii functional, we acquire sufficient conditions to guarantee the stability of the fault detection filter for the discrete systems, and the filter parameters are also derived by solving linear matrix inequality. Finally, an illustrative example is provided to show the usefulness and effectiveness of the proposed design method.

  17. Detection of Partial Demagnetization Fault in PMSMs Operating under Nonstationary Conditions

    DEFF Research Database (Denmark)

    Wang, Chao; Delgado Prieto, Miguel; Romeral, Luis

    2016-01-01

    Demagnetization fault detection of in-service Permanent Magnet Synchronous Machines (PMSMs) is a challenging task because most PMSMs operate under nonstationary circumstances in industrial applications. A novel approach based on tracking characteristic orders of stator current using Vold...

  18. Framework for the Design and Implementation of Fault Detection and Isolation, Phase II

    Data.gov (United States)

    National Aeronautics and Space Administration — SySense, Inc. proposes to develop a framework for the design and implementation of fault detection and isolation (FDI) systems. The framework will include protocols...

  19. Fault Detection and Isolation of Satellite Formations using a Ground Station, Phase I

    Data.gov (United States)

    National Aeronautics and Space Administration — This proposal is for the development a fault detection and isolation (FDI) algorithm for a formation of satellites but processed at a ground station. The algorithm...

  20. Fault detection in multiply-redundant measurement systems via sequential testing

    International Nuclear Information System (INIS)

    Ray, A.

    1988-01-01

    The theory and application of a sequential test procedure for fault detection and isolation. The test procedure is suited for development of intelligent instrumentation in strategic processes like aircraft and nuclear plants where redundant measurements are usually available for individual critical variables. The test procedure consists of: (1) a generic redundancy management procedure which is essentially independent of the fault detection strategy and measurement noise statistics, and (2) a modified version of sequential probability ratio test algorithm for fault detection and isolation, which functions within the framework of this redundancy management procedure. The sequential test procedure is suitable for real-time applications using commercially available microcomputers and its efficacy has been verified by online fault detection in an operating nuclear reactor. 15 references

  1. A Kalman Filter Based Technique for Stator Turn-Fault Detection of the Induction Motors

    Science.gov (United States)

    Ghanbari, Teymoor; Samet, Haidar

    2017-11-01

    Monitoring of the Induction Motors (IMs) through stator current for different faults diagnosis has considerable economic and technical advantages in comparison with the other techniques in this content. Among different faults of an IM, stator and bearing faults are more probable types, which can be detected by analyzing signatures of the stator currents. One of the most reliable indicators for fault detection of IMs is lower sidebands of power frequency in the stator currents. This paper deals with a novel simple technique for detecting stator turn-fault of the IMs. Frequencies of the lower sidebands are determined using the motor specifications and their amplitudes are estimated by a Kalman Filter (KF). Instantaneous Total Harmonic Distortion (ITHD) of these harmonics is calculated. Since variation of the ITHD for the three-phase currents is considerable in case of stator turn-fault, the fault can be detected using this criterion, confidently. Different simulation results verify high performance of the proposed method. The performance of the method is also confirmed using some experiments.

  2. Fault detection and diagnosis using statistical control charts and artificial neural networks

    International Nuclear Information System (INIS)

    Leger, R.P.; Garland, W.J.; Poehlman, W.F.S.

    1995-01-01

    In order to operate a successful plant or process, continuous improvement must be made in the areas of safety, quality and reliability. Central to this continuous improvement is the early or proactive detection and correct diagnosis of process faults. This research examines the feasibility of using Cumulative Summation (CUSUM) Control Charts and artificial neural networks together for fault detection and diagnosis (FDD). The proposed FDD strategy was tested on a model of the heat transport system of a CANDU nuclear reactor. The results of the investigation indicate that a FDD system using CUSUM Control Charts and a Radial Basis Function (RBF) neural network is not only feasible but also of promising potential. The control charts and neural network are linked together by using a characteristic fault signature pattern for each fault which is to be detected and diagnosed. When tested, the system was able to eliminate all false alarms at steady state, promptly detect 6 fault conditions and correctly diagnose 5 out of the 6 faults. The diagnosis for the sixth fault was inconclusive. (author). 9 refs., 6 tabs., 7 figs

  3. Fault Detection for Shipboard Monitoring – Volterra Kernel and Hammerstein Model Approaches

    DEFF Research Database (Denmark)

    Lajic, Zoran; Blanke, Mogens; Nielsen, Ulrik Dam

    2009-01-01

    In this paper nonlinear fault detection for in-service monitoring and decision support systems for ships will be presented. The ship is described as a nonlinear system, and the stochastic wave elevation and the associated ship responses are conveniently modelled in frequency domain. The transform....... The transformation from time domain to frequency domain has been conducted by use of Volterra theory. The paper takes as an example fault detection of a containership on which a decision support system has been installed....

  4. Detection of Naturally Occurring Gear and Bearing Faults in a Helicopter Drivetrain

    Science.gov (United States)

    2014-01-01

    Detection of Naturally Occurring Gear and Bearing Faults in a Helicopter Drivetrain by Kelsen E. LaBerge, Eric C. Ames, and Brian D. Dykas...5066 ARL-TR-6795 January 2014 Detection of Naturally Occurring Gear and Bearing Faults in a Helicopter Drivetrain Kelsen E. LaBerge...ELEMENT NUMBER 6. AUTHOR(S) Kelsen E. LaBerge, Eric C. Ames, and Brian D. Dykas 5d. PROJECT NUMBER 5e. TASK NUMBER 5f. WORK UNIT NUMBER

  5. A system for the non-destructive detection of faults in safety fuses using radioisotopes

    International Nuclear Information System (INIS)

    Goncalves, D.

    1980-01-01

    Design of an equipment for on line detection of faults in the safety fuses for conventional explosives employing transmission of #betta#-radiation is reported. The faults are detected by an ion-chamber based on the variation of the intensity of the beta particles transmitted through the fuse during its passage across the collimated beam. Strontium-90 encapsulated in stainless steel or aluminum is used as the #betta#-source. An electrical signal corresponding the fault is obtained by subtraction of an external current, that is equivalent to the output of the ion-chamber in the presence of faultless fuse. (Author) [pt

  6. Methods and apparatus using commutative error detection values for fault isolation in multiple node computers

    Science.gov (United States)

    Almasi, Gheorghe [Ardsley, NY; Blumrich, Matthias Augustin [Ridgefield, CT; Chen, Dong [Croton-On-Hudson, NY; Coteus, Paul [Yorktown, NY; Gara, Alan [Mount Kisco, NY; Giampapa, Mark E [Irvington, NY; Heidelberger, Philip [Cortlandt Manor, NY; Hoenicke, Dirk I [Ossining, NY; Singh, Sarabjeet [Mississauga, CA; Steinmacher-Burow, Burkhard D [Wernau, DE; Takken, Todd [Brewster, NY; Vranas, Pavlos [Bedford Hills, NY

    2008-06-03

    Methods and apparatus perform fault isolation in multiple node computing systems using commutative error detection values for--example, checksums--to identify and to isolate faulty nodes. When information associated with a reproducible portion of a computer program is injected into a network by a node, a commutative error detection value is calculated. At intervals, node fault detection apparatus associated with the multiple node computer system retrieve commutative error detection values associated with the node and stores them in memory. When the computer program is executed again by the multiple node computer system, new commutative error detection values are created and stored in memory. The node fault detection apparatus identifies faulty nodes by comparing commutative error detection values associated with reproducible portions of the application program generated by a particular node from different runs of the application program. Differences in values indicate a possible faulty node.

  7. Potential fault region detection in TFDS images based on convolutional neural network

    Science.gov (United States)

    Sun, Junhua; Xiao, Zhongwen

    2016-10-01

    In recent years, more than 300 sets of Trouble of Running Freight Train Detection System (TFDS) have been installed on railway to monitor the safety of running freight trains in China. However, TFDS is simply responsible for capturing, transmitting, and storing images, and fails to recognize faults automatically due to some difficulties such as such as the diversity and complexity of faults and some low quality images. To improve the performance of automatic fault recognition, it is of great importance to locate the potential fault areas. In this paper, we first introduce a convolutional neural network (CNN) model to TFDS and propose a potential fault region detection system (PFRDS) for simultaneously detecting four typical types of potential fault regions (PFRs). The experimental results show that this system has a higher performance of image detection to PFRs in TFDS. An average detection recall of 98.95% and precision of 100% are obtained, demonstrating the high detection ability and robustness against various poor imaging situations.

  8. Efficient drilling problem detection. Early fault detection by the combination of physical models and artificial intelligence

    Energy Technology Data Exchange (ETDEWEB)

    Nyboe, Roar

    2009-09-15

    The drilling of an oil or gas well is an expensive undertaking. Hence, it is not surprising that mistakes and accidents during drilling incur a high cost. Accidents could result in the loss of expensive equipment and subsequent delays setting back the operation for days or weeks and thus running up large bills on rig-time and personnel hours. Some types of accidents also pose a risk to the personnel or the environment. In this dissertation we study alarm systems which could give the driller an early warning of upcoming problems, and thus provide time to avoid these accidents. We explore alarm systems which combine advanced physical models of the well and drilling process with artificial intelligence and time series analysis. Finally, we determine the advantages as well as the challenges of this approach. It is our hope that this dissertation is accessible to both practitioners in machine learning and control engineering, as well as to petroleum engineers with a passing familiarity with machine learning. Hence this dissertation starts with a quick introduction to drilling problems and some terms from time series analysis and machine learning. We then briefly describe the theory of observer-based fault detection and isolation. Theories of supervisory control systems are also introduced, as these concern both the choice of algorithms and how AI-based alarm systems integrate with the rest of the operation. From chapter 6 and onward, the challenges to fault detection in drilling are discussed. We focus on clarifying what restrictions the available training data put on our choice of machine learning methods. In chapter 8 and 9, we propose ways to combine machine learning and observer-based fault detection. Experimental results are presented in chapter 10, before we end with concluding remarks in chapter 11. Our main conclusion, reflected in our experimental results, is that physical models and artificial intelligence can be combined to produce hybrid alarm systems that

  9. Magnetoplasmadynamic thruster applications

    International Nuclear Information System (INIS)

    Pawlik, E.V.

    1976-01-01

    Advance study activities within NASA indicate that electric propulsion will be required to make certain types of potential missions feasible. The large power levels under consideration make magnetoplasmadynamic thrusters a good candidate for these applications since this type of electric thruster is best suited to operation at high power levels. The status of the magnetoplasmadynamic thruster is examined and compared to the ion thruster which also is a candidate. The use of these two types of electric propulsion devices for orbit raising of a self-powered large satellite is examined from a cost standpoint. In addition the use of nuclear electric propulsion is described for use as both a near-earth space tug and for an interplanetary exploration vehicle. These preliminary examinations indicate that the magnetoplasmadynamic thruster is the lowest cost thruster and therefore merits serious consideration for these applications

  10. Fault detection and classification in electrical power transmission system using artificial neural network.

    Science.gov (United States)

    Jamil, Majid; Sharma, Sanjeev Kumar; Singh, Rajveer

    2015-01-01

    This paper focuses on the detection and classification of the faults on electrical power transmission line using artificial neural networks. The three phase currents and voltages of one end are taken as inputs in the proposed scheme. The feed forward neural network along with back propagation algorithm has been employed for detection and classification of the fault for analysis of each of the three phases involved in the process. A detailed analysis with varying number of hidden layers has been performed to validate the choice of the neural network. The simulation results concluded that the present method based on the neural network is efficient in detecting and classifying the faults on transmission lines with satisfactory performances. The different faults are simulated with different parameters to check the versatility of the method. The proposed method can be extended to the Distribution network of the Power System. The various simulations and analysis of signals is done in the MATLAB(®) environment.

  11. Fault detection and diagnosis in nonlinear systems a differential and algebraic viewpoint

    CERN Document Server

    Martinez-Guerra, Rafael

    2014-01-01

    The high reliability required in industrial processes has created the necessity of detecting abnormal conditions, called faults, while processes are operating. The term fault generically refers to any type of process degradation, or degradation in equipment performance because of changes in the process's physical characteristics, process inputs or environmental conditions. This book is about the fundamentals of fault detection and diagnosis in a variety of nonlinear systems which are represented by ordinary differential equations. The fault detection problem is approached from a differential algebraic viewpoint, using residual generators based upon high-gain nonlinear auxiliary systems (‘observers’). A prominent role is played by the type of mathematical tools that will be used, requiring knowledge of differential algebra and differential equations. Specific theorems tailored to the needs of the problem-solving procedures are developed and proved. Applications to real-world problems, both with constant an...

  12. FAULT DETECTION AND DIAGNOSIS ON A PWM INVERTER BY DIFFERENT TECHNIQUES

    Directory of Open Access Journals (Sweden)

    S. Chafei

    2008-06-01

    Full Text Available This paper investigates the use of different techniques for fault detection in voltage-fed asynchronous machine drive systems. With the proposed techniques it is possible to detect and identify the power switch in which the fault has occurred. A diagnosis system which uses only the input variables of the drive is presented. It is based on the analysis of the current-vector trajectory, of the instantaneous frequency in faulty mode, and the evaluation of machine state variables which are processed due to the machine control algorithm. With this algorithm a fast an reliable fault detection can be realized. Furthermore limited drive operation in case of a fault mode will be discussed. All obtained results are based on computer simulation. These knowledge based methods have been test in simulation.

  13. Fault Detection of Aircraft Cable via Spread Spectrum Time Domain Reflectometry

    Directory of Open Access Journals (Sweden)

    Xudong SHI

    2014-03-01

    Full Text Available As the airplane cable fault detection based on TDR (time domain reflectometry is affected easily by various noise signals, which makes the reflected signal attenuate and distort heavily, failing to locate the fault. In order to solve these problems, a method of spread spectrum time domain reflectometry (SSTDR is introduced in this paper, taking the advantage of the sharp peak of correlation function. The test signal is generated from ML sequence (MLS modulated by sine wave in the same frequency. Theoretically, the test signal has the very high immunity of noise, which can be applied with excellent precision to fault location on the aircraft cable. In this paper, the method of SSTDR was normally simulated in MATLAB. Then, an experimental setup, based on LabVIEW, was organized to detect and locate the fault on the aircraft cable. It has been demonstrated that SSTDR has the high immunity of noise, reducing some detection errors effectively.

  14. Fault detection, isolation, and diagnosis of self-validating multifunctional sensors

    Science.gov (United States)

    Yang, Jing-li; Chen, Yin-sheng; Zhang, Li-li; Sun, Zhen

    2016-06-01

    A novel fault detection, isolation, and diagnosis (FDID) strategy for self-validating multifunctional sensors is presented in this paper. The sparse non-negative matrix factorization-based method can effectively detect faults by using the squared prediction error (SPE) statistic, and the variables contribution plots based on SPE statistic can help to locate and isolate the faulty sensitive units. The complete ensemble empirical mode decomposition is employed to decompose the fault signals to a series of intrinsic mode functions (IMFs) and a residual. The sample entropy (SampEn)-weighted energy values of each IMFs and the residual are estimated to represent the characteristics of the fault signals. Multi-class support vector machine is introduced to identify the fault mode with the purpose of diagnosing status of the faulty sensitive units. The performance of the proposed strategy is compared with other fault detection strategies such as principal component analysis, independent component analysis, and fault diagnosis strategies such as empirical mode decomposition coupled with support vector machine. The proposed strategy is fully evaluated in a real self-validating multifunctional sensors experimental system, and the experimental results demonstrate that the proposed strategy provides an excellent solution to the FDID research topic of self-validating multifunctional sensors.

  15. Fault detection, isolation, and diagnosis of self-validating multifunctional sensors.

    Science.gov (United States)

    Yang, Jing-Li; Chen, Yin-Sheng; Zhang, Li-Li; Sun, Zhen

    2016-06-01

    A novel fault detection, isolation, and diagnosis (FDID) strategy for self-validating multifunctional sensors is presented in this paper. The sparse non-negative matrix factorization-based method can effectively detect faults by using the squared prediction error (SPE) statistic, and the variables contribution plots based on SPE statistic can help to locate and isolate the faulty sensitive units. The complete ensemble empirical mode decomposition is employed to decompose the fault signals to a series of intrinsic mode functions (IMFs) and a residual. The sample entropy (SampEn)-weighted energy values of each IMFs and the residual are estimated to represent the characteristics of the fault signals. Multi-class support vector machine is introduced to identify the fault mode with the purpose of diagnosing status of the faulty sensitive units. The performance of the proposed strategy is compared with other fault detection strategies such as principal component analysis, independent component analysis, and fault diagnosis strategies such as empirical mode decomposition coupled with support vector machine. The proposed strategy is fully evaluated in a real self-validating multifunctional sensors experimental system, and the experimental results demonstrate that the proposed strategy provides an excellent solution to the FDID research topic of self-validating multifunctional sensors.

  16. Improved Sensor Fault Detection, Isolation, and Mitigation Using Multiple Observers Approach

    Science.gov (United States)

    Wang, Zheng; Anand, D. M.; Moyne, J.; Tilbury, D. M.

    2017-01-01

    Traditional Fault Detection and Isolation (FDI) methods analyze a residual signal to detect and isolate sensor faults. The residual signal is the difference between the sensor measurements and the estimated outputs of the system based on an observer. The traditional residual-based FDI methods, however, have some limitations. First, they require that the observer has reached its steady state. In addition, residual-based methods may not detect some sensor faults, such as faults on critical sensors that result in an unobservable system. Furthermore, the system may be in jeopardy if actions required for mitigating the impact of the faulty sensors are not taken before the faulty sensors are identified. The contribution of this paper is to propose three new methods to address these limitations. Faults that occur during the observers' transient state can be detected by analyzing the convergence rate of the estimation error. Open-loop observers, which do not rely on sensor information, are used to detect faults on critical sensors. By switching among different observers, we can potentially mitigate the impact of the faulty sensor during the FDI process. These three methods are systematically integrated with a previously developed residual-based method to provide an improved FDI and mitigation capability framework. The overall approach is validated mathematically, and the effectiveness of the overall approach is demonstrated through simulation on a 5-state suspension system. PMID:28924303

  17. A Design Space Exploration Framework for ANN-Based Fault Detection in Hardware Systems

    Directory of Open Access Journals (Sweden)

    Andreas G. Savva

    2017-01-01

    Full Text Available This work presents a design exploration framework for developing a high level Artificial Neural Network (ANN for fault detection in hardware systems. ANNs can be used for fault detection purposes since they have excellent characteristics such as generalization capability, robustness, and fault tolerance. Designing an ANN in order to be used for fault detection purposes includes different parameters. Through this work, those parameters are presented and analyzed based on simulations. Moreover, after the development of the ANN, in order to evaluate it, a case study scenario based on Networks on Chip is used for detection of interrouter link faults. Simulation results with various synthetic traffic models show that the proposed work can detect up to 96–99% of interrouter link faults with a delay less than 60 cycles. Added to this, the size of the ANN is kept relatively small and they can be implemented in hardware easily. Synthesis results indicate an estimated amount of 0.0523 mW power consumption per neuron for the implemented ANN when computing a complete cycle.

  18. Fault Detection and Isolation using Multi Objective Controller Design Techniques

    DEFF Research Database (Denmark)

    Stoustrup, Jakob; Niemann, Hans Henrik

    1996-01-01

    Abstract: This paper describes a method for designing fault detectionand isolation filters. The method is multi objective in the sense thatit follows optimization with arbitrarily mixed criteria specified ine.g. the QTR H-infinity or the QTR H^2 norm. Moreover,the involved optimization yields less...

  19. Fault evaluation and adaptive threshold detection of helicopter pilot ...

    African Journals Online (AJOL)

    Hitherto, in the field of aerospace science and industry, some acceptable results from control behavior of human operator (pilot), are caught using usual methods. However, very fewer research, has been done based on personal characteristics. The performed investigations, show that many of happened faults (especially in ...

  20. Similarity ratio analysis for early stage fault detection with optical emission spectrometer in plasma etching process.

    Science.gov (United States)

    Yang, Jie; McArdle, Conor; Daniels, Stephen

    2014-01-01

    A Similarity Ratio Analysis (SRA) method is proposed for early-stage Fault Detection (FD) in plasma etching processes using real-time Optical Emission Spectrometer (OES) data as input. The SRA method can help to realise a highly precise control system by detecting abnormal etch-rate faults in real-time during an etching process. The method processes spectrum scans at successive time points and uses a windowing mechanism over the time series to alleviate problems with timing uncertainties due to process shift from one process run to another. A SRA library is first built to capture features of a healthy etching process. By comparing with the SRA library, a Similarity Ratio (SR) statistic is then calculated for each spectrum scan as the monitored process progresses. A fault detection mechanism, named 3-Warning-1-Alarm (3W1A), takes the SR values as inputs and triggers a system alarm when certain conditions are satisfied. This design reduces the chance of false alarm, and provides a reliable fault reporting service. The SRA method is demonstrated on a real semiconductor manufacturing dataset. The effectiveness of SRA-based fault detection is evaluated using a time-series SR test and also using a post-process SR test. The time-series SR provides an early-stage fault detection service, so less energy and materials will be wasted by faulty processing. The post-process SR provides a fault detection service with higher reliability than the time-series SR, but with fault testing conducted only after each process run completes.

  1. Enhanced detection of rolling element bearing fault based on stochastic resonance

    Science.gov (United States)

    Zhang, Xiaofei; Hu, Niaoqing; Cheng, Zhe; Hu, Lei

    2012-11-01

    Early bearing faults can generate a series of weak impacts. All the influence factors in measurement may degrade the vibration signal. Currently, bearing fault enhanced detection method based on stochastic resonance(SR) is implemented by expensive computation and demands high sampling rate, which requires high quality software and hardware for fault diagnosis. In order to extract bearing characteristic frequencies component, SR normalized scale transform procedures are presented and a circuit module is designed based on parameter-tuning bistable SR. In the simulation test, discrete and analog sinusoidal signals under heavy noise are enhanced by SR normalized scale transform and circuit module respectively. Two bearing fault enhanced detection strategies are proposed. One is realized by pure computation with normalized scale transform for sampled vibration signal, and the other is carried out by designed SR hardware with circuit module for analog vibration signal directly. The first strategy is flexible for discrete signal processing, and the second strategy demands much lower sampling frequency and less computational cost. The application results of the two strategies on bearing inner race fault detection of a test rig show that the local signal to noise ratio of the characteristic components obtained by the proposed methods are enhanced by about 50% compared with the band pass envelope analysis for the bearing with weaker fault. In addition, helicopter transmission bearing fault detection validates the effectiveness of the enhanced detection strategy with hardware. The combination of SR normalized scale transform and circuit module can meet the need of different application fields or conditions, thus providing a practical scheme for enhanced detection of bearing fault.

  2. Similarity ratio analysis for early stage fault detection with optical emission spectrometer in plasma etching process.

    Directory of Open Access Journals (Sweden)

    Jie Yang

    Full Text Available A Similarity Ratio Analysis (SRA method is proposed for early-stage Fault Detection (FD in plasma etching processes using real-time Optical Emission Spectrometer (OES data as input. The SRA method can help to realise a highly precise control system by detecting abnormal etch-rate faults in real-time during an etching process. The method processes spectrum scans at successive time points and uses a windowing mechanism over the time series to alleviate problems with timing uncertainties due to process shift from one process run to another. A SRA library is first built to capture features of a healthy etching process. By comparing with the SRA library, a Similarity Ratio (SR statistic is then calculated for each spectrum scan as the monitored process progresses. A fault detection mechanism, named 3-Warning-1-Alarm (3W1A, takes the SR values as inputs and triggers a system alarm when certain conditions are satisfied. This design reduces the chance of false alarm, and provides a reliable fault reporting service. The SRA method is demonstrated on a real semiconductor manufacturing dataset. The effectiveness of SRA-based fault detection is evaluated using a time-series SR test and also using a post-process SR test. The time-series SR provides an early-stage fault detection service, so less energy and materials will be wasted by faulty processing. The post-process SR provides a fault detection service with higher reliability than the time-series SR, but with fault testing conducted only after each process run completes.

  3. A universal, fault-tolerant, non-linear analytic network for modeling and fault detection

    Energy Technology Data Exchange (ETDEWEB)

    Mott, J.E. [Advanced Modeling Techniques Corp., Idaho Falls, ID (United States); King, R.W.; Monson, L.R.; Olson, D.L.; Staffon, J.D. [Argonne National Lab., Idaho Falls, ID (United States)

    1992-03-06

    The similarities and differences of a universal network to normal neural networks are outlined. The description and application of a universal network is discussed by showing how a simple linear system is modeled by normal techniques and by universal network techniques. A full implementation of the universal network as universal process modeling software on a dedicated computer system at EBR-II is described and example results are presented. It is concluded that the universal network provides different feature recognition capabilities than a neural network and that the universal network can provide extremely fast, accurate, and fault-tolerant estimation, validation, and replacement of signals in a real system.

  4. A universal, fault-tolerant, non-linear analytic network for modeling and fault detection

    International Nuclear Information System (INIS)

    Mott, J.E.; King, R.W.; Monson, L.R.; Olson, D.L.; Staffon, J.D.

    1992-01-01

    The similarities and differences of a universal network to normal neural networks are outlined. The description and application of a universal network is discussed by showing how a simple linear system is modeled by normal techniques and by universal network techniques. A full implementation of the universal network as universal process modeling software on a dedicated computer system at EBR-II is described and example results are presented. It is concluded that the universal network provides different feature recognition capabilities than a neural network and that the universal network can provide extremely fast, accurate, and fault-tolerant estimation, validation, and replacement of signals in a real system

  5. Detection of frictional heat in seismic faults by coal reflectance

    Science.gov (United States)

    Kitamura, M.; Mukoyoshi, H.; Fulton, P. M.; Hirose, T.

    2012-12-01

    Quantitative assessment of heat generation along a fault during coseismic faulting is of primary importance in understanding the dynamics of earthquakes. Evidence of substantial frictional heating along a fault is also a reliable indicator determining whether a fault has slipped at high velocity in the past, which is crucial for assessing earthquake and tsunami hazard. The reflectance measurement of vitrinite (one of the primary components of coals) has been considered a possible geothermometer of fault zones, especially in accretionary wedges where vitrinite fragments are common [e.g., Sakaguchi et al., 2011]. Under normal burial conditions, vitrinite reflectance (Ro) increases by irreversible maturation reaction as temperature is elevated and thus sensitively records the maximum temperature to which the vitrinite is subjected. However, the commonly used kinetic models of vitrinite maturation [e.g., Sweeney and Burnham, 1990] may not yield accurate estimates of the peak temperature in a fault zone resulting from fast frictional heating rates [Fulton and Harris, 2012]. Whether or not coal can mature in typical earthquake rise time (e.g., ~10 seconds) remains uncertain. Here we present the results of friction experiments aimed at revealing coal maturation by frictional heat generated at slip velocities representative of natural earthquakes of up to 1.3 m/s. All friction experiments were conducted on a mixture of 90 wt% quartz powder and 10 wt% coal grains for simulated fault gouge at three different velocities of 0.0013 m/s, 0.65 m/s and 1.3 m/s, a constant normal stress of 1.0 MPa and ~15 m displacement under anoxic, dry nitrogen atmosphere at room temperature. We also measured temperature in the gouge zone during faulting by thermocouples. The initial coal fragments consist of vitrinite, inertinite and liptinite. Although liptinite was easy to identify microscopically, it was difficult to discriminate between vitrinite and inertinite grains as their grain size

  6. Fault Detection based on MCSA for a 400Hz Asynchronous Motor for Airborne Applications

    Directory of Open Access Journals (Sweden)

    Steffen Haus

    2013-01-01

    Full Text Available Future health monitoring concepts in different fields of engineering require reliable fault detection to avoid unscheduled machine downtime. Diagnosis of electrical induction machines for industrial applications is widely discussed in literature. In aviation industry, this topic is still only rarely discussed.A common approach to health monitoring for electrical induction machines is to use Motor Current Signature Analysis (MCSA based on a Fast Fourier Transform (FFT. Research results on this topic are available for comparatively large motors, where the power supply is typically based on 50Hz alternating current, which is the general power supply frequency for industrial applications.In this paper, transferability to airborne applications, where the power supply is 400Hz, is assessed. Three phase asynchronous motors are used to analyse detectability of different motor faults. The possibility to transfer fault detection results from 50Hz to 400Hz induction machines is the main question answered in this research work. 400Hz power supply frequency requires adjusted motor design, causing increased motor speed compared to 50Hz supply frequency. The motor used for experiments in this work is a 800W motor with 200V phase to phase power supply, powering an avionic fan. The fault cases to be examined are a bearing fault, a rotor unbalance, a stator winding fault, a broken rotor bar and a static air gap eccentricity. These are the most common faults in electrical induction machines which can cause machine downtime. The focus of the research work is the feasibility of the application of MCSA for small scale, high speed motor design, using the Fourier spectra of the current signal.Detectability is given for all but the bearing fault, although rotor unbalance can only be detected in case of severe damage level. Results obtained in the experiments are interpreted with respect to the motor design. Physical interpretation are given in case the results differ

  7. Real-time fault detection of braiding ropes using recognition methods

    Science.gov (United States)

    Matela, Lukas

    2004-10-01

    Formation of this paper is evoked by solving of device that is able to detect faults of braiding ropes in real-time. Many various inspection devices for textile industry were developed. However, rope-producing textile company has come with demand of intelligent inspection device that is able to detect faults in finishing process. The winding speeds are 50 - 200 m/min. Nowadays commercial devices are focused on textile fabrics (weaving or knitting) and they are only able to detect basic faults (holes, dirty and oil spots). Considering textile structure faults are possible to find in several research papers, however, for specific types of textiles or for slow processes only. The inspection device, which has been developed in our laboratory, is able to work with high winding speeds of rope. The device is based on fast line-scan camera with Camera-Link interface. The goal of the project was to search three basic structure faults: missing strand, strands pulled tight and stitch irregularity. The principle of fault detection is based on gathering the most suitable symptoms that are used for recognition methods. These methods are very successful for speech recognition and using them even bring us better results than using neural networks. This paper shows the way of finding the most suitable symptoms, their statistical evaluation and decision making algorithms. The most important step is reducing the problem from time-consuming image processing to one-dimensional signal processing.

  8. 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.

  9. A geometric approach for fault detection and isolation of stator short circuit failure in a single asynchronous machine

    KAUST Repository

    Khelouat, Samir

    2012-06-01

    This paper deals with the problem of detection and isolation of stator short-circuit failure in a single asynchronous machine using a geometric approach. After recalling the basis of the geometric approach for fault detection and isolation in nonlinear systems, we will study some structural properties which are fault detectability and isolation fault filter existence. We will then design filters for residual generation. We will consider two approaches: a two-filters structure and a single filter structure, both aiming at generating residuals which are sensitive to one fault and insensitive to the other faults. Some numerical tests will be presented to illustrate the efficiency of the method.

  10. Methodology for fault detection in induction motors via sound and vibration signals

    Science.gov (United States)

    Delgado-Arredondo, Paulo Antonio; Morinigo-Sotelo, Daniel; Osornio-Rios, Roque Alfredo; Avina-Cervantes, Juan Gabriel; Rostro-Gonzalez, Horacio; Romero-Troncoso, Rene de Jesus

    2017-01-01

    Nowadays, timely maintenance of electric motors is vital to keep up the complex processes of industrial production. There are currently a variety of methodologies for fault diagnosis. Usually, the diagnosis is performed by analyzing current signals at a steady-state motor operation or during a start-up transient. This method is known as motor current signature analysis, which identifies frequencies associated with faults in the frequency domain or by the time-frequency decomposition of the current signals. Fault identification may also be possible by analyzing acoustic sound and vibration signals, which is useful because sometimes this information is the only available. The contribution of this work is a methodology for detecting faults in induction motors in steady-state operation based on the analysis of acoustic sound and vibration signals. This proposed approach uses the Complete Ensemble Empirical Mode Decomposition for decomposing the signal into several intrinsic mode functions. Subsequently, the frequency marginal of the Gabor representation is calculated to obtain the spectral content of the IMF in the frequency domain. This proposal provides good fault detectability results compared to other published works in addition to the identification of more frequencies associated with the faults. The faults diagnosed in this work are two broken rotor bars, mechanical unbalance and bearing defects.

  11. 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

  12. Train axle bearing fault detection using a feature selection scheme based multi-scale morphological filter

    Science.gov (United States)

    Li, Yifan; Liang, Xihui; Lin, Jianhui; Chen, Yuejian; Liu, Jianxin

    2018-02-01

    This paper presents a novel signal processing scheme, feature selection based multi-scale morphological filter (MMF), for train axle bearing fault detection. In this scheme, more than 30 feature indicators of vibration signals are calculated for axle bearings with different conditions and the features which can reflect fault characteristics more effectively and representatively are selected using the max-relevance and min-redundancy principle. Then, a filtering scale selection approach for MMF based on feature selection and grey relational analysis is proposed. The feature selection based MMF method is tested on diagnosis of artificially created damages of rolling bearings of railway trains. Experimental results show that the proposed method has a superior performance in extracting fault features of defective train axle bearings. In addition, comparisons are performed with the kurtosis criterion based MMF and the spectral kurtosis criterion based MMF. The proposed feature selection based MMF method outperforms these two methods in detection of train axle bearing faults.

  13. Bearing Fault Detection Based on Empirical Wavelet Transform and Correlated Kurtosis by Acoustic Emission.

    Science.gov (United States)

    Gao, Zheyu; Lin, Jing; Wang, Xiufeng; Xu, Xiaoqiang

    2017-05-24

    Rolling bearings are widely used in rotating equipment. Detection of bearing faults is of great importance to guarantee safe operation of mechanical systems. Acoustic emission (AE), as one of the bearing monitoring technologies, is sensitive to weak signals and performs well in detecting incipient faults. Therefore, AE is widely used in monitoring the operating status of rolling bearing. This paper utilizes Empirical Wavelet Transform (EWT) to decompose AE signals into mono-components adaptively followed by calculation of the correlated kurtosis (CK) at certain time intervals of these components. By comparing these CK values, the resonant frequency of the rolling bearing can be determined. Then the fault characteristic frequencies are found by spectrum envelope. Both simulation signal and rolling bearing AE signals are used to verify the effectiveness of the proposed method. The results show that the new method performs well in identifying bearing fault frequency under strong background noise.

  14. CRISP. Fault detection, analysis and diagnostics in high-DG distribution systems

    International Nuclear Information System (INIS)

    Fontela, M.; Bacha, S.; Hadsjaid, N.; Andrieu, C.; Raison, B.; Penkov, D.

    2004-04-01

    The fault in the electrotechnical meaning is defined in the document. The main part of faults in overhead lines are non permanent faults, what entails the network operator to maintain the existing techniques to clear as fast as possible these faults. When a permanent fault occurs the operator has to detect and to limit the risks as soon as possible. Different axes are followed: limitation of the fault current, clearing the faulted feeder, locating the fault by test and try under possible fault condition. So the fault detection, fault clearing and fault localization are important functions of an EPS (electric power systems) to allow secure and safe operation of the system. The function may be improved by means of a better use of ICT components in the future sharing conveniently the intelligence needed near the distributed devices and a defined centralized intelligence. This improvement becomes necessary in distribution EPS with a high introduction of DR (distributed resources). The transmission and sub-transmission protection systems are already installed in order to manage power flow in all directions, so the DR issue is less critical for this part of the power system in term of fault clearing and diagnosis. Nevertheless the massive introduction of RES involves another constraints to the transmission system which are the bottlenecks caused by important local and fast installed production as wind power plants. Dealing with the distribution power system, and facing a permanent fault, two main actions must be achieved: identify the faulted elementary EPS area quickly and allow the field crew to locate and to repair the fault as soon as possible. The introduction of DR in distribution EPS involves some changes in fault location methods or equipment. The different existing neutral grounding systems make it difficult the achievement of a general method relevant for any distribution EPS in Europe. Some solutions are studied in the CRISP project in order to improve the

  15. Method of detecting construction faults in concrete pressure vessels

    International Nuclear Information System (INIS)

    Robertson, S.A.; Duhoux, M.; Dawance, G.; Carrie, C.; Morel, D.

    1976-01-01

    A major problem in the design and construction of concrete pressure vessels for nuclear power stations is the risk of excessive air leaks through the concrete itself, due to faulty construction. The 'sonic coring' method of non-destructive concrete testing has been used successfully in pile and diaphragm wall construction control for several years, and the potential use of this method to control the presence of faults in concrete pressure vessels is here described. (author)

  16. Advanced power system protection and incipient fault detection and protection of spaceborne power systems

    Science.gov (United States)

    Russell, B. Don

    1989-01-01

    This research concentrated on the application of advanced signal processing, expert system, and digital technologies for the detection and control of low grade, incipient faults on spaceborne power systems. The researchers have considerable experience in the application of advanced digital technologies and the protection of terrestrial power systems. This experience was used in the current contracts to develop new approaches for protecting the electrical distribution system in spaceborne applications. The project was divided into three distinct areas: (1) investigate the applicability of fault detection algorithms developed for terrestrial power systems to the detection of faults in spaceborne systems; (2) investigate the digital hardware and architectures required to monitor and control spaceborne power systems with full capability to implement new detection and diagnostic algorithms; and (3) develop a real-time expert operating system for implementing diagnostic and protection algorithms. Significant progress has been made in each of the above areas. Several terrestrial fault detection algorithms were modified to better adapt to spaceborne power system environments. Several digital architectures were developed and evaluated in light of the fault detection algorithms.

  17. 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.

  18. 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

  19. Open-circuit fault detection and tolerant operation for a parallel-connected SAB DC-DC converter

    DEFF Research Database (Denmark)

    Park, Kiwoo; Chen, Zhe

    2014-01-01

    This paper presents an open-circuit fault detection method and its tolerant control strategy for a Parallel-Connected Single Active Bridge (PCSAB) dc-dc converter. The structural and operational characteristics of the PCSAB converter lead to several advantages especially for high power applications...... also possesses better reliability under a certain open-circuit fault condition. The proposed fault diagnosis method identifies both location and type of a fault using one current sensor in the output. Depending on the type of the fault, the proposed fault-tolerant strategy tries to keep the capability...

  20. Application of PEANO and ALADDIN in fault detection and diagnosis of concurrent real-time software

    International Nuclear Information System (INIS)

    Thunem, Atoosa P-J.; Thunem, Harald P-J.; Gran, Frauke S.

    2004-04-01

    Concurrent Real-Time (henceforth also addressed in its abbreviated form 'CRT') software systems are multiprocessing environments with many concurrent processes. Faults and their frequencies and consequences in such systems have very complex profiles, as each state of the system is not only a result of an independent behaviour of a single process, but also a result of inter-process communications. Without tailor-made use of tools to detect faults and their patterns in CRT systems, the application of probability-based techniques to assess their risks is therefore without effect. The above indicates the importance of two complementary activities in order to perform successful fault detection and diagnosis of CRT software systems. On the one hand, there is a need for identifying, classifying and modelling the properties of software components such as their functional characteristics and their potentials for demonstrating various patterns of internal behaviour and inter-communication, calling for the development of more realistic, adequate and organised system models as knowledge bases for customised fault detection and diagnosis tools. On the other hand, there is an equal need for the development of such tools that are equipped with proper reasoning and training mechanisms enabling them to predict and detect system faults, guided by received knowledge from the system models. The tools PEANO and ALADDIN, both developed at the Halden Project, were originally designed for monitoring physical processes. Based on the correlations that exist between various sensors, the tools perform signal validation and fault detection with a high degree of accuracy. Due to the analogy between physical processes and software processes in complex real-time systems, this report describes the first steps in exploiting the potentials of the two tools for use in fault detection and diagnosis of concurrent real-time software systems. (Author)

  1. Recycle Requirements for NASA's 30 cm Xenon Ion Thruster

    Science.gov (United States)

    Pinero, Luis R.; Rawlin, Vincent K.

    1994-01-01

    Electrical breakdowns have been observed during ion thruster operation. These breakdowns, or arcs, can be caused by several conditions. In flight systems, the power processing unit must be designed to handle these faults autonomously. This has a strong impact on power processor requirements and must be understood fully for the power processing unit being designed for the NASA Solar Electric Propulsion Technology Application Readiness program. In this study, fault conditions were investigated using a NASA 30 cm ion thruster and a power console. Power processing unit output specifications were defined based on the breakdown phenomena identified and characterized.

  2. Induction machine bearing faults detection based on a multi-dimensional MUSIC algorithm and maximum likelihood estimation.

    Science.gov (United States)

    Elbouchikhi, Elhoussin; Choqueuse, Vincent; Benbouzid, Mohamed

    2016-07-01

    Condition monitoring of electric drives is of paramount importance since it contributes to enhance the system reliability and availability. Moreover, the knowledge about the fault mode behavior is extremely important in order to improve system protection and fault-tolerant control. Fault detection and diagnosis in squirrel cage induction machines based on motor current signature analysis (MCSA) has been widely investigated. Several high resolution spectral estimation techniques have been developed and used to detect induction machine abnormal operating conditions. This paper focuses on the application of MCSA for the detection of abnormal mechanical conditions that may lead to induction machines failure. In fact, this paper is devoted to the detection of single-point defects in bearings based on parametric spectral estimation. A multi-dimensional MUSIC (MD MUSIC) algorithm has been developed for bearing faults detection based on bearing faults characteristic frequencies. This method has been used to estimate the fundamental frequency and the fault related frequency. Then, an amplitude estimator of the fault characteristic frequencies has been proposed and fault indicator has been derived for fault severity measurement. The proposed bearing faults detection approach is assessed using simulated stator currents data, issued from a coupled electromagnetic circuits approach for air-gap eccentricity emulating bearing faults. Then, experimental data are used for validation purposes. Copyright © 2016 ISA. Published by Elsevier Ltd. All rights reserved.

  3. An adaptive demodulation approach for bearing fault detection based on adaptive wavelet filtering and spectral subtraction

    Science.gov (United States)

    Zhang, Yan; Tang, Baoping; Liu, Ziran; Chen, Rengxiang

    2016-02-01

    Fault diagnosis of rolling element bearings is important for improving mechanical system reliability and performance. Vibration signals contain a wealth of complex information useful for state monitoring and fault diagnosis. However, any fault-related impulses in the original signal are often severely tainted by various noises and the interfering vibrations caused by other machine elements. Narrow-band amplitude demodulation has been an effective technique to detect bearing faults by identifying bearing fault characteristic frequencies. To achieve this, the key step is to remove the corrupting noise and interference, and to enhance the weak signatures of the bearing fault. In this paper, a new method based on adaptive wavelet filtering and spectral subtraction is proposed for fault diagnosis in bearings. First, to eliminate the frequency associated with interfering vibrations, the vibration signal is bandpass filtered with a Morlet wavelet filter whose parameters (i.e. center frequency and bandwidth) are selected in separate steps. An alternative and efficient method of determining the center frequency is proposed that utilizes the statistical information contained in the production functions (PFs). The bandwidth parameter is optimized using a local ‘greedy’ scheme along with Shannon wavelet entropy criterion. Then, to further reduce the residual in-band noise in the filtered signal, a spectral subtraction procedure is elaborated after wavelet filtering. Instead of resorting to a reference signal as in the majority of papers in the literature, the new method estimates the power spectral density of the in-band noise from the associated PF. The effectiveness of the proposed method is validated using simulated data, test rig data, and vibration data recorded from the transmission system of a helicopter. The experimental results and comparisons with other methods indicate that the proposed method is an effective approach to detecting the fault-related impulses

  4. An adaptive demodulation approach for bearing fault detection based on adaptive wavelet filtering and spectral subtraction

    International Nuclear Information System (INIS)

    Zhang, Yan; Tang, Baoping; Chen, Rengxiang; Liu, Ziran

    2016-01-01

    Fault diagnosis of rolling element bearings is important for improving mechanical system reliability and performance. Vibration signals contain a wealth of complex information useful for state monitoring and fault diagnosis. However, any fault-related impulses in the original signal are often severely tainted by various noises and the interfering vibrations caused by other machine elements. Narrow-band amplitude demodulation has been an effective technique to detect bearing faults by identifying bearing fault characteristic frequencies. To achieve this, the key step is to remove the corrupting noise and interference, and to enhance the weak signatures of the bearing fault. In this paper, a new method based on adaptive wavelet filtering and spectral subtraction is proposed for fault diagnosis in bearings. First, to eliminate the frequency associated with interfering vibrations, the vibration signal is bandpass filtered with a Morlet wavelet filter whose parameters (i.e. center frequency and bandwidth) are selected in separate steps. An alternative and efficient method of determining the center frequency is proposed that utilizes the statistical information contained in the production functions (PFs). The bandwidth parameter is optimized using a local ‘greedy’ scheme along with Shannon wavelet entropy criterion. Then, to further reduce the residual in-band noise in the filtered signal, a spectral subtraction procedure is elaborated after wavelet filtering. Instead of resorting to a reference signal as in the majority of papers in the literature, the new method estimates the power spectral density of the in-band noise from the associated PF. The effectiveness of the proposed method is validated using simulated data, test rig data, and vibration data recorded from the transmission system of a helicopter. The experimental results and comparisons with other methods indicate that the proposed method is an effective approach to detecting the fault-related impulses

  5. Fault Detection Enhancement in Rolling Element Bearings via Peak-Based Multiscale Decomposition and Envelope Demodulation

    Directory of Open Access Journals (Sweden)

    Hua-Qing Wang

    2014-01-01

    Full Text Available Vibration signals of rolling element bearings faults are usually immersed in background noise, which makes it difficult to detect the faults. Wavelet-based methods being used commonly can reduce some types of noise, but there is still plenty of room for improvement due to the insufficient sparseness of vibration signals in wavelet domain. In this work, in order to eliminate noise and enhance the weak fault detection, a new kind of peak-based approach combined with multiscale decomposition and envelope demodulation is developed. First, to preserve effective middle-low frequency signals while making high frequency noise more significant, a peak-based piecewise recombination is utilized to convert middle frequency components into low frequency ones. The newly generated signal becomes so smoother that it will have a sparser representation in wavelet domain. Then a noise threshold is applied after wavelet multiscale decomposition, followed by inverse wavelet transform and backward peak-based piecewise transform. Finally, the amplitude of fault characteristic frequency is enhanced by means of envelope demodulation. The effectiveness of the proposed method is validated by rolling bearings faults experiments. Compared with traditional wavelet-based analysis, experimental results show that fault features can be enhanced significantly and detected easily by the proposed method.

  6. Hybrid Model-Based and Data-Driven Fault Detection and Diagnostics for Commercial Buildings

    Energy Technology Data Exchange (ETDEWEB)

    Frank, Stephen; Heaney, Michael; Jin, Xin; Robertson, Joseph; Cheung, Howard; Elmore, Ryan; Henze, Gregor

    2016-08-26

    Commercial buildings often experience faults that produce undesirable behavior in building systems. Building faults waste energy, decrease occupants' comfort, and increase operating costs. Automated fault detection and diagnosis (FDD) tools for buildings help building owners discover and identify the root causes of faults in building systems, equipment, and controls. Proper implementation of FDD has the potential to simultaneously improve comfort, reduce energy use, and narrow the gap between actual and optimal building performance. However, conventional rule-based FDD requires expensive instrumentation and valuable engineering labor, which limit deployment opportunities. This paper presents a hybrid, automated FDD approach that combines building energy models and statistical learning tools to detect and diagnose faults noninvasively, using minimal sensors, with little customization. We compare and contrast the performance of several hybrid FDD algorithms for a small security building. Our results indicate that the algorithms can detect and diagnose several common faults, but more work is required to reduce false positive rates and improve diagnosis accuracy.

  7. Fault Detection and Localization in Transmission Lines with a Static Synchronous Series Compensator

    Directory of Open Access Journals (Sweden)

    REYES-ARCHUNDIA, E.

    2015-08-01

    Full Text Available This paper proposes a fault detection and localization method for power transmission lines with a Static Synchronous Series Compensator (SSSC. The algorithm is based on applying a modal transformation to the current and voltage signals sampled at high frequencies. Then, the wavelet transform is used for calculating the current and voltage traveling waves, avoiding low frequency interference generated by the system and the SSSC. Finally, by using reflectometry principles, straightforward expressions for fault detection and localization in the transmission line are derived. The algorithm performance was tested considering several study cases, where some relevant parameters such as voltage compensation level, fault resistance and fault inception angle are varied. The results indicate that the algorithm can be successfully be used for fault detection and localization in transmission lines compensated with a SSSC. The estimated error in calculating the distance to the fault is smaller than 1% of the transmission line length. The test system is simulated in PSCAD platform and the algorithm is implemented in MATLAB software.

  8. A Novel Method for Detection and Classification of Covered Conductor Faults

    Directory of Open Access Journals (Sweden)

    Stanislav Misak

    2016-01-01

    Full Text Available Medium-Voltage (MV overhead lines with Covered Conductors (CCs are increasingly being used around the world primarily in forested or dissected terrain areas or in urban areas where it is not possible to utilize MV cable lines. The CC is specific in high operational reliability provided by the conductor core insulation compared to Aluminium-Conductor Steel-Reinforced (ACSR overhead lines. The only disadvantage of the CC is rather the problematic detection of faults compared to the ACSR. In this work, we consider the following faults: the contact of a tree branch with a CC and the fall of a conductor on the ground. The standard protection relays are unable to detect the faults and so the faults pose a risk for individuals in the vicinity of the conductor as well as it compromises the overall safety and reliability of the MV distribution system. In this article, we continue with our previous work aimed at the method enabling detection of the faults and we introduce a method enabling a classification of the fault type. Such a classification is especially important for an operator of an MV distribution system to plan the optimal maintenance or repair the faulty conductors since the fall of a tree branch can be solved later whereas the breakdown of a conductor means an immediate action of the operator.

  9. Hybrid Model-Based and Data-Driven Fault Detection and Diagnostics for Commercial Buildings: Preprint

    Energy Technology Data Exchange (ETDEWEB)

    Frank, Stephen; Heaney, Michael; Jin, Xin; Robertson, Joseph; Cheung, Howard; Elmore, Ryan; Henze, Gregor

    2016-08-01

    Commercial buildings often experience faults that produce undesirable behavior in building systems. Building faults waste energy, decrease occupants' comfort, and increase operating costs. Automated fault detection and diagnosis (FDD) tools for buildings help building owners discover and identify the root causes of faults in building systems, equipment, and controls. Proper implementation of FDD has the potential to simultaneously improve comfort, reduce energy use, and narrow the gap between actual and optimal building performance. However, conventional rule-based FDD requires expensive instrumentation and valuable engineering labor, which limit deployment opportunities. This paper presents a hybrid, automated FDD approach that combines building energy models and statistical learning tools to detect and diagnose faults noninvasively, using minimal sensors, with little customization. We compare and contrast the performance of several hybrid FDD algorithms for a small security building. Our results indicate that the algorithms can detect and diagnose several common faults, but more work is required to reduce false positive rates and improve diagnosis accuracy.

  10. Fault detection of a spur gear using vibration signal with multivariable statistical parameters

    Directory of Open Access Journals (Sweden)

    Songpon Klinchaeam

    2014-10-01

    Full Text Available This paper presents a condition monitoring technique of a spur gear fault detection using vibration signal analysis based on time domain. Vibration signals were acquired from gearboxes and used to simulate various faults on spur gear tooth. In this study, vibration signals were applied to monitor a normal and various fault conditions of a spur gear such as normal, scuffing defect, crack defect and broken tooth. The statistical parameters of vibration signal were used to compare and evaluate the value of fault condition. This technique can be applied to set alarm limit of the signal condition based on statistical parameter such as variance, kurtosis, rms and crest factor. These parameters can be used to set as a boundary decision of signal condition. From the results, the vibration signal analysis with single statistical parameter is unclear to predict fault of the spur gears. The using at least two statistical parameters can be clearly used to separate in every case of fault detection. The boundary decision of statistical parameter with the 99.7% certainty ( 3   from 300 referenced dataset and detected the testing condition with 99.7% ( 3   accuracy and had an error of less than 0.3 % using 50 testing dataset.

  11. Incipient Stator Insulation Fault Detection of Permanent Magnet Synchronous Wind Generators Based on Hilbert–Huang Transformation

    DEFF Research Database (Denmark)

    Wang, Chao; Liu, Xiao; Chen, Zhe

    2014-01-01

    Incipient stator winding fault in permanent magnet synchronous wind generators (PMSWGs) is very difficult to be detected as the fault generated variations in terminal electrical parameters are very weak and chaotic. This paper simulates the incipient stator winding faults at different degree...

  12. Improved Data-based Fault Detection Strategy and Application to Distillation Columns

    KAUST Repository

    Madakyaru, Muddu

    2017-01-31

    Chemical and petrochemical processes require continuous monitoring to detect abnormal events and to sustain normal operations. Furthermore, process monitoring enhances productivity, efficiency, and safety in process industries. Here, we propose an innovative statistical approach that exploits the advantages of multiscale partial least squares (MSPLS) models and generalized likelihood ratio (GLR) tests for fault detection in processes. Specifically, we combine an MSPLS algorithm with wavelet analysis to create our modeling framework. Then, we use GLR hypothesis testing based on the uncorrelated residuals obtained from the MSPLS model to improve fault detection. We use simulated distillation column data to evaluate the MSPLS-based GLR chart. Results show that our MSPLS-based GLR method is more powerful than the PLS-based Q and GLR method and MSPLS-based Q method, especially in early detection of small faults with abrupt or incipient behavior.

  13. Distributed Fault Detection and Isolation for Flocking in a Multi-robot System with Imperfect Communication

    Directory of Open Access Journals (Sweden)

    Shao Shiliang

    2014-06-01

    Full Text Available In this paper, we focus on distributed fault detection and isolation (FDI for a multi-robot system where multiple robots execute a flocking task. Firstly, we propose a fault detection method based on the local-information-exchange and sensor-measurement technologies to cover cases of both perfect communication and imperfect communication. The two detection technologies can be adaptively selected according to the packet loss rate (PLR. Secondly, we design a fault isolation method, considering a situation in which faulty robots still influence the behaviours of other robots. Finally, a complete FDI scheme, based on the proposed detection and isolation methods, is simulated in various scenarios. The results demonstrate that our FDI scheme is effective.

  14. Early fault detection and on-line diagnosis in real-time environments

    Directory of Open Access Journals (Sweden)

    Andreas Bye

    1993-01-01

    Full Text Available This paper describes an approach to fault detection and diagnosis involving the simultaneous employment of quantitative and qualitative reasoning techniques. We show that early identification of process anomalies by means of a separate fault detection module paves the way for a fast and accuratc follow-up diagnosis. The diagnosis task is dramatically simplified because the diagnostic inferences can be performed at the soonest possible time: when the detection module first spots deviations between its calculated reference points and the corresponding measurements from the process.

  15. Fault Detection for Nonlinear Process With Deterministic Disturbances: A Just-In-Time Learning Based Data Driven Method.

    Science.gov (United States)

    Yin, Shen; Gao, Huijun; Qiu, Jianbin; Kaynak, Okyay

    2017-11-01

    Data-driven fault detection plays an important role in industrial systems due to its applicability in case of unknown physical models. In fault detection, disturbances must be taken into account as an inherent characteristic of processes. Nevertheless, fault detection for nonlinear processes with deterministic disturbances still receive little attention, especially in data-driven field. To solve this problem, a just-in-time learning-based data-driven (JITL-DD) fault detection method for nonlinear processes with deterministic disturbances is proposed in this paper. JITL-DD employs JITL scheme for process description with local model structures to cope with processes dynamics and nonlinearity. The proposed method provides a data-driven fault detection solution for nonlinear processes with deterministic disturbances, and owns inherent online adaptation and high accuracy of fault detection. Two nonlinear systems, i.e., a numerical example and a sewage treatment process benchmark, are employed to show the effectiveness of the proposed method.

  16. Induced Voltages Ratio-Based Algorithm for Fault Detection, and Faulted Phase and Winding Identification of a Three-Winding Power Transformer

    Directory of Open Access Journals (Sweden)

    Byung Eun Lee

    2014-09-01

    Full Text Available This paper proposes an algorithm for fault detection, faulted phase and winding identification of a three-winding power transformer based on the induced voltages in the electrical power system. The ratio of the induced voltages of the primary-secondary, primary-tertiary and secondary-tertiary windings is the same as the corresponding turns ratio during normal operating conditions, magnetic inrush, and over-excitation. It differs from the turns ratio during an internal fault. For a single phase and a three-phase power transformer with wye-connected windings, the induced voltages of each pair of windings are estimated. For a three-phase power transformer with delta-connected windings, the induced voltage differences are estimated to use the line currents, because the delta winding currents are practically unavailable. Six detectors are suggested for fault detection. An additional three detectors and a rule for faulted phase and winding identification are presented as well. The proposed algorithm can not only detect an internal fault, but also identify the faulted phase and winding of a three-winding power transformer. The various test results with Electromagnetic Transients Program (EMTP-generated data show that the proposed algorithm successfully discriminates internal faults from normal operating conditions including magnetic inrush and over-excitation. This paper concludes by implementing the algorithm into a prototype relay based on a digital signal processor.

  17. Fault Detection of Inline Reciprocating Diesel Engine: A Mass and Gas-Torque Approach

    Directory of Open Access Journals (Sweden)

    S. H. Gawande

    2012-01-01

    Full Text Available Early fault detection and diagnosis for medium-speed diesel engines are important to ensure reliable operation throughout the course of their service. This work presents an investigation of the diesel engine combustion-related fault detection capability of crankshaft torsional vibrations. Proposed methodology state the way of early fault detection in the operating six-cylinder diesel engine. The model of six cylinders DI Diesel engine is developed appropriately. As per the earlier work by the same author the torsional vibration amplitudes are used to superimpose the mass and gas torque. Further mass and gas torque analysis is used to detect fault in the operating engine. The DFT of the measured crankshaft’s speed, under steady-state operating conditions at constant load shows significant variation of the amplitude of the lowest major harmonic order. This is valid both for uniform operating and faulty conditions and the lowest harmonic orders may be used to correlate its amplitude to the gas pressure torque and mass torque for a given engine. The amplitudes of the lowest harmonic orders (0.5, 1, and 1.5 of the gas pressure torque and mass torque are used to map the fault. A method capable to detect faulty cylinder of operating Kirloskar diesel engine of SL90 Engine-SL8800TA type is developed, based on the phases of the lowest three harmonic orders.

  18. Automated valve fault detection based on acoustic emission parameters and support vector machine

    Directory of Open Access Journals (Sweden)

    Salah M. Ali

    2018-03-01

    Full Text Available Reciprocating compressors are one of the most used types of compressors with wide applications in industry. The most common failure in reciprocating compressors is always related to the valves. Therefore, a reliable condition monitoring method is required to avoid the unplanned shutdown in this category of machines. Acoustic emission (AE technique is one of the effective recent methods in the field of valve condition monitoring. However, a major challenge is related to the analysis of AE signal which perhaps only depends on the experience and knowledge of technicians. This paper proposes automated fault detection method using support vector machine (SVM and AE parameters in an attempt to reduce human intervention in the process. Experiments were conducted on a single stage reciprocating air compressor by combining healthy and faulty valve conditions to acquire the AE signals. Valve functioning was identified through AE waveform analysis. SVM faults detection model was subsequently devised and validated based on training and testing samples respectively. The results demonstrated automatic valve fault detection model with accuracy exceeding 98%. It is believed that valve faults can be detected efficiently without human intervention by employing the proposed model for a single stage reciprocating compressor. Keywords: Condition monitoring, Faults detection, Signal analysis, Acoustic emission, Support vector machine

  19. Compressive Sensing of Roller Bearing Faults via Harmonic Detection from Under-Sampled Vibration Signals.

    Science.gov (United States)

    Tang, Gang; Hou, Wei; Wang, Huaqing; Luo, Ganggang; Ma, Jianwei

    2015-10-09

    The Shannon sampling principle requires substantial amounts of data to ensure the accuracy of on-line monitoring of roller bearing fault signals. Challenges are often encountered as a result of the cumbersome data monitoring, thus a novel method focused on compressed vibration signals for detecting roller bearing faults is developed in this study. Considering that harmonics often represent the fault characteristic frequencies in vibration signals, a compressive sensing frame of characteristic harmonics is proposed to detect bearing faults. A compressed vibration signal is first acquired from a sensing matrix with information preserved through a well-designed sampling strategy. A reconstruction process of the under-sampled vibration signal is then pursued as attempts are conducted to detect the characteristic harmonics from sparse measurements through a compressive matching pursuit strategy. In the proposed method bearing fault features depend on the existence of characteristic harmonics, as typically detected directly from compressed data far before reconstruction completion. The process of sampling and detection may then be performed simultaneously without complete recovery of the under-sampled signals. The effectiveness of the proposed method is validated by simulations and experiments.

  20. Bearing fault detection utilizing group delay and the Hilbert-Huang transform

    Energy Technology Data Exchange (ETDEWEB)

    Jin, Shuai; Lee, Sang-Kwon [Inha University, Incheon (Korea, Republic of)

    2017-03-15

    Vibration signals measured from a mechanical system are useful to detect system faults. Signal processing has been used to extract fault information in bearing systems. However, a wide vibration signal frequency band often affects the ability to obtain the effective fault features. In addition, a few oscillation components are not useful at the entire frequency band in a vibration signal. By contrast, useful fatigue information can be embedded in the noise oscillation components. Thus, a method to estimate which frequency band contains fault information utilizing group delay was proposed in this paper. Group delay as a measure of phase distortion can indicate the phase structure relationship in the frequency domain between original (with noise) and denoising signals. We used the empirical mode decomposition of a Hilbert-Huang transform to sift the useful intrinsic mode functions based on the results of group delay after determining the valuable frequency band. Finally, envelope analysis and the energy distribution after the Hilbert transform were used to complete the fault diagnosis. The practical bearing fault data, which were divided into inner and outer race faults, were used to verify the efficiency and quality of the proposed method.

  1. Nonlinear System Identification and Its Applications in Fault Detection and Diagnosis

    DEFF Research Database (Denmark)

    Sun, Zhen

    Interest in nonlinear system identification has grown significantly in recent years. It is much more difficult to develop general results than the concern for linear models since the nonlinear model structures are often much more complicated. As a consequence, the thesis only considers two differ...... be performed by identifying these fault related parameters. Afterwards, the decision whether the fault happened or how large the fault is can be made by comparison and analysis based on the estimated values....... and then for a space robot system. Secondly, the system considered is described by a nonlinear FOPDT model. This type of FOPDT model is an extension of the traditional FOPDT model which pre-assumes all the model parameters are constants. The nonlinearity that is defined in the model is reflected in its two categories...... refrigeration system. The proposed models and methods are further extended for the purpose of Fault Detection and Diagnosis (FDD). In a system where it exists possible parametric fault, if some fault happens, one or several parameters related to fault may change their values. Then the FDD procedure can...

  2. 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.

  3. Improved nonlinear fault detection strategy based on the Hellinger distance metric: Plug flow reactor monitoring

    KAUST Repository

    Harrou, Fouzi

    2017-03-18

    Fault detection has a vital role in the process industry to enhance productivity, efficiency, and safety, and to avoid expensive maintenance. This paper proposes an innovative multivariate fault detection method that can be used for monitoring nonlinear processes. The proposed method merges advantages of nonlinear projection to latent structures (NLPLS) modeling and those of Hellinger distance (HD) metric to identify abnormal changes in highly correlated multivariate data. Specifically, the HD is used to quantify the dissimilarity between current NLPLS-based residual and reference probability distributions obtained using fault-free data. Furthermore, to enhance further the robustness of these methods to measurement noise, and reduce the false alarms due to modeling errors, wavelet-based multiscale filtering of residuals is used before the application of the HD-based monitoring scheme. The performances of the developed NLPLS-HD fault detection technique is illustrated using simulated plug flow reactor data. The results show that the proposed method provides favorable performance for detection of faults compared to the conventional NLPLS method.

  4. 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)

  5. 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)

  6. PLAT: An Automated Fault and Behavioural Anomaly Detection Tool for PLC Controlled Manufacturing Systems

    Directory of Open Access Journals (Sweden)

    Arup Ghosh

    2016-01-01

    Full Text Available Operational faults and behavioural anomalies associated with PLC control processes take place often in a manufacturing system. Real time identification of these operational faults and behavioural anomalies is necessary in the manufacturing industry. In this paper, we present an automated tool, called PLC Log-Data Analysis Tool (PLAT that can detect them by using log-data records of the PLC signals. PLAT automatically creates a nominal model of the PLC control process and employs a novel hash table based indexing and searching scheme to satisfy those purposes. Our experiments show that PLAT is significantly fast, provides real time identification of operational faults and behavioural anomalies, and can execute within a small memory footprint. In addition, PLAT can easily handle a large manufacturing system with a reasonable computing configuration and can be installed in parallel to the data logging system to identify operational faults and behavioural anomalies effectively.

  7. Data-driven fault detection for industrial processes canonical correlation analysis and projection based methods

    CERN Document Server

    Chen, Zhiwen

    2017-01-01

    Zhiwen Chen aims to develop advanced fault detection (FD) methods for the monitoring of industrial processes. With the ever increasing demands on reliability and safety in industrial processes, fault detection has become an important issue. Although the model-based fault detection theory has been well studied in the past decades, its applications are limited to large-scale industrial processes because it is difficult to build accurate models. Furthermore, motivated by the limitations of existing data-driven FD methods, novel canonical correlation analysis (CCA) and projection-based methods are proposed from the perspectives of process input and output data, less engineering effort and wide application scope. For performance evaluation of FD methods, a new index is also developed. Contents A New Index for Performance Evaluation of FD Methods CCA-based FD Method for the Monitoring of Stationary Processes Projection-based FD Method for the Monitoring of Dynamic Processes Benchmark Study and Real-Time Implementat...

  8. Broken Bar Fault Detection in IM Operating Under No-Load Condition

    Directory of Open Access Journals (Sweden)

    RELJIC, D.

    2016-11-01

    Full Text Available This paper presents a novel method for broken rotor bar detection in a squirrel-cage induction motor (IM. The proposed method applies a single-phase AC voltage as a test signal on motor terminals, resulting in a stator backward-rotating magnetic field. The field ultimately causes additional current components in the stator windings whose magnitudes depend on the broken bar fault severity, even if the motor is unloaded. This allows robust broken bar fault detection based only on standard motor current signature analysis (MCSA technique. The proposed fault detection method is at first verified via simulations, using an IM model based on finite element analysis (FEA and multiple coupled circuit approach (MCCA. The subsequent experimental investigations have shown good agreement with both theoretical predictions and simulation results.

  9. Open-switch fault detection method of an NPC converter for wind turbine systems

    DEFF Research Database (Denmark)

    Lee, June-Seok; Lee, Kyo-Beum; Blaabjerg, Frede

    2013-01-01

    In wind turbine generation (WTG) systems, the neutral-point-clamped (NPC) topology is widely used as the part of a back-to-back converter since the three-level NPC topology has more advantages than the conventional two-level inverter especially for high power. There are twelve switches in the NPC...... topology and an open-switch fault of the NPC converter leads to current distortion and the torque ripple in the system. Furthermore, WTG systems can breakdown in the worst case by this ripple. To improve the reliability of WTG systems, an open-switch fault detection method is required. The open......-switch detection method of the NPC converter is different from that of the NPC inverter due to the different current paths of the NPC converter. This paper proposes the open-switch fault detection method of the NPC converter connected the permanent-magnet synchronous generator (PMSG). Moreover, the open...

  10. A Novel Approach to Fault Detection in Complex Electric Power Systems

    Directory of Open Access Journals (Sweden)

    ZHANG, Y.

    2014-08-01

    Full Text Available The new type of backup protection can utilize different kinds of information in a larger scale. The research of this paper is focused on the centralized decision and distributed implementation of wide area backup protection system in large-scale power grid. Topology analysis of power network is substantially network connectivity judgment. The operation conditions in case of a failure should be truthfully reflected in the actual structure of network topology, which requires the system failure must be detected promptly and accurately, and prepare for the subsequent adjustment of operation scheme. In the research of this paper, for different kinds of complex system failures, we have put forward a novel fault factor analysis scheme which can realize rapid, accurate and effective fault detection. Many simulations have verified that the fault factor analysis can successfully detect the failures in complex electric power system.

  11. Online model-based fault detection for grid connected PV systems monitoring

    KAUST Repository

    Harrou, Fouzi

    2017-12-14

    This paper presents an efficient fault detection approach to monitor the direct current (DC) side of photovoltaic (PV) systems. The key contribution of this work is combining both single diode model (SDM) flexibility and the cumulative sum (CUSUM) chart efficiency to detect incipient faults. In fact, unknown electrical parameters of SDM are firstly identified using an efficient heuristic algorithm, named Artificial Bee Colony algorithm. Then, based on the identified parameters, a simulation model is built and validated using a co-simulation between Matlab/Simulink and PSIM. Next, the peak power (Pmpp) residuals of the entire PV array are generated based on both real measured and simulated Pmpp values. Residuals are used as the input for the CUSUM scheme to detect potential faults. We validate the effectiveness of this approach using practical data from an actual 20 MWp grid-connected PV system located in the province of Adrar, Algeria.

  12. Torsional vibration signal analysis as a diagnostic tool for planetary gear fault detection

    Science.gov (United States)

    Xue, Song; Howard, Ian

    2018-02-01

    This paper aims to investigate the effectiveness of using the torsional vibration signal as a diagnostic tool for planetary gearbox faults detection. The traditional approach for condition monitoring of the planetary gear uses a stationary transducer mounted on the ring gear casing to measure all the vibration data when the planet gears pass by with the rotation of the carrier arm. However, the time variant vibration transfer paths between the stationary transducer and the rotating planet gear modulate the resultant vibration spectra and make it complex. Torsional vibration signals are theoretically free from this modulation effect and therefore, it is expected to be much easier and more effective to diagnose planetary gear faults using the fault diagnostic information extracted from the torsional vibration. In this paper, a 20 degree of freedom planetary gear lumped-parameter model was developed to obtain the gear dynamic response. In the model, the gear mesh stiffness variations are the main internal vibration generation mechanism and the finite element models were developed for calculation of the sun-planet and ring-planet gear mesh stiffnesses. Gear faults on different components were created in the finite element models to calculate the resultant gear mesh stiffnesses, which were incorporated into the planetary gear model later on to obtain the faulted vibration signal. Some advanced signal processing techniques were utilized to analyses the fault diagnostic results from the torsional vibration. It was found that the planetary gear torsional vibration not only successfully detected the gear fault, but also had the potential to indicate the location of the gear fault. As a result, the planetary gear torsional vibration can be considered an effective alternative approach for planetary gear condition monitoring.

  13. Improved Statistical Fault Detection Technique and Application to Biological Phenomena Modeled by S-Systems.

    Science.gov (United States)

    Mansouri, Majdi; Nounou, Mohamed N; Nounou, Hazem N

    2017-09-01

    In our previous work, we have demonstrated the effectiveness of the linear multiscale principal component analysis (PCA)-based moving window (MW)-generalized likelihood ratio test (GLRT) technique over the classical PCA and multiscale principal component analysis (MSPCA)-based GLRT methods. The developed fault detection algorithm provided optimal properties by maximizing the detection probability for a particular false alarm rate (FAR) with different values of windows, and however, most real systems are nonlinear, which make the linear PCA method not able to tackle the issue of non-linearity to a great extent. Thus, in this paper, first, we apply a nonlinear PCA to obtain an accurate principal component of a set of data and handle a wide range of nonlinearities using the kernel principal component analysis (KPCA) model. The KPCA is among the most popular nonlinear statistical methods. Second, we extend the MW-GLRT technique to one that utilizes exponential weights to residuals in the moving window (instead of equal weightage) as it might be able to further improve fault detection performance by reducing the FAR using exponentially weighed moving average (EWMA). The developed detection method, which is called EWMA-GLRT, provides improved properties, such as smaller missed detection and FARs and smaller average run length. The idea behind the developed EWMA-GLRT is to compute a new GLRT statistic that integrates current and previous data information in a decreasing exponential fashion giving more weight to the more recent data. This provides a more accurate estimation of the GLRT statistic and provides a stronger memory that will enable better decision making with respect to fault detection. Therefore, in this paper, a KPCA-based EWMA-GLRT method is developed and utilized in practice to improve fault detection in biological phenomena modeled by S-systems and to enhance monitoring process mean. The idea behind a KPCA-based EWMA-GLRT fault detection algorithm is to

  14. Shallow Faulting in Morelia, Mexico, Based on Seismic Tomography and Geodetically Detected Land Subsidence

    Science.gov (United States)

    Cabral-Cano, E.; Arciniega-Ceballos, A.; Vergara-Huerta, F.; Chaussard, E.; Wdowinski, S.; DeMets, C.; Salazar-Tlaczani, L.

    2013-12-01

    Subsidence has been a common occurrence in several cities in central Mexico for the past three decades. This process causes substantial damage to the urban infrastructure and housing in several cities and it is a major factor to be considered when planning urban development, land-use zoning and hazard mitigation strategies. Since the early 1980's the city of Morelia in Central Mexico has experienced subsidence associated with groundwater extraction in excess of natural recharge from rainfall. Previous works have focused on the detection and temporal evolution of the subsidence spatial distribution. The most recent InSAR analysis confirms the permanence of previously detected rapidly subsiding areas such as the Rio Grande Meander area and also defines 2 subsidence patches previously undetected in the newly developed suburban sectors west of Morelia at the Fraccionamiento Del Bosque along, south of Hwy. 15 and another patch located north of Morelia along Gabino Castañeda del Rio Ave. Because subsidence-induced, shallow faulting develops at high horizontal strain localization, newly developed a subsidence areas are particularly prone to faulting and fissuring. Shallow faulting increases groundwater vulnerability because it disrupts discharge hydraulic infrastructure and creates a direct path for transport of surface pollutants into the underlying aquifer. Other sectors in Morelia that have been experiencing subsidence for longer time have already developed well defined faults such as La Colina, Central Camionera, Torremolinos and La Paloma faults. Local construction codes in the vicinity of these faults define a very narrow swath along which housing construction is not allowed. In order to better characterize these fault systems and provide better criteria for future municipal construction codes we have surveyed the La Colina and Torremolinos fault systems in the western sector of Morelia using seismic tomographic techniques. Our results indicate that La Colina Fault

  15. Observer and data-driven model based fault detection in Power Plant Coal Mills

    DEFF Research Database (Denmark)

    Fogh Odgaard, Peter; Lin, Bao; Jørgensen, Sten Bay

    2008-01-01

    model with motor power as the controlled variable, data-driven methods for fault detection are also investigated. Regression models that represent normal operating conditions (NOCs) are developed with both static and dynamic principal component analysis and partial least squares methods. The residual...... caused by a blocked inlet pipe. All three approaches detect the fault as it emerges. The optimal unknown input observer approach is most robust, in that, it has no false positives. On the other hand, the data-driven approaches are more straightforward to implement, since they just require the selection...

  16. Envelope analysis with a genetic algorithm-based adaptive filter bank for bearing fault detection.

    Science.gov (United States)

    Kang, Myeongsu; Kim, Jaeyoung; Choi, Byeong-Keun; Kim, Jong-Myon

    2015-07-01

    This paper proposes a fault detection methodology for bearings using envelope analysis with a genetic algorithm (GA)-based adaptive filter bank. Although a bandpass filter cooperates with envelope analysis for early identification of bearing defects, no general consensus has been reached as to which passband is optimal. This study explores the impact of various passbands specified by the GA in terms of a residual frequency components-to-defect frequency components ratio, which evaluates the degree of defectiveness in bearings and finally outputs an optimal passband for reliable bearing fault detection.

  17. Karhunen Loeve Basis Used for Detection of Gearbox Faults in a Wind Turbine

    DEFF Research Database (Denmark)

    Odgaard, Peter Fogh; Stoustrup, Jakob

    2014-01-01

    Reliability and sustainability of wind turbines increase in importance as wind turbines contribute with increasing power generation to the world's power grids. One possible way to achieve this is by using advanced fault detection and isolation methods in wind turbines based on the measurements...... provided to the control system. In this paper a Karhunen-Loeve basis approach is designed for detecting changes in frequency response from rotating parts like a gearbox. The potential of this method is shown by applying it to an established Wind Turbine FDI and FTC Benchmark model. These faults...

  18. Observer-based Fault Detection and Isolation for Nonlinear Systems

    DEFF Research Database (Denmark)

    Lootsma, T.F.

    systems. It consists of four different contributions. First, it presents a review of the idea and the theory behind the geometric approach for FDI. Starting from the original solution for linear systems up to the latest results for input-affine systems the theory and solutions are described....... Then the geometric approach is applied to a nonlinear ship propulsion system benchmark. The calculations and application results are presented in detail to give an illustrative example. The obtained subsystems are considered for the design of nonlinear observers in order to obtain FDI. Additionally, an adaptive...... for the observers designed for the ship propulsion system. Furthermore, it stresses the importance of the time-variant character of the linearization along a trajectory. It leads to a different stability analysis than for linearization at one operation point. Finally, the preliminary concept of (actuator) fault...

  19. Detection of Stator Winding Fault in Induction Motor Using Fuzzy Logic with Optimal Rules

    Directory of Open Access Journals (Sweden)

    Hamid Fekri Azgomi

    2013-04-01

    Full Text Available Induction motors are critical components in many industrial processes. Therefore, swift, precise and reliable monitoring and fault detection systems are required to prevent any further damages. The online monitoring of induction motors has been becoming increasingly important. The main difficulty in this task is the lack of an accurate analytical model to describe a faulty motor. A fuzzy logic approach may help to diagnose traction motor faults. This paper presents a simple method for the detection of stator winding faults (which make up 38% of induction motor failures based on monitoring the line/terminal current amplitudes. In this method, fuzzy logic is used to make decisions about the stator motor condition. In fact, fuzzy logic is reminiscent of human thinking processes and natural language enabling decisions to be made based on vague information. The motor condition is described using linguistic variables. Fuzzy subsets and the corresponding membership functions describe stator current amplitudes. A knowledge base, comprising rule and data bases, is built to support the fuzzy inference. Simulation results are presented to verify the accuracy of motor’s fault detection and knowledge extraction feasibility. The preliminary results show that the proposed fuzzy approach can be used for accurate stator fault diagnosis.

  20. Simple Random Sampling-Based Probe Station Selection for Fault Detection in Wireless Sensor Networks

    Science.gov (United States)

    Huang, Rimao; Qiu, Xuesong; Rui, Lanlan

    2011-01-01

    Fault detection for wireless sensor networks (WSNs) has been studied intensively in recent years. Most existing works statically choose the manager nodes as probe stations and probe the network at a fixed frequency. This straightforward solution leads however to several deficiencies. Firstly, by only assigning the fault detection task to the manager node the whole network is out of balance, and this quickly overloads the already heavily burdened manager node, which in turn ultimately shortens the lifetime of the whole network. Secondly, probing with a fixed frequency often generates too much useless network traffic, which results in a waste of the limited network energy. Thirdly, the traditional algorithm for choosing a probing node is too complicated to be used in energy-critical wireless sensor networks. In this paper, we study the distribution characters of the fault nodes in wireless sensor networks, validate the Pareto principle that a small number of clusters contain most of the faults. We then present a Simple Random Sampling-based algorithm to dynamic choose sensor nodes as probe stations. A dynamic adjusting rule for probing frequency is also proposed to reduce the number of useless probing packets. The simulation experiments demonstrate that the algorithm and adjusting rule we present can effectively prolong the lifetime of a wireless sensor network without decreasing the fault detected rate. PMID:22163789

  1. Fault Detection in High Speed Helical Gears Considering Signal Processing Method in Real Simulation

    Directory of Open Access Journals (Sweden)

    Amir Ali Tabatabai Adnani

    Full Text Available Abstract In the present study, in order to detect the fault of the gearmeshs, two engaged gears based on research department of a major automotive company have been modeled. First off, by using the CATIA software the fault was induced to the output gear. Then, the faulty gearmesh and non-faulty gearmesh is modeled to find the fault pattern to predict and estimate the failure of the gearmesh. The induced defect is according to the frequently practical fault that takes place to the teeth of gears. In order to record the acceleration signals to calculate the decomposition algorithm, mount the accelerometer on accessible place of the output shaft to recognize the pattern. Then, for more realistic simulation, noise is added to the output signal. At the first step by means of Butterworth low pass digital, the noise has to be removed from signals after that by using the Empirical Mode Decomposition (EMD, signals have decomposed into the Instinct Mode Function (IMF and every IMF were tested by using the Instantaneous Frequency (IF in way of Hillbert Transform (HT. For this purpose a code was developed in MATLAB software. Then, in order to detect the presence of the fault the frequency spectrum of IMF's are created and defect is detected in gearmesh frequency of the spectrum.

  2. Parameter-free bearing fault detection based on maximum likelihood estimation and differentiation

    International Nuclear Information System (INIS)

    Bozchalooi, I Soltani; Liang, Ming

    2009-01-01

    Bearing faults can lead to malfunction and ultimately complete stall of many machines. The conventional high-frequency resonance (HFR) method has been commonly used for bearing fault detection. However, it is often very difficult to obtain and calibrate bandpass filter parameters, i.e. the center frequency and bandwidth, the key to the success of the HFR method. This inevitably undermines the usefulness of the conventional HFR technique. To avoid such difficulties, we propose parameter-free, versatile yet straightforward techniques to detect bearing faults. We focus on two types of measured signals frequently encountered in practice: (1) a mixture of impulsive faulty bearing vibrations and intrinsic background noise and (2) impulsive faulty bearing vibrations blended with intrinsic background noise and vibration interferences. To design a proper signal processing technique for each case, we analyze the effects of intrinsic background noise and vibration interferences on amplitude demodulation. For the first case, a maximum likelihood-based fault detection method is proposed to accommodate the Rician distribution of the amplitude-demodulated signal mixture. For the second case, we first illustrate that the high-amplitude low-frequency vibration interferences can make the amplitude demodulation ineffective. Then we propose a differentiation method to enhance the fault detectability. It is shown that the iterative application of a differentiation step can boost the relative strength of the impulsive faulty bearing signal component with respect to the vibration interferences. This preserves the effectiveness of amplitude demodulation and hence leads to more accurate fault detection. The proposed approaches are evaluated on simulated signals and experimental data acquired from faulty bearings

  3. Fault Detection of Wind Turbines with Uncertain Parameters: A Set-Membership Approach

    Directory of Open Access Journals (Sweden)

    Thomas Bak

    2012-07-01

    Full Text Available In this paper a set-membership approach for fault detection of a benchmark wind turbine is proposed. The benchmark represents relevant fault scenarios in the control system, including sensor, actuator and system faults. In addition we also consider parameter uncertainties and uncertainties on the torque coefficient. High noise on the wind speed measurement, nonlinearities in the aerodynamic torque and uncertainties on the parameters make fault detection a challenging problem. We use an effective wind speed estimator to reduce the noise on the wind speed measurements. A set-membership approach is used generate a set that contains all states consistent with the past measurements and the given model of the wind turbine including uncertainties and noise. This set represents all possible states the system can be in if not faulty. If the current measurement is not consistent with this set, a fault is detected. For representation of these sets we use zonotopes and for modeling of uncertainties we use matrix zonotopes, which yields a computationally efficient algorithm. The method is applied to the wind turbine benchmark problem without and with uncertainties. The result demonstrates the effectiveness of the proposed method compared to other proposed methods applied to the same problem. An advantage of the proposed method is that there is no need for threshold design, and it does not produce positive false alarms. In the case where uncertainty on the torque lookup table is introduced, some faults are not detectable. Previous research has not addressed this uncertainty. The method proposed here requires equal or less detection time than previous results.

  4. Fault Detection Based on Tracking Differentiator Applied on the Suspension System of Maglev Train

    Directory of Open Access Journals (Sweden)

    Hehong Zhang

    2015-01-01

    Full Text Available A fault detection method based on the optimized tracking differentiator is introduced. It is applied on the acceleration sensor of the suspension system of maglev train. It detects the fault of the acceleration sensor by comparing the acceleration integral signal with the speed signal obtained by the optimized tracking differentiator. This paper optimizes the control variable when the states locate within or beyond the two-step reachable region to improve the performance of the approximate linear discrete tracking differentiator. Fault-tolerant control has been conducted by feedback based on the speed signal acquired from the optimized tracking differentiator when the acceleration sensor fails. The simulation and experiment results show the practical usefulness of the presented method.

  5. Voltage Based Detection Method for High Impedance Fault in a Distribution System

    Science.gov (United States)

    Thomas, Mini Shaji; Bhaskar, Namrata; Prakash, Anupama

    2016-09-01

    High-impedance faults (HIFs) on distribution feeders cannot be detected by conventional protection schemes, as HIFs are characterized by their low fault current level and waveform distortion due to the nonlinearity of the ground return path. This paper proposes a method to identify the HIFs in distribution system and isolate the faulty section, to reduce downtime. This method is based on voltage measurements along the distribution feeder and utilizes the sequence components of the voltages. Three models of high impedance faults have been considered and source side and load side breaking of the conductor have been studied in this work to capture a wide range of scenarios. The effect of neutral grounding of the source side transformer is also accounted in this study. The results show that the algorithm detects the HIFs accurately and rapidly. Thus, the faulty section can be isolated and service can be restored to the rest of the consumers.

  6. Runtime Verification in Context : Can Optimizing Error Detection Improve Fault Diagnosis

    Science.gov (United States)

    Dwyer, Matthew B.; Purandare, Rahul; Person, Suzette

    2010-01-01

    Runtime verification has primarily been developed and evaluated as a means of enriching the software testing process. While many researchers have pointed to its potential applicability in online approaches to software fault tolerance, there has been a dearth of work exploring the details of how that might be accomplished. In this paper, we describe how a component-oriented approach to software health management exposes the connections between program execution, error detection, fault diagnosis, and recovery. We identify both research challenges and opportunities in exploiting those connections. Specifically, we describe how recent approaches to reducing the overhead of runtime monitoring aimed at error detection might be adapted to reduce the overhead and improve the effectiveness of fault diagnosis.

  7. To err is robotic, to tolerate immunological: fault detection in multirobot systems.

    Science.gov (United States)

    Tarapore, Danesh; Lima, Pedro U; Carneiro, Jorge; Christensen, Anders Lyhne

    2015-02-02

    Fault detection and fault tolerance represent two of the most important and largely unsolved issues in the field of multirobot systems (MRS). Efficient, long-term operation requires an accurate, timely detection, and accommodation of abnormally behaving robots. Most existing approaches to fault-tolerance prescribe a characterization of normal robot behaviours, and train a model to recognize these behaviours. Behaviours unrecognized by the model are consequently labelled abnormal or faulty. MRS employing these models do not transition well to scenarios involving temporal variations in behaviour (e.g., online learning of new behaviours, or in response to environment perturbations). The vertebrate immune system is a complex distributed system capable of learning to tolerate the organism's tissues even when they change during puberty or metamorphosis, and to mount specific responses to invading pathogens, all without the need of a genetically hardwired characterization of normality. We present a generic abnormality detection approach based on a model of the adaptive immune system, and evaluate the approach in a swarm of robots. Our results reveal the robust detection of abnormal robots simulating common electro-mechanical and software faults, irrespective of temporal changes in swarm behaviour. Abnormality detection is shown to be scalable in terms of the number of robots in the swarm, and in terms of the size of the behaviour classification space.

  8. Artificial neural network-based all-sky power estimation and fault detection in photovoltaic modules

    Science.gov (United States)

    Jazayeri, Kian; Jazayeri, Moein; Uysal, Sener

    2017-04-01

    The development of a system for output power estimation and fault detection in photovoltaic (PV) modules using an artificial neural network (ANN) is presented. Over 30,000 healthy and faulty data sets containing per-minute measurements of PV module output power (W) and irradiance (W/m2) along with real-time calculations of the Sun's position in the sky and the PV module surface temperature, collected during a three-month period, are fed to different ANNs as training paths. The first ANN being trained on healthy data is used for PV module output power estimation and the second ANN, which is trained on both healthy and faulty data, is utilized for PV module fault detection. The proposed PV module-level fault detection algorithm can expectedly be deployed in broader PV fleets by taking developmental considerations. The machine-learning-based automated system provides the possibility of all-sky real-time monitoring and fault detection of PV modules under any meteorological condition. Utilizing the proposed system, any power loss caused by damaged cells, shading conditions, accumulated dirt and dust on module surface, etc., is detected and reported immediately, potentially yielding increased reliability and efficiency of the PV systems and decreased support and maintenance costs.

  9. 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.

  10. Detection and Modeling of High-Dimensional Thresholds for Fault Detection and Diagnosis

    Science.gov (United States)

    He, Yuning

    2015-01-01

    Many Fault Detection and Diagnosis (FDD) systems use discrete models for detection and reasoning. To obtain categorical values like oil pressure too high, analog sensor values need to be discretized using a suitablethreshold. Time series of analog and discrete sensor readings are processed and discretized as they come in. This task isusually performed by the wrapper code'' of the FDD system, together with signal preprocessing and filtering. In practice,selecting the right threshold is very difficult, because it heavily influences the quality of diagnosis. If a threshold causesthe alarm trigger even in nominal situations, false alarms will be the consequence. On the other hand, if threshold settingdoes not trigger in case of an off-nominal condition, important alarms might be missed, potentially causing hazardoussituations. In this paper, we will in detail describe the underlying statistical modeling techniques and algorithm as well as the Bayesian method for selecting the most likely shape and its parameters. Our approach will be illustrated by several examples from the Aerospace domain.

  11. Analysis of microseismic activity detected by the WIZARD array, Alpine Fault, New Zealand

    Science.gov (United States)

    Feenstra, J. P.; Roecker, S. W.; Thurber, C. H.; Lord, N.; O'Brien, G.; Pesicek, J. D.; Townend, J.; Bannister, S. C.

    2012-12-01

    A primary goal for the UW-Madison-RPI WIZARD array is the characterization of background seismicity around the Deep Fault Drilling Project (DFDP) site on the Alpine Fault, South Island, New Zealand. The WIZARD array consists of 20 stations, half broadband, deployed for a planned 2-year period around the Whataroa Valley DFDP-2 drill site. Two neighboring arrays, SAMBA (Victoria University of Wellington) to the southwest and ALFA'12 (GNS Science) to the northeast, along with several GeoNet permanent stations, provide broad coverage of the region. The earthquakes that are detected will (1) help to define the geometry of the Alpine Fault and other active faults at depth, (2) provide data for seismic imaging, focal mechanisms, and shear-wave splitting analysis, and (3) enable the assessment of possible changes in seismic activity induced by future fault zone drilling. We are currently analyzing data from the first 2 months of the deployment. Dozens of nearby earthquakes (S-P time of up to a few seconds) have been detected, far more than are in the New Zealand GeoNET catalog. This is expected because the magnitude completion level of the GeoNet seismometer network is around 2.5 in the Whataroa region, due to a relatively sparse station coverage. In this presentation, we report on earthquake location results for 8 months of WIZARD data, along with focal mechanisms for selected larger events.

  12. 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).

  13. Remote sensing analysis for fault-zones detection in the Central Andean Plateau (Catamarca, Argentina)

    Science.gov (United States)

    Traforti, Anna; Massironi, Matteo; Zampieri, Dario; Carli, Cristian

    2015-04-01

    Remote sensing techniques have been extensively used to detect the structural framework of investigated areas, which includes lineaments, fault zones and fracture patterns. The identification of these features is fundamental in exploration geology, as it allows the definition of suitable sites for the exploitation of different resources (e.g. ore mineral, hydrocarbon, geothermal energy and groundwater). Remote sensing techniques, typically adopted in fault identification, have been applied to assess the geological and structural framework of the Laguna Blanca area (26°35'S-66°49'W). This area represents a sector of the south-central Andes localized in the Argentina region of Catamarca, along the south-eastern margin of the Puna plateau. The study area is characterized by a Precambrian low-grade metamorphic basement intruded by Ordovician granitoids. These rocks are unconformably covered by a volcano-sedimentary sequence of Miocene age, followed by volcanic and volcaniclastic rocks of Upper Miocene to Plio-Pleistocene age. All these units are cut by two systems of major faults, locally characterized by 15-20 m wide damage zones. The detection of main tectonic lineaments in the study area was firstly carried out by classical procedures: image sharpening of Landsat 7 ETM+ images, directional filters applied to ASTER images, medium resolution Digital Elevation Models analysis (SRTM and ASTER GDEM) and hill shades interpretation. In addition, a new approach in fault zone identification, based on multispectral satellite images classification, has been tested in the Laguna Blanca area and in other sectors of south-central Andes. In this perspective, several prominent fault zones affecting basement and granitoid rocks have been sampled. The collected fault gouge samples have been analyzed with a Field-Pro spectrophotometer mounted on a goniometer. We acquired bidirectional reflectance spectra, from 0.35μm to 2.5μm with 1nm spectral sampling, of the sampled fault rocks

  14. Ion thruster design and analysis

    Science.gov (United States)

    Kami, S.; Schnelker, D. E.

    1976-01-01

    Questions concerning the mechanical design of a thruster are considered, taking into account differences in the design of an 8-cm and a 30-cm model. The components of a thruster include the thruster shell assembly, the ion extraction electrode assembly, the cathode isolator vaporizer assembly, the neutralizer isolator vaporizer assembly, ground screen and mask, and the main isolator vaporizer assembly. Attention is given to the materials used in thruster fabrication, the advanced manufacturing methods used, details of thruster performance, an evaluation of thruster life, structural and thermal design considerations, and questions of reliability and quality assurance.

  15. A Hybrid Fault Detection Approach for Context-aware Wireless Sensor Networks

    NARCIS (Netherlands)

    Warriach, Ehsan Ullah; Nguyen, Tuan Anh; Aiello, Marco; Tei, Kenji

    2012-01-01

    Wireless Sensor Network (WSN) deployment experiences show that data collected is prone to be imprecise and faulty due to internal and external influences, such as battery drain, environmental interference, sensor aging. An early detection of such faults is necessary for the effective operation of

  16. Fault detection properties of global, local and time evolving models for batch process monitoring

    NARCIS (Netherlands)

    Ramaker, H. J.; van Sprang, E. N. M.; Westerhuis, J. A.; Smilde, A. K.

    2005-01-01

    This paper discusses alternative methods for batch process monitoring. Two alternative methods are investigated and compared to an existing one (the benchmark). A description of the models is given and the performance is discussed by means of fault detection performance indices. The performance

  17. Fault detection properties of global, local and time evolving models for batch process monitoring.

    NARCIS (Netherlands)

    Ramaker, H.J.; van Sprang, E.N.M.; Westerhuis, J.A.; Smilde, A.K.

    2005-01-01

    This paper discusses alternative methods for batch process monitoring. Two alternative methods are investigated and compared to an existing one (the benchmark). A description of the models is given and the performance is discussed by means of fault detection performance indices. The performance

  18. A Fault Detection Mechanism in a Data-flow Scheduled Multithreaded Processor

    NARCIS (Netherlands)

    Fu, J.; Yang, Q.; Poss, R.; Jesshope, C.R.; Zhang, C.

    2014-01-01

    This paper designs and implements the Redundant Multi-Threading (RMT) in a Data-flow scheduled MultiThreaded (DMT) multicore processor, called Data-flow scheduled Redundant Multi-Threading (DRMT). Meanwhile, It presents Asynchronous Output Comparison (AOC) for RMT techniques to avoid fault detection

  19. Soil-gas helium and surface-waves detection of fault zones in ...

    Indian Academy of Sciences (India)

    Soil-gas helium emanometry has been utilized in Wailapally watershed, near Hyderabad in southern India, for the detection of fracture and fault zones in a granite basement terrain having a thin regolith. Based on satellite imagery and geologic mapping, three sites were selected for detailed investigation. High spatial ...

  20. Soil-gas helium and surface-waves detection of fault zones in ...

    Indian Academy of Sciences (India)

    in southern India, for the detection of fracture and fault zones in a granite basement terrain having ... groundwater resource zone in hard rock terrains. A significant ..... 51 121–129. Xu C and Butt S D 2006 Evaluation of MASW techniques to image steeply dipping cavities in laterally inhomogeneous terrain; J. Appl. Geophys.

  1. The New Method of the PV Panels Fault Detection Using Impedance Spectroscopy

    DEFF Research Database (Denmark)

    Symonowicz, Joanna Karolina; Riedel, Nicholas; Thorsteinsson, Sune

    The aim of our project is to develop a new method for photovoltaic (PV) panel fault detection based on analyzing impedance spectroscopy (IS) spectra. Although this technique was successful in assessing the state of degradation of fuel cells and batteries, it has never been applied to PV cells...

  2. Poster Abstract : Fault Detection in Wireless Sensor Networks: A Hybrid Approach

    NARCIS (Netherlands)

    Warriach, Ehsan; Nguyen, Tuan Anh; Aiello, Marco; Tel, Kenji

    2012-01-01

    Wireless Sensor Network (WSN) deployment experiences show that data collected is prone to be imprecise and faulty due to internal and external influences, such as battery drain, environmental interference, sensor aging. An early detection of such faults is necessary for the effective operation of

  3. Closed-loop fault detection for full-envelope flight vehicle with measurement delays

    Directory of Open Access Journals (Sweden)

    Wang Zhaolei

    2015-06-01

    Full Text Available A closed-loop fault detection problem is investigated for the full-envelope flight vehicle with measurement delays, where the flight dynamics are modeled as a switched system with delayed feedback signals. The mode-dependent observer-based fault detection filters and state estimation feedback controllers are derived by considering the delays’ impact on the control system and fault detection system simultaneously. Then, considering updating lags of the controllers/filters’ switching signals which are introduced by the delayed measurement of altitude and Mach number, an asynchronous H∞ analysis method is proposed and the system model is further augmented to be an asynchronously switched time-delay system. Also, the global stability and desired performance of the augmented system are guaranteed by combining the switched delay-dependent Lyapunov–Krasovskii functional method with the average dwell time method (ADT, and the delay-dependent existing conditions for the controllers and fault detection filters are obtained in the form of the linear matrix inequalities (LMIs. Finally, numerical example based on the hypersonic vehicles and highly maneuverable technology (HiMAT vehicle is given to demonstrate the merits of the proposed method.

  4. Model-based fault detection for proton exchange membrane fuel cell ...

    African Journals Online (AJOL)

    In this paper, an intelligent model-based fault detection (FD) is developed for proton exchange membrane fuel cell (PEMFC) dynamic systems using an independent radial basis function (RBF) networks. The novelty is that this RBF networks is used to model the PEMFC dynamic systems and residuals are generated based ...

  5. Infrared Thermographic Diagnosis Mechanism for Fault Detection of Ball Bearing under Dynamic Loading Conditions

    International Nuclear Information System (INIS)

    Seo, Jin Ju; Yoon, Hanvit; Kim, Dong Yeon; Hong, Dong Pyo; Kim, Won Tae

    2011-01-01

    Fault detection for dynamic loading conditions of rotational machineries was considered from the contactless, non-destructive infrared thermographic method, rather than the traditional diagnosis method. In this paper, by applying a rotating deep-grooved ball bearing, passive thermographic experiment was performed as an alternative way proceeding the traditional fault monitoring. In addition, the thermographic experiments were compared with the vibration spectrum analysis to evaluate the efficiency of the proposed method. Based on the results, it was concluded the temperature characteristics of the ball bearing under dynamic loading conditions were analyzed thoroughly

  6. Operations management system advanced automation: Fault detection isolation and recovery prototyping

    Science.gov (United States)

    Hanson, Matt

    1990-01-01

    The purpose of this project is to address the global fault detection, isolation and recovery (FDIR) requirements for Operation's Management System (OMS) automation within the Space Station Freedom program. This shall be accomplished by developing a selected FDIR prototype for the Space Station Freedom distributed processing systems. The prototype shall be based on advanced automation methodologies in addition to traditional software methods to meet the requirements for automation. A secondary objective is to expand the scope of the prototyping to encompass multiple aspects of station-wide fault management (SWFM) as discussed in OMS requirements documentation.

  7. 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

    is in terms of a patient simulation model, where the model in the detector is the same as the patient simulation model used for evaluation of the detector. The detection module consists of two CGM sensors, two fault detectors, a fault isolator, and an adaptive unscented Kalman filter (UKF). Two types...... 100 out of 100 simulated drifts and 485 out of 500 simulated PISA events. Compared to a nonadaptive UKF, the adaptive UKF reduces the deviation of the CGM measurements from their paired blood glucose concentrations from 72.0% to 12.5% when CGM is corrupted by drift, and from 10.7% to 6.8% when CGM...

  8. Fault Detection and Diagnosis of Railway Point Machines by Sound Analysis

    Science.gov (United States)

    Lee, Jonguk; Choi, Heesu; Park, Daihee; Chung, Yongwha; Kim, Hee-Young; Yoon, Sukhan

    2016-01-01

    Railway point devices act as actuators that provide different routes to trains by driving switchblades from the current position to the opposite one. Point failure can significantly affect railway operations, with potentially disastrous consequences. Therefore, early detection of anomalies is critical for monitoring and managing the condition of rail infrastructure. We present a data mining solution that utilizes audio data to efficiently detect and diagnose faults in railway condition monitoring systems. The system enables extracting mel-frequency cepstrum coefficients (MFCCs) from audio data with reduced feature dimensions using attribute subset selection, and employs support vector machines (SVMs) for early detection and classification of anomalies. Experimental results show that the system enables cost-effective detection and diagnosis of faults using a cheap microphone, with accuracy exceeding 94.1% whether used alone or in combination with other known methods. PMID:27092509

  9. Fault Detection and Diagnosis of Railway Point Machines by Sound Analysis.

    Science.gov (United States)

    Lee, Jonguk; Choi, Heesu; Park, Daihee; Chung, Yongwha; Kim, Hee-Young; Yoon, Sukhan

    2016-04-16

    Railway point devices act as actuators that provide different routes to trains by driving switchblades from the current position to the opposite one. Point failure can significantly affect railway operations, with potentially disastrous consequences. Therefore, early detection of anomalies is critical for monitoring and managing the condition of rail infrastructure. We present a data mining solution that utilizes audio data to efficiently detect and diagnose faults in railway condition monitoring systems. The system enables extracting mel-frequency cepstrum coefficients (MFCCs) from audio data with reduced feature dimensions using attribute subset selection, and employs support vector machines (SVMs) for early detection and classification of anomalies. Experimental results show that the system enables cost-effective detection and diagnosis of faults using a cheap microphone, with accuracy exceeding 94.1% whether used alone or in combination with other known methods.

  10. Fault Detection and Diagnosis of Railway Point Machines by Sound Analysis

    Directory of Open Access Journals (Sweden)

    Jonguk Lee

    2016-04-01

    Full Text Available Railway point devices act as actuators that provide different routes to trains by driving switchblades from the current position to the opposite one. Point failure can significantly affect railway operations, with potentially disastrous consequences. Therefore, early detection of anomalies is critical for monitoring and managing the condition of rail infrastructure. We present a data mining solution that utilizes audio data to efficiently detect and diagnose faults in railway condition monitoring systems. The system enables extracting mel-frequency cepstrum coefficients (MFCCs from audio data with reduced feature dimensions using attribute subset selection, and employs support vector machines (SVMs for early detection and classification of anomalies. Experimental results show that the system enables cost-effective detection and diagnosis of faults using a cheap microphone, with accuracy exceeding 94.1% whether used alone or in combination with other known methods.

  11. Faults and fractures detection in 2D seismic data based on principal component analysis

    Directory of Open Access Journals (Sweden)

    Poorandokht Soltani

    2017-12-01

    Full Text Available Various approached have been introduced to extract as much as information form seismic image for any specific reservoir or geological study. Modeling of faults and fractures are among the most attracted objects for interpretation in geological study on seismic images that several strategies have been presented for this specific purpose. In this study, we have presented a modified approach of application concept of the principle components analysis to enhance faults and fractures from low quality seismic image. In the first step, relevant attributes considering imaging faults and fractures were have drawn based on vast study on previous successful applications of different attributes. Subsequently, major informative components of each attribute were defined by performing principle component analysis. Since random noise in seismic image exhibits no correlation in seismic data, true reflectors and diffraction events show high coherency value thus these objects would be separated into different orthogonal components in principle component analysis. It will make it easy to remove irrelevant information considering faults and fractures from seismic image and thus will make a higher quality image by combining attribute sections in principle component analysis. Afterwards, selected components were stacked to enhance the fault position in final image. However, since that are other geological objects that might show correlation in other orthogonal components, so there should be refinement step on the final image to stack only the favorable information. This approach was performed on a field land data example form north east of Iran. Result of application the proposed strategy shows that the method is capable to image faults compared to the conventional image analysis for fault detection. The method was also capable to image accurate position of the body of mud volcanoes exited in the image that could not be easily tracked by conventional seismic image

  12. Fault detection of feed water treatment process using PCA-WD with parameter optimization.

    Science.gov (United States)

    Zhang, Shirong; Tang, Qian; Lin, Yu; Tang, Yuling

    2017-05-01

    Feed water treatment process (FWTP) is an essential part of utility boilers; and fault detection is expected for its reliability improvement. Classical principal component analysis (PCA) has been applied to FWTPs in our previous work; however, the noises of T 2 and SPE statistics result in false detections and missed detections. In this paper, Wavelet denoise (WD) is combined with PCA to form a new algorithm, (PCA-WD), where WD is intentionally employed to deal with the noises. The parameter selection of PCA-WD is further formulated as an optimization problem; and PSO is employed for optimization solution. A FWTP, sustaining two 1000MW generation units in a coal-fired power plant, is taken as a study case. Its operation data is collected for following verification study. The results show that the optimized WD is effective to restrain the noises of T 2 and SPE statistics, so as to improve the performance of PCA-WD algorithm. And, the parameter optimization enables PCA-WD to get its optimal parameters in an automatic way rather than on individual experience. The optimized PCA-WD is further compared with classical PCA and sliding window PCA (SWPCA), in terms of four cases as bias fault, drift fault, broken line fault and normal condition, respectively. The advantages of the optimized PCA-WD, against classical PCA and SWPCA, is finally convinced with the results. Copyright © 2017 ISA. Published by Elsevier Ltd. All rights reserved.

  13. 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.

  14. 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.

  15. Comparative Study of Parametric and Non-parametric Approaches in Fault Detection and Isolation

    DEFF Research Database (Denmark)

    Katebi, S.D.; Blanke, M.; Katebi, M.R.

    This report describes a comparative study between two approaches to fault detection and isolation in dynamic systems. The first approach uses a parametric model of the system. The main components of such techniques are residual and signature generation for processing and analyzing. The second app...... algorithms employed are adopted from the template matching in pattern recognition. Extensive simulation studies are performed to demonstrate satisfactory performance of the proposed techniques. The advantages and disadvantages of each approach are discussed and analyzed....... approach is non-parametric in the sense that the signature analysis is only dependent on the frequency or time domain information extracted directly from the input-output signals. Based on these approaches, two different fault monitoring schemes are developed where the feature extraction and fault decision...

  16. A windowing and mapping strategy for gear tooth fault detection of a planetary gearbox

    Science.gov (United States)

    Liang, Xihui; Zuo, Ming J.; Liu, Libin

    2016-12-01

    When there is a single cracked tooth in a planet gear, the cracked tooth is enmeshed for very short time duration in comparison to the total time of a full revolution of the planet gear. The fault symptom generated by the single cracked tooth may be very weak. This study aims to develop a windowing and mapping strategy to interpret the vibration signal of a planetary gear at the tooth level. The fault symptoms generated by a single cracked tooth of the planet gear of interest can be extracted. The health condition of the planet gear can be assessed by comparing the differences among the signals of all teeth of the planet gear. The proposed windowing and mapping strategy is tested with both simulated vibration signals and experimental vibration signals. The tooth signals can be successfully decomposed and a single tooth fault on a planet gear can be effectively detected.

  17. 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.

  18. CRISP. Simulation tool for fault detection and diagnostics in high-DG power networks

    International Nuclear Information System (INIS)

    Fontela, M.; Andrieu, C.; Raison, B.

    2004-08-01

    This document gives a description of a tool proposed for fault detection and diagnostics. The main principles of the functions of fault localization are described and detailed for a given MV network that will be used for the ICT experiment in Grenoble (experiment 3B). The aim of the tool is to create a technical, simple and realistic context for testing ICT dedicated to an electrical application. The tool gives the expected inputs and outputs contents of the various distributed ICT components when a fault occurs in a given MV network. So the requirements for the ICT components are given in term of expected data collected, analysed and transmitted. Several examples are given in order to illustrate the inputs/outputs in case of different faults. The tool includes a topology description which is a main aspect to develop in the future for managing the distribution network. Updating topology in real time will become necessary for fault diagnostic and protection, but also necessary for the various possible added applications (local market balance and local electrical power quality for instance). The tool gives a context and a simple view for the ICT components behaviours assuming an ideal response and transmission from them. The real characteristics and possible limitations for the ICT (information latency, congestion, security) will be established during the experiments from the same context described in the HTFD tool

  19. Detection of Static Eccentricity Fault in Saturated Induction Motors by ...

    African Journals Online (AJOL)

    Unfortunately, motor current signature analysis (MCSA) cannot detect the small degrees of the purely static eccentricity (SE) defects, while the air-gap magnetic flux signature analysis (FSA) is applied successfully. The simulation results are obtained by using time stepping finite elements (TSFE) method. In order to show the ...

  20. Applications of pattern recognition techniques to online fault detection

    International Nuclear Information System (INIS)

    Singer, R.M.; Gross, K.C.; King, R.W.

    1993-01-01

    A common problem to operators of complex industrial systems is the early detection of incipient degradation of sensors and components in order to avoid unplanned outages, to orderly plan for anticipated maintenance activities and to assure continued safe operation. In such systems, there usually are a large number of sensors (upwards of several thousand is not uncommon) serving many functions, ranging from input to control systems, monitoring of safety parameters and component performance limits, system environmental conditions, etc. Although sensors deemed to measure important process conditions are generally alarmed, the alarm set points usually are just high-low limits and the operator's response to such alarms is based on written procedures and his or her experience and training. In many systems this approach has been successful, but in situations where the cost of a forced outage is high an improved method is needed. In such cases it is desirable, if not necessary, to detect disturbances in either sensors or the process prior to any actual failure that could either shut down the process or challenge any safety system that is present. Recent advances in various artificial intelligence techniques have provided the opportunity to perform such functions of early detection and diagnosis. In this paper, the experience gained through the application of several pattern-recognition techniques to the on-line monitoring and incipient disturbance detection of several coolant pumps and numerous sensors at the Experimental Breeder Reactor-II (EBR-II) which is located at the Idaho National Engineering Laboratory is presented

  1. Modeling and detection of high Impedance arcing fault in medium voltage networks

    Energy Technology Data Exchange (ETDEWEB)

    Elkalashy, N.I.

    2007-07-01

    In this dissertation, a universal arc representation using the Electromagnetic Transient Program (Emp) is first developed. It is accomplished based on the bilateral interaction between Emp network and Transient Analysis Control System (Tacks) field. This arc modeling procedure is used as a useful guide to present a new model for high impedance arcing faults due to leaning trees. At the Power Systems and High Voltage Laboratory, Helsinki University of Technology (TKK), Finland, experiments have been performed to measure the fault characteristics due to leaning trees and therefore to verify the proposed model. Towards investigating detection facilities of this fault type, the fault model is incorporated at different locations in 20 kV Medium Voltage (MV) networks using the ATP draw program, which is a graphical interface utilized to simplify the ATP/Emp processing. Then, phase quantities and residual components are taken at different measuring nodes in the simulated networks. It is revealed that the main feature of this fault type that can enhance its detection is the periodicity of electromagnetic transients created by repetitions of the arc-reignition after each current zero-crossing. This feature is obtained considering different earthing concepts. Different detection techniques are proposed based on Discrete Wavelet Transform (DWT). The absolute sum of the wavelet coefficients in the respective frequency band is investigated for the detection purposes while several selectivity functions are proposed for the first time. The selectivity functions are presented using Logic Functions, fundamental current components and transient power directionalities. Test cases provide evidence of the efficacy of the proposed techniques. This dissertation is written in a form of the article dissertation. Its core depends on both of a summary and six original publications. (orig.)

  2. Iodine Hall Thruster

    Science.gov (United States)

    Szabo, James

    2015-01-01

    Iodine enables dramatic mass and cost savings for lunar and Mars cargo missions, including Earth escape and near-Earth space maneuvers. The demonstrated throttling ability of iodine is important for a singular thruster that might be called upon to propel a spacecraft from Earth to Mars or Venus. The ability to throttle efficiently is even more important for missions beyond Mars. In the Phase I project, Busek Company, Inc., tested an existing Hall thruster, the BHT-8000, on iodine propellant. The thruster was fed by a high-flow iodine feed system and supported by an existing Busek hollow cathode flowing xenon gas. The Phase I propellant feed system was evolved from a previously demonstrated laboratory feed system. Throttling of the thruster between 2 and 11 kW at 200 to 600 V was demonstrated. Testing showed that the efficiency of iodine fueled BHT-8000 is the same as with xenon, with iodine delivering a slightly higher thrust-to-power (T/P) ratio. In Phase II, a complete iodine-fueled system was developed, including the thruster, hollow cathode, and iodine propellant feed system. The nominal power of the Phase II system is 8 kW; however, it can be deeply throttled as well as clustered to much higher power levels. The technology also can be scaled to greater than 100 kW per thruster to support megawatt-class missions. The target thruster efficiency for the full-scale system is 65 percent at high specific impulse (Isp) (approximately 3,000 s) and 60 percent at high thrust (Isp approximately 2,000 s).

  3. Linear Quadratic Controller with Fault Detection in Compact Disk Players

    DEFF Research Database (Denmark)

    Vidal, Enrique Sanchez; Hansen, K.G.; Andersen, R.S.

    2001-01-01

    The design of the positioning controllers in Optical Disk Drives are today subjected to a trade off between an acceptable suppression of external disturbances and an acceptable immunity against surfaces defects. In this paper an algorithm is suggested to detect defects of the disk surface combined...... with an observer and a Linear Quadratic Regulator. As a result, the mentioned trade off is minimized and the playability of the tested compact disk player is considerably enhanced....

  4. Multiple fault separation and detection by joint subspace learning for the health assessment of wind turbine gearboxes

    Science.gov (United States)

    Du, Zhaohui; Chen, Xuefeng; Zhang, Han; Zi, Yanyang; Yan, Ruqiang

    2017-09-01

    The gearbox of a wind turbine (WT) has dominant failure rates and highest downtime loss among all WT subsystems. Thus, gearbox health assessment for maintenance cost reduction is of paramount importance. The concurrence of multiple faults in gearbox components is a common phenomenon due to fault induction mechanism. This problem should be considered before planning to replace the components of the WT gearbox. Therefore, the key fault patterns should be reliably identified from noisy observation data for the development of an effective maintenance strategy. However, most of the existing studies focusing on multiple fault diagnosis always suffer from inappropriate division of fault information in order to satisfy various rigorous decomposition principles or statistical assumptions, such as the smooth envelope principle of ensemble empirical mode decomposition and the mutual independence assumption of independent component analysis. Thus, this paper presents a joint subspace learning-based multiple fault detection (JSL-MFD) technique to construct different subspaces adaptively for different fault patterns. Its main advantage is its capability to learn multiple fault subspaces directly from the observation signal itself. It can also sparsely concentrate the feature information into a few dominant subspace coefficients. Furthermore, it can eliminate noise by simply performing coefficient shrinkage operations. Consequently, multiple fault patterns are reliably identified by utilizing the maximum fault information criterion. The superiority of JSL-MFD in multiple fault separation and detection is comprehensively investigated and verified by the analysis of a data set of a 750 kW WT gearbox. Results show that JSL-MFD is superior to a state-of-the-art technique in detecting hidden fault patterns and enhancing detection accuracy.

  5. E-core transverse flux machine with integrated fault detection system

    DEFF Research Database (Denmark)

    Rasmussen, Peter Omand; Runólfsson, Gunnar; Thorsdóttir, Thórunn Ágústa

    2011-01-01

    The E-core transverse flux machine, which is a variation of the classical Switched Reluctance machine (SRM), have all the basic properties to be considered as a very fault tolerant machine. Every single coil in the machine is isolated from the each others both magnetic, electrical and to some...... extent also thermal. Since the E-core transverse flux-machine belongs to the family of the SRMs it has unique properties of intervals without current in the windings. By careful investigation of the voltage and current in these intervals a very simple method to detect single and partial turn short...... circuit faults have been developed. For other types of machines the single and partial turn short circuit is very difficult to deal with and requires normally very comprehensive detection and calculation schemes. The developed detection algorithm combined with the E-core transverse flux machine...

  6. Fault Detection for Non-Gaussian Stochastic Systems with Time-Varying Delay

    Directory of Open Access Journals (Sweden)

    Tao Li

    2013-01-01

    Full Text Available Fault detection (FD for non-Gaussian stochastic systems with time-varying delay is studied. The available information for the addressed problem is the input and the measured output probability density functions (PDFs of the system. In this framework, firstly, by constructing an augmented Lyapunov functional, which involves some slack variables and a tuning parameter, a delay-dependent condition for the existence of FD observer is derived in terms of linear matrix inequality (LMI and the fault can be detected through a threshold. Secondly, in order to improve the detection sensitivity performance, the optimal algorithm is applied to minimize the threshold value. Finally, paper-making process example is given to demonstrate the applicability of the proposed approach.

  7. New method of silicon photovoltaic panel fault detection using impedance spectroscopy

    DEFF Research Database (Denmark)

    Symonowicz, Joanna Karolina; Riedel, Nicholas; Thorsteinsson, Sune

    2017-01-01

    The aim of our project is to develop a new method for photovoltaic (PV) panel fault detection based on analysing its impedance spectra (IS). Although this technique was successful in assessing the state of degradation of fuel cells and batteries [1, 2], it has never been applied to PV cells...... on a wide scale. In this paper, we show that, unlike current-voltage (I-V) tests, the IS method is capable of early detection of changes in PV panel parameters due to microcracks and potential-induced degradation (PID). Although our measurements are only successful under dark conditions, the results...... are similar for both laboratory environment and for outdoor tests in various weather conditions. A fully developed IS technique, accounting for all kinds of most common PV panel degradation types, would surpass the existing PV fault detection methods then it comes to cost and accuracy [3,4]....

  8. Monitoring a robot swarm using a data-driven fault detection approach

    KAUST Repository

    Khaldi, Belkacem

    2017-06-30

    Using swarm robotics system, with one or more faulty robots, to accomplish specific tasks may lead to degradation in performances complying with the target requirements. In such circumstances, robot swarms require continuous monitoring to detect abnormal events and to sustain normal operations. In this paper, an innovative exogenous fault detection method for monitoring robots swarm is presented. The method merges the flexibility of principal component analysis (PCA) models and the greater sensitivity of the exponentially-weighted moving average (EWMA) and cumulative sum (CUSUM) control charts to insidious changes. The method is tested and evaluated on a swarm of simulated foot-bot robots performing a circle formation task, via the viscoelastic control model. We illustrate through simulated data collected from the ARGoS simulator that a significant improvement in fault detection can be obtained by using the proposed method where compared to the conventional PCA-based methods (i.e., T2 and Q).

  9. A measurement-based fault detection approach applied to monitor robots swarm

    KAUST Repository

    Khaldi, Belkacem

    2017-07-10

    Swarm robotics requires continuous monitoring to detect abnormal events and to sustain normal operations. Indeed, swarm robotics with one or more faulty robots leads to degradation of performances complying with the target requirements. This paper present an innovative data-driven fault detection method for monitoring robots swarm. The method combines the flexibility of principal component analysis (PCA) models and the greater sensitivity of the exponentially-weighted moving average control chart to incipient changes. We illustrate through simulated data collected from the ARGoS simulator that a significant improvement in fault detection can be obtained by using the proposed methods as compared to the use of the conventional PCA-based methods.

  10. Fault detection and isolation for a full-scale railway vehicle suspension with multiple Kalman filters

    Science.gov (United States)

    Jesussek, Mathias; Ellermann, Katrin

    2014-12-01

    Reliability and dependability in complex mechanical systems can be improved by fault detection and isolation (FDI) methods. These techniques are key elements for maintenance on demand, which could decrease service cost and time significantly. This paper addresses FDI for a railway vehicle: the mechanical model is described as a multibody system, which is excited randomly due to track irregularities. Various parameters, like masses, spring- and damper-characteristics, influence the dynamics of the vehicle. Often, the exact values of the parameters are unknown and might even change over time. Some of these changes are considered critical with respect to the operation of the system and they require immediate maintenance. The aim of this work is to detect faults in the suspension system of the vehicle. A Kalman filter is used in order to estimate the states. To detect and isolate faults the detection error is minimised with multiple Kalman filters. A full-scale train model with nonlinear wheel/rail contact serves as an example for the described techniques. Numerical results for different test cases are presented. The analysis shows that for the given system it is possible not only to detect a failure of the suspension system from the system's dynamic response, but also to distinguish clearly between different possible causes for the changes in the dynamical behaviour.

  11. Vibration-Based Adaptive Novelty Detection Method for Monitoring Faults in a Kinematic Chain

    Directory of Open Access Journals (Sweden)

    Jesus Adolfo Cariño-Corrales

    2016-01-01

    Full Text Available This paper presents an adaptive novelty detection methodology applied to a kinematic chain for the monitoring of faults. The proposed approach has the premise that only information of the healthy operation of the machine is initially available and fault scenarios will eventually develop. This approach aims to cover some of the challenges presented when condition monitoring is applied under a continuous learning framework. The structure of the method is divided into two recursive stages: first, an offline stage for initialization and retraining of the feature reduction and novelty detection modules and, second, an online monitoring stage to continuously assess the condition of the machine. Contrary to classical static feature reduction approaches, the proposed method reformulates the features by employing first a Laplacian Score ranking and then the Fisher Score ranking for retraining. The proposed methodology is validated experimentally by monitoring the vibration measurements of a kinematic chain driven by an induction motor. Two faults are induced in the motor to validate the method performance to detect anomalies and adapt the feature reduction and novelty detection modules to the new information. The obtained results show the advantages of employing an adaptive approach for novelty detection and feature reduction making the proposed method suitable for industrial machinery diagnosis applications.

  12. Project of an ion thruster

    International Nuclear Information System (INIS)

    Perche, G.E.

    1983-07-01

    The mercury bombardment electrostatic ion thruster is the most successful electric thruster available today. This work describes a 5 cm diameter ion thruster with 3.000 s specific impulse and 5 mN thrust. The advantages of electric propulsion and the tests that will be performed are also presented. (Author) [pt

  13. 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)

  14. A method for detection and location of high resistance earth faults

    Energy Technology Data Exchange (ETDEWEB)

    Haenninen, S.; Lehtonen, M. [VTT Energy, Espoo (Finland); Antila, E. [ABB Transmit Oy (Finland)

    1998-08-01

    In the first part of this presentation, the theory of earth faults in unearthed and compensated power systems is briefly presented. The main factors affecting the high resistance fault detection are outlined and common practices for earth fault protection in present systems are summarized. The algorithms of the new method for high resistance fault detection and location are then presented. These are based on the change of neutral voltage and zero sequence currents, measured at the high voltage / medium voltage substation and also at the distribution line locations. The performance of the method is analyzed, and the possible error sources discussed. Among these are, for instance, switching actions, thunder storms and heavy snow fall. The feasibility of the method is then verified by an analysis based both on simulated data, which was derived using an EMTP-ATP simulator, and by real system data recorded during field tests at three substations. For the error source analysis, some real case data recorded during natural power system events, is also used

  15. 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)

  16. 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.

  17. Disk Crack Detection for Seeded Fault Engine Test

    Science.gov (United States)

    Luo, Huageng; Rodriguez, Hector; Hallman, Darren; Corbly, Dennis; Lewicki, David G. (Technical Monitor)

    2004-01-01

    Work was performed to develop and demonstrate vibration diagnostic techniques for the on-line detection of engine rotor disk cracks and other anomalies through a real engine test. An existing single-degree-of-freedom non-resonance-based vibration algorithm was extended to a multi-degree-of-freedom model. In addition, a resonance-based algorithm was also proposed for the case of one or more resonances. The algorithms were integrated into a diagnostic system using state-of-the- art commercial analysis equipment. The system required only non-rotating vibration signals, such as accelerometers and proximity probes, and the rotor shaft 1/rev signal to conduct the health monitoring. Before the engine test, the integrated system was tested in the laboratory by using a small rotor with controlled mass unbalances. The laboratory tests verified the system integration and both the non-resonance and the resonance-based algorithm implementations. In the engine test, the system concluded that after two weeks of cycling, the seeded fan disk flaw did not propagate to a large enough size to be detected by changes in the synchronous vibration. The unbalance induced by mass shifting during the start up and coast down was still the dominant response in the synchronous vibration.

  18. Detecting tangential dislocations on planar faults from traction free surface observations

    International Nuclear Information System (INIS)

    Ionescu, Ioan R; Volkov, Darko

    2009-01-01

    We propose in this paper robust reconstruction methods for tangential dislocations on planar faults. We assume that only surface observations are available, and that a traction free condition applies at that surface. This study is an extension to the full three dimensions of Ionescu and Volkov (2006 Inverse Problems 22 2103). We also explore in this present paper the possibility of detecting slow slip events (such as silent earthquakes, or earthquake nucleation phases) from GPS observations. Our study uses extensively an asymptotic estimate for the observed surface displacement. This estimate is first used to derive what we call the moments reconstruction method. Then it is also used for finding necessary conditions for a surface displacement field to have been caused by a slip on a fault. These conditions lead to the introduction of two parameters: the activation factor and the confidence index. They can be computed from the surface observations in a robust fashion. They indicate whether a measured displacement field is due to an active fault. We also infer a second, combined, reconstruction technique blending least square minimization and the moments method. We carefully assess how our reconstruction method is affected by the sensitivity of the observation apparatus and the stepsize for the grid of surface observation points. The maximum permissible stepsize for such a grid is computed for different values of fault depth and orientation. Finally we present numerical examples of reconstruction of faults. We demonstrate that our combined method is sharp, robust and computationally inexpensive. We also note that this method performs satisfactorily for shallow faults, despite the fact that our asymptotic formula deteriorates in that case

  19. Model based fault detection and diagnosis using structured residual approach in a multi-input multi-output system

    Directory of Open Access Journals (Sweden)

    Asokan A.

    2007-01-01

    Full Text Available Fault detection and isolation (FDI is a task to deduce from observed variable of the system if any component is faulty, to locate the faulty components and also to estimate the fault magnitude present in the system. This paper provides a systematic method of fault diagnosis to detect leak in the three-tank process. The proposed scheme makes use of structured residual approach for detection, isolation and estimation of faults acting on the process [1]. This technique includes residual generation and residual evaluation. A literature review showed that the conventional fault diagnosis methods like the ordinary Chisquare (ψ2 test method, generalized likelihood ratio test have limitations such as the "false alarm" problem. From the results it is inferred that the proposed FDI scheme diagnoses better when compared to other conventional methods.

  20. A H-infinity Fault Detection and Diagnosis Scheme for Discrete Nonlinear System Using Output Probability Density Estimation

    International Nuclear Information System (INIS)

    Zhang Yumin; Lum, Kai-Yew; Wang Qingguo

    2009-01-01

    In this paper, a H-infinity fault detection and diagnosis (FDD) scheme for a class of discrete nonlinear system fault using output probability density estimation is presented. Unlike classical FDD problems, the measured output of the system is viewed as a stochastic process and its square root probability density function (PDF) is modeled with B-spline functions, which leads to a deterministic space-time dynamic model including nonlinearities, uncertainties. A weighting mean value is given as an integral function of the square root PDF along space direction, which leads a function only about time and can be used to construct residual signal. Thus, the classical nonlinear filter approach can be used to detect and diagnose the fault in system. A feasible detection criterion is obtained at first, and a new H-infinity adaptive fault diagnosis algorithm is further investigated to estimate the fault. Simulation example is given to demonstrate the effectiveness of the proposed approaches.

  1. Rolling bearing fault detection using an adaptive lifting multiwavelet packet with a 1(1/2) dimension spectrum

    International Nuclear Information System (INIS)

    Jiang, Hongkai; Xia, Yong; Wang, Xiaodong

    2013-01-01

    Defect faults on the surface of rolling bearing elements are the most frequent cause of malfunctions and breakages of electrical machines. Due to increasing demands for quality and reliability, extracting fault features in vibration signals is an important topic for fault detection in rolling bearings. In this paper, a novel adaptive lifting multiwavelet packet with 1(1/2) dimension spectrum to detect defects in rolling bearing elements is developed. The adaptive lifting multiwavelet packet is constructed to match vibration signal properties based on the minimum singular value decomposition (SVD) entropy using a genetic algorithm. A 1(1/2) dimension spectrum is further employed to extract rolling bearing fault characteristic frequencies from background noise. The proposed method is applied to analyze the vibration signal collected from electric locomotive rolling bearings with outer raceway and inner raceway defects. The experimental investigation shows that the method is accurate and robust in rolling bearing fault detection. (paper)

  2. 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.

  3. Detection of intermittent resistive faults in electronic systems based on the mixed-signal boundary-scan standard

    NARCIS (Netherlands)

    Kerkhoff, Hans G.; Ebrahimi, Hassan

    2015-01-01

    In avionics, like glide computers, the problem of No Faults Found (NFF) is a very serious and extremely costly affair. The rare occurrences and short bursts of these faults are the most difficult ones to detect and diagnose in the testing arena. Several techniques are now being developed in ICs by

  4. 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

    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...... 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...

  5. Software fault detection and recovery in critical real-time systems: An approach based on loose coupling

    Energy Technology Data Exchange (ETDEWEB)

    Alho, Pekka, E-mail: pekka.alho@tut.fi; Mattila, Jouni

    2014-10-15

    Highlights: •We analyze fault tolerance in mission-critical real-time systems. •Decoupled architectural model can be used to implement fault tolerance. •Prototype implementation for remote handling control system and service manager. •Recovery from transient faults by restarting services. -- Abstract: Remote handling (RH) systems are used to inspect, make changes to, and maintain components in the ITER machine and as such are an example of mission-critical system. Failure in a critical system may cause damage, significant financial losses and loss of experiment runtime, making dependability one of their most important properties. However, even if the software for RH control systems has been developed using best practices, the system might still fail due to undetected faults (bugs), hardware failures, etc. Critical systems therefore need capability to tolerate faults and resume operation after their occurrence. However, design of effective fault detection and recovery mechanisms poses a challenge due to timeliness requirements, growth in scale, and complex interactions. In this paper we evaluate effectiveness of service-oriented architectural approach to fault tolerance in mission-critical real-time systems. We use a prototype implementation for service management with an experimental RH control system and industrial manipulator. The fault tolerance is based on using the high level of decoupling between services to recover from transient faults by service restarts. In case the recovery process is not successful, the system can still be used if the fault was not in a critical software module.

  6. Magnesium Hall Thruster

    Science.gov (United States)

    Szabo, James J.

    2015-01-01

    This Phase II project is developing a magnesium (Mg) Hall effect thruster system that would open the door for in situ resource utilization (ISRU)-based solar system exploration. Magnesium is light and easy to ionize. For a Mars- Earth transfer, the propellant mass savings with respect to a xenon Hall effect thruster (HET) system are enormous. Magnesium also can be combusted in a rocket with carbon dioxide (CO2) or water (H2O), enabling a multimode propulsion system with propellant sharing and ISRU. In the near term, CO2 and H2O would be collected in situ on Mars or the moon. In the far term, Mg itself would be collected from Martian and lunar regolith. In Phase I, an integrated, medium-power (1- to 3-kW) Mg HET system was developed and tested. Controlled, steady operation at constant voltage and power was demonstrated. Preliminary measurements indicate a specific impulse (Isp) greater than 4,000 s was achieved at a discharge potential of 400 V. The feasibility of delivering fluidized Mg powder to a medium- or high-power thruster also was demonstrated. Phase II of the project evaluated the performance of an integrated, highpower Mg Hall thruster system in a relevant space environment. Researchers improved the medium power thruster system and characterized it in detail. Researchers also designed and built a high-power (8- to 20-kW) Mg HET. A fluidized powder feed system supporting the high-power thruster was built and delivered to Busek Company, Inc.

  7. Fault Detection for Wireless Networked Control Systems with Stochastic Switching Topology and Time Delay

    Directory of Open Access Journals (Sweden)

    Pengfei Guo

    2014-01-01

    Full Text Available This paper deals with the fault detection problem for a class of discrete-time wireless networked control systems described by switching topology with uncertainties and disturbances. System states of each individual node are affected not only by its own measurements, but also by other nodes’ measurements according to a certain network topology. As the topology of system can be switched in a stochastic way, we aim to design H∞ fault detection observers for nodes in the dynamic time-delay systems. By using the Lyapunov method and stochastic analysis techniques, sufficient conditions are acquired to guarantee the existence of the filters satisfying the H∞ performance constraint, and observer gains are derived by solving linear matrix inequalities. Finally, an illustrated example is provided to verify the effectiveness of the theoretical results.

  8. Consideration of Gyroscopic Effect in Fault Detection and Isolation for Unbalance Excited Rotor Systems

    Directory of Open Access Journals (Sweden)

    Zhentao Wang

    2012-01-01

    Full Text Available Fault detection and isolation (FDI in rotor systems often faces the problem that the system dynamics is dependent on the rotor rotary frequency because of the gyroscopic effect. In unbalance excited rotor systems, the continuously distributed unbalances are hard to be determined or estimated accurately. The unbalance forces as disturbances make fault detection more complicated. The aim of this paper is to develop linear time invariant (LTI FDI methods (i.e., with constant parameters for rotor systems under consideration of gyroscopic effect and disturbances. Two approaches to describe the gyroscopic effect, that is, as unknown inputs and as model uncertainties, are investigated. Based on these two approaches, FDI methods are developed and the results are compared regarding the resulting FDI performances. Results are obtained by the application in a rotor test rig. Restrictions for the application of these methods are discussed.

  9. Development and performance analysis of model-based fault detection and diagnosis algorithm

    International Nuclear Information System (INIS)

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

    2002-05-01

    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 pressurized water reactor 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 based on the interacting multiple model (IMM) algorithm. The other is input-output model-based FDD algorithm based on the ART neural network. Extensive computer simulations are carried out to evaluate the performance in terms of speed and accuracy

  10. Eigenvector/eigenvalue analysis of a 3D current referential fault detection and diagnosis of an induction motor

    International Nuclear Information System (INIS)

    Pires, V. Fernao; Martins, J.F.; Pires, A.J.

    2010-01-01

    In this paper an integrated approach for on-line induction motor fault detection and diagnosis is presented. The need to insure a continuous and safety operation for induction motors involves preventive maintenance procedures combined with fault diagnosis techniques. The proposed approach uses an automatic three step algorithm. Firstly, the induction motor stator currents are measured which will give typical patterns that can be used to identify the fault. Secondly, the eigenvectors/eigenvalues of the 3D current referential are computed. Finally the proposed algorithm will discern if the motor is healthy or not and report the extent of the fault. Furthermore this algorithm is able to identify distinct faults (stator winding faults or broken bars). The proposed approach was experimentally implemented and its performance verified on various types of working conditions.

  11. 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...

  12. Intelligent alarms detection for the analysis of system fault impact on business

    OpenAIRE

    Pace, C.; Russo, I.; Fernández, V.; Britos, Paola Verónica; Rossi, Bibiana D.; García Martínez, Ramón

    1998-01-01

    The tools for fault impact analysis are important for the deployment of critical mission systems. These tools can be also used as a development phase aid. We introduce several concepts related to "business alarms". Business alarms are an approximation to the company's business conceptual scheme driven by the business rules from systems conceptual schemes. In order to specify them we propose the utilization of Knowledge Engineering typical techniques. The object of alarm detection for impa...

  13. Local Interaction Simulation Approach for Fault Detection in Medical Ultrasonic Transducers

    Directory of Open Access Journals (Sweden)

    Z. Hashemiyan

    2015-01-01

    Full Text Available A new approach is proposed for modelling medical ultrasonic transducers operating in air. The method is based on finite elements and the local interaction simulation approach. The latter leads to significant reductions of computational costs. Transmission and reception properties of the transducer are analysed using in-air reverberation patterns. The proposed approach can help to provide earlier detection of transducer faults and their identification, reducing the risk of misdiagnosis due to poor image quality.

  14. Basic methods for automated fault detection and energy data validation in existing district heating systems

    OpenAIRE

    Sandin, Fredrik; Gustafsson, Jonas; Delsing, Jerker; Eklund, Robert

    2012-01-01

    Fault detection and diagnostics (FDD) of district heating substations (DHS) are important activities because malfunctioning components can lead to incorrect billing and waste of energy. Although FDD has been an activate research area for nearly two decades, only a few simple tools are commonly deployed in the district energy industry. Some of the methods proposed in the literature are promising, but their complexity may prevent broader application. Other methods require sensor data that are n...

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

    OpenAIRE

    Li, Yun; He, Guang; Li, Jie

    2013-01-01

    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 appropria...

  16. Fault Detection for Vibration Signals on Rolling Bearings Based on the Symplectic Entropy Method

    Directory of Open Access Journals (Sweden)

    Min Lei

    2017-11-01

    Full Text Available Bearing vibration response studies are crucial for the condition monitoring of bearings and the quality inspection of rotating machinery systems. However, it is still very difficult to diagnose bearing faults, especially rolling element faults, due to the complex, high-dimensional and nonlinear characteristics of vibration signals as well as the strong background noise. A novel nonlinear analysis method—the symplectic entropy (SymEn measure—is proposed to analyze the measured signals for fault monitoring of rolling bearings. The core technique of the SymEn approach is the entropy analysis based on the symplectic principal components. The dynamical characteristics of the rolling bearing data are analyzed using the SymEn method. Unlike other techniques consisting of high-dimensional features in the time-domain, frequency-domain and the empirical mode decomposition (EMD/wavelet-domain, the SymEn approach constructs low-dimensional (i.e., two-dimensional features based on the SymEn estimate. The vibration signals from our experiments and the Case Western Reserve University Bearing Data Center are applied to verify the effectiveness of the proposed method. Meanwhile, it is found that faulty bearings have a great influence on the other normal bearings. To sum up, the results indicate that the proposed method can be used to detect rolling bearing faults.

  17. Event-Triggered Fault Detection Filter Design for a Continuous-Time Networked Control System.

    Science.gov (United States)

    Wang, Yu-Long; Shi, Peng; Lim, Cheng-Chew; Liu, Yuan

    2016-12-01

    This paper studies the problem of event-triggered fault detection filter (FDF) and controller coordinated design for a continuous-time networked control system (NCS) with biased sensor faults. By considering sensor-to-FDF network-induced delays and packet dropouts, which do not impose a constraint on the event-triggering mechanism, and proposing the simultaneous network bandwidth utilization ratio and fault occurrence probability-based event-triggering mechanism, a new closed-loop model for the considered NCS is established. Based on the established model, the event-triggered H ∞ performance analysis, and FDF and controller coordinated design are presented. The combined mutually exclusive distribution and Wirtinger-based integral inequality approach is proposed for the first time to deal with integral inequalities for products of vectors. This approach is proved to be less conservative than the existing Wirtinger-based integral inequality approach. The designed FDF and controller can guarantee the sensitivity of the residual signal to faults and the robustness of the NCS to external disturbances. The simulation results verify the effectiveness of the proposed event-triggering mechanism, and the FDF and controller coordinated design.

  18. VIBRATION ANALYSIS FOR DETECTION AND LOCALIZATION THE FAULTS OF ROTATING MACHINERY USING WAVELET TECHINIQUES

    Directory of Open Access Journals (Sweden)

    MIHAIL PRICOP

    2016-06-01

    Full Text Available Vulnerable and critical mechanical systems are bearings and drive belts. Signal analysis of vibration highlights the changes in root mean square, the frequency spectrum (frequencies and amplitudes in the time- frequency (Short Time Fourier Transform and Wavelet Transform, are the most used method for faults diagnosis and location of rotating machinery. This article presents the results of an experimental study applied on a di agnostic platform of rotating machinery through three Wavelet methods: (Discrete Wavelet Transform -DWT, Continuous Wavelet Transform -CWT, Wavelet Packet Transform -WPT with different mother wavelet. Wavelet Transform is used to decompose the original sig nal into sub -frequency band signals in order to obtain multiple data series at different resolutions and to identify faults appearing in the complex rotation systems. This paper investigates the use of different mother wavelet functions for drive belts and bearing fault diagnosis. The results demonstrate the possibility of using different mother wavelets in rotary systems diagnosis detecting and locating in this way the faults in bearings and drive belts.

  19. An imbalance fault detection method based on data normalization and EMD for marine current turbines.

    Science.gov (United States)

    Zhang, Milu; Wang, Tianzhen; Tang, Tianhao; Benbouzid, Mohamed; Diallo, Demba

    2017-05-01

    This paper proposes an imbalance fault detection method based on data normalization and Empirical Mode Decomposition (EMD) for variable speed direct-drive Marine Current Turbine (MCT) system. The method is based on the MCT stator current under the condition of wave and turbulence. The goal of this method is to extract blade imbalance fault feature, which is concealed by the supply frequency and the environment noise. First, a Generalized Likelihood Ratio Test (GLRT) detector is developed and the monitoring variable is selected by analyzing the relationship between the variables. Then, the selected monitoring variable is converted into a time series through data normalization, which makes the imbalance fault characteristic frequency into a constant. At the end, the monitoring variable is filtered out by EMD method to eliminate the effect of turbulence. The experiments show that the proposed method is robust against turbulence through comparing the different fault severities and the different turbulence intensities. Comparison with other methods, the experimental results indicate the feasibility and efficacy of the proposed method. Copyright © 2017 ISA. Published by Elsevier Ltd. All rights reserved.

  20. AF-DHNN: Fuzzy Clustering and Inference-Based Node Fault Diagnosis Method for Fire Detection.

    Science.gov (United States)

    Jin, Shan; Cui, Wen; Jin, Zhigang; Wang, Ying

    2015-07-17

    Wireless Sensor Networks (WSNs) have been utilized for node fault diagnosis in the fire detection field since the 1990s. However, the traditional methods have some problems, including complicated system structures, intensive computation needs, unsteady data detection and local minimum values. In this paper, a new diagnosis mechanism for WSN nodes is proposed, which is based on fuzzy theory and an Adaptive Fuzzy Discrete Hopfield Neural Network (AF-DHNN). First, the original status of each sensor over time is obtained with two features. One is the root mean square of the filtered signal (FRMS), the other is the normalized summation of the positive amplitudes of the difference spectrum between the measured signal and the healthy one (NSDS). Secondly, distributed fuzzy inference is introduced. The evident abnormal nodes' status is pre-alarmed to save time. Thirdly, according to the dimensions of the diagnostic data, an adaptive diagnostic status system is established with a Fuzzy C-Means Algorithm (FCMA) and Sorting and Classification Algorithm to reducing the complexity of the fault determination. Fourthly, a Discrete Hopfield Neural Network (DHNN) with iterations is improved with the optimization of the sensors' detected status information and standard diagnostic levels, with which the associative memory is achieved, and the search efficiency is improved. The experimental results show that the AF-DHNN method can diagnose abnormal WSN node faults promptly and effectively, which improves the WSN reliability.

  1. 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.

  2. Fault Diagnosis of Internal Combustion Engine Valve Clearance Using the Impact Commencement Detection Method

    Science.gov (United States)

    Jiang, Zhinong; Wang, Zijia; Zhang, Jinjie

    2017-01-01

    Internal combustion engines (ICEs) are widely used in many important fields. The valve train clearance of an ICE usually exceeds the normal value due to wear or faulty adjustment. This work aims at diagnosing the valve clearance fault based on the vibration signals measured on the engine cylinder heads. The non-stationarity of the ICE operating condition makes it difficult to obtain the nominal baseline, which is always an awkward problem for fault diagnosis. This paper overcomes the problem by inspecting the timing of valve closing impacts, of which the referenced baseline can be obtained by referencing design parameters rather than extraction during healthy conditions. To accurately detect the timing of valve closing impact from vibration signals, we carry out a new method to detect and extract the commencement of the impacts. The results of experiments conducted on a twelve-cylinder ICE test rig show that the approach is capable of extracting the commencement of valve closing impact accurately and using only one feature can give a superior monitoring of valve clearance. With the help of this technique, the valve clearance fault becomes detectable even without the comparison to the baseline, and the changing trend of the clearance could be trackable. PMID:29244722

  3. Fault Diagnosis of Internal Combustion Engine Valve Clearance Using the Impact Commencement Detection Method.

    Science.gov (United States)

    Jiang, Zhinong; Mao, Zhiwei; Wang, Zijia; Zhang, Jinjie

    2017-12-15

    Internal combustion engines (ICEs) are widely used in many important fields. The valve train clearance of an ICE usually exceeds the normal value due to wear or faulty adjustment. This work aims at diagnosing the valve clearance fault based on the vibration signals measured on the engine cylinder heads. The non-stationarity of the ICE operating condition makes it difficult to obtain the nominal baseline, which is always an awkward problem for fault diagnosis. This paper overcomes the problem by inspecting the timing of valve closing impacts, of which the referenced baseline can be obtained by referencing design parameters rather than extraction during healthy conditions. To accurately detect the timing of valve closing impact from vibration signals, we carry out a new method to detect and extract the commencement of the impacts. The results of experiments conducted on a twelve-cylinder ICE test rig show that the approach is capable of extracting the commencement of valve closing impact accurately and using only one feature can give a superior monitoring of valve clearance. With the help of this technique, the valve clearance fault becomes detectable even without the comparison to the baseline, and the changing trend of the clearance could be trackable.

  4. Enhanced dynamic data-driven fault detection approach: Application to a two-tank heater system

    KAUST Repository

    Harrou, Fouzi

    2018-02-12

    Principal components analysis (PCA) has been intensively studied and used in monitoring industrial systems. However, data generated from chemical processes are usually correlated in time due to process dynamics, which makes the fault detection based on PCA approach a challenging task. Accounting for the dynamic nature of data can also reflect the performance of the designed fault detection approaches. In PCA-based methods, this dynamic characteristic of the data can be accounted for by using dynamic PCA (DPCA), in which lagged variables are used in the PCA model to capture the time evolution of the process. This paper presents a new approach that combines the DPCA to account for autocorrelation in data and generalized likelihood ratio (GLR) test to detect faults. A DPCA model is applied to perform dimension reduction while appropriately considering the temporal relationships in the data. Specifically, the proposed approach uses the DPCA to generate residuals, and then apply GLR test to reveal any abnormality. The performances of the proposed method are evaluated through a continuous stirred tank heater system.

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

    Science.gov (United States)

    Sun, Hailiang; Zi, Yanyang; He, Zhengjia; Yuan, Jing; Wang, Xiaodong; Chen, Lue

    2013-01-01

    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. PMID:23334609

  6. Cassini Thruster Calibration Algorithm Using Reaction Wheel Biasing Data

    Science.gov (United States)

    Rizvi, Farheen

    2012-01-01

    Thrust force estimates for the reaction control thrusters on-board Cassini spacecraft are presented in this paper. Cassini consists of two thruster branches (A and B) each with eight thrusters. The four Z-thrusters control the X and Y-axes, while the four Y-thrusters control the Z-axis. It is important to track the thrust force estimates in order to detect any thruster degradation and for supporting various activities in spacecraft operations (Titan flyby, spacecraft maneuvers). The Euler equation, which describes the rotational motion of the spacecraft during a reaction wheel bias event, is used to develop the algorithm. The thrust estimates are obtained from the pseudo inverse solution using flight telemetry during the bias. Results show that the A-branch Z3A and Z4A thrusters exhibited degraded thrust in November 2008. Due to the degraded thrust performance of Z3A and Z4A, A-branch usage was discontinued and prime branch was swapped to B-branch in March 2009. The thrust estimates from the B-branch do not show any degradation to date. The algorithm is used to trend the B-branch thrust force estimates as the mission continues.

  7. Integrated ensemble noise-reconstructed empirical mode decomposition for mechanical fault detection

    Science.gov (United States)

    Yuan, Jing; Ji, Feng; Gao, Yuan; Zhu, Jun; Wei, Chenjun; Zhou, Yu

    2018-05-01

    A new branch of fault detection is utilizing the noise such as enhancing, adding or estimating the noise so as to improve the signal-to-noise ratio (SNR) and extract the fault signatures. Hereinto, ensemble noise-reconstructed empirical mode decomposition (ENEMD) is a novel noise utilization method to ameliorate the mode mixing and denoised the intrinsic mode functions (IMFs). Despite the possibility of superior performance in detecting weak and multiple faults, the method still suffers from the major problems of the user-defined parameter and the powerless capability for a high SNR case. Hence, integrated ensemble noise-reconstructed empirical mode decomposition is proposed to overcome the drawbacks, improved by two noise estimation techniques for different SNRs as well as the noise estimation strategy. Independent from the artificial setup, the noise estimation by the minimax thresholding is improved for a low SNR case, which especially shows an outstanding interpretation for signature enhancement. For approximating the weak noise precisely, the noise estimation by the local reconfiguration using singular value decomposition (SVD) is proposed for a high SNR case, which is particularly powerful for reducing the mode mixing. Thereinto, the sliding window for projecting the phase space is optimally designed by the correlation minimization. Meanwhile, the reasonable singular order for the local reconfiguration to estimate the noise is determined by the inflection point of the increment trend of normalized singular entropy. Furthermore, the noise estimation strategy, i.e. the selection approaches of the two estimation techniques along with the critical case, is developed and discussed for different SNRs by means of the possible noise-only IMF family. The method is validated by the repeatable simulations to demonstrate the synthetical performance and especially confirm the capability of noise estimation. Finally, the method is applied to detect the local wear fault

  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. Real Time Supervisors and Monitors for Performing Health Monitoring and Fault Detection for Systems Operating in Multiple Regimes

    National Research Council Canada - National Science Library

    Jaw, Link

    2003-01-01

    In this Phase I STTR, SMI and ARL have developed a Real Time Supervisor for fault detection and system reconfiguration in a team of micro UAVs, that are tasked to perform a team mission like surveillance or rendezvous...

  10. Development of Fault Detection and Diagnosis Schemes for Industrial Refrigeration Systems

    DEFF Research Database (Denmark)

    Thybo, C.; Izadi-Zamanabadi, Roozbeh

    2004-01-01

    The success of a fault detection and diagnosis (FDD) scheme depends not alone on developing an advanced detection scheme. To enable successful deployment in industrial applications, an economically optimal development of FDD schemes are required. This paper reviews and discusses the gained...... experiences achieved by employing a combination of various techniques, methods, and algorithms, which are proposed by academia, on an industrial application. The main focus is on sharing the "lessons learned" from developing and employing Faulttolerant functionalities to a controlled process in order to meet...... the industrial needs while satisfying economically motivated constraints....

  11. Fault Analysis of Space Station DC Power Systems-Using Neural Network Adaptive Wavelets to Detect Faults

    Science.gov (United States)

    Momoh, James A.; Wang, Yanchun; Dolce, James L.

    1997-01-01

    This paper describes the application of neural network adaptive wavelets for fault diagnosis of space station power system. The method combines wavelet transform with neural network by incorporating daughter wavelets into weights. Therefore, the wavelet transform and neural network training procedure become one stage, which avoids the complex computation of wavelet parameters and makes the procedure more straightforward. The simulation results show that the proposed method is very efficient for the identification of fault locations.

  12. Bond Graph Modelling for Fault Detection and Isolation of an Ultrasonic Linear Motor

    Directory of Open Access Journals (Sweden)

    Mabrouk KHEMLICHE

    2010-12-01

    Full Text Available In this paper Bond Graph modeling, simulation and monitoring of ultrasonic linear motors are presented. Only the vibration of piezoelectric ceramics and stator will be taken into account. Contact problems between stator and rotor are not treated here. So, standing and travelling waves will be briefly presented since the majority of the motors use another wave type to generate the stator vibration and thus obtain the elliptic trajectory of the points on the surface of the stator in the first time. Then, electric equivalent circuit will be presented with the aim for giving a general idea of another way of graphical modelling of the vibrator introduced and developed. The simulations of an ultrasonic linear motor are then performed and experimental results on a prototype built at the laboratory are presented. Finally, validation of the Bond Graph method for modelling is carried out, comparing both simulation and experiment results. This paper describes the application of the FDI approach to an electrical system. We demonstrate the FDI effectiveness with real data collected from our automotive test. We introduce the analysis of the problem involved in the faults localization in this process. We propose a method of fault detection applied to the diagnosis and to determine the gravity of a detected fault. We show the possibilities of application of the new approaches to the complex system control.

  13. Automated fault detection and classification of etch systems using modular neural networks

    Science.gov (United States)

    Hong, Sang J.; May, Gary S.; Yamartino, John; Skumanich, Andrew

    2004-04-01

    Modular neural networks (MNNs) are investigated as a tool for modeling process behavior and fault detection and classification (FDC) using tool data in plasma etching. Principal component analysis (PCA) is initially employed to reduce the dimensionality of the voluminous multivariate tool data and to establish relationships between the acquired data and the process state. MNNs are subsequently used to identify anomalous process behavior. A gradient-based fuzzy C-means clustering algorithm is implemented to enhance MNN performance. MNNs for eleven individual steps of etch runs are trained with data acquired from baseline, control (acceptable), and perturbed (unacceptable) runs, and then tested with data not used for training. In the fault identification phase, a 0% of false alarm rate for the control runs is achieved.

  14. Direct detection of near-surface faults by migration of back-scattered surface waves

    KAUST Repository

    Yu, Han

    2014-08-05

    We show that diffraction stack migration can be used to estimate the distribution of near-surface faults. The assumption is that near-surface faults generate detectable back-scattered surface waves from impinging surface waves. The processing steps are to isolate the back-scattered surface waves, and then migrate them by diffraction migration using the surface wave velocity as the migration velocity. Instead of summing events along trial quasi-hyperbolas, surface wave migration sums events along trial quasi-linear trajectories that correspond to the moveout of back-scattered surface waves. A deconvolution filter derived from the data can be used to collapse a dispersive arrival into a non-dispersive event. Results with synthetic data and field records validate the feasibility of this method. Applying this method to USArray data or passively recorded exploration data might open new opportunities in mapping tectonic features over the extent of the array.

  15. Bearing fault detection using motor current signal analysis based on wavelet packet decomposition and Hilbert envelope

    Directory of Open Access Journals (Sweden)

    Imaouchen Yacine

    2015-01-01

    Full Text Available To detect rolling element bearing defects, many researches have been focused on Motor Current Signal Analysis (MCSA using spectral analysis and wavelet transform. This paper presents a new approach for rolling element bearings diagnosis without slip estimation, based on the wavelet packet decomposition (WPD and the Hilbert transform. Specifically, the Hilbert transform first extracts the envelope of the motor current signal, which contains bearings fault-related frequency information. Subsequently, the envelope signal is adaptively decomposed into a number of frequency bands by the WPD algorithm. Two criteria based on the energy and correlation analyses have been investigated to automate the frequency band selection. Experimental studies have confirmed that the proposed approach is effective in diagnosing rolling element bearing faults for improved induction motor condition monitoring and damage assessment.

  16. Model-Based Water Wall Fault Detection and Diagnosis of FBC Boiler Using Strong Tracking Filter

    Directory of Open Access Journals (Sweden)

    Li Sun

    2014-01-01

    Full Text Available Fluidized bed combustion (FBC boilers have received increasing attention in recent decades. The erosion issue on the water wall is one of the most common and serious faults for FBC boilers. Unlike direct measurement of tube thickness used by ultrasonic methods, the wastage of water wall is reconsidered equally as the variation of the overall heat transfer coefficient in the furnace. In this paper, a model-based approach is presented to estimate internal states and heat transfer coefficient dually from the noisy measurable outputs. The estimated parameter is compared with the normal value. Then the modified Bayesian algorithm is adopted for fault detection and diagnosis (FDD. The simulation results demonstrate that the approach is feasible and effective.

  17. 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.

  18. 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.

  19. Analysis of Space Shuttle Ground Support System Fault Detection, Isolation, and Recovery Processes and Resources

    Science.gov (United States)

    Gross, Anthony R.; Gerald-Yamasaki, Michael; Trent, Robert P.

    2009-01-01

    As part of the FDIR (Fault Detection, Isolation, and Recovery) Project for the Constellation Program, a task was designed within the context of the Constellation Program FDIR project called the Legacy Benchmarking Task to document as accurately as possible the FDIR processes and resources that were used by the Space Shuttle ground support equipment (GSE) during the Shuttle flight program. These results served as a comparison with results obtained from the new FDIR capability. The task team assessed Shuttle and EELV (Evolved Expendable Launch Vehicle) historical data for GSE-related launch delays to identify expected benefits and impact. This analysis included a study of complex fault isolation situations that required a lengthy troubleshooting process. Specifically, four elements of that system were considered: LH2 (liquid hydrogen), LO2 (liquid oxygen), hydraulic test, and ground special power.

  20. Adaptive Fault Detection for Complex Dynamic Processes Based on JIT Updated Data Set

    Directory of Open Access Journals (Sweden)

    Jinna Li

    2012-01-01

    Full Text Available A novel fault detection technique is proposed to explicitly account for the nonlinear, dynamic, and multimodal problems existed in the practical and complex dynamic processes. Just-in-time (JIT detection method and k-nearest neighbor (KNN rule-based statistical process control (SPC approach are integrated to construct a flexible and adaptive detection scheme for the control process with nonlinear, dynamic, and multimodal cases. Mahalanobis distance, representing the correlation among samples, is used to simplify and update the raw data set, which is the first merit in this paper. Based on it, the control limit is computed in terms of both KNN rule and SPC method, such that we can identify whether the current data is normal or not by online approach. Noted that the control limit obtained changes with updating database such that an adaptive fault detection technique that can effectively eliminate the impact of data drift and shift on the performance of detection process is obtained, which is the second merit in this paper. The efficiency of the developed method is demonstrated by the numerical examples and an industrial case.

  1. Fault detection, isolation, and diagnosis of status self-validating gas sensor arrays.

    Science.gov (United States)

    Chen, Yin-Sheng; Xu, Yong-Hui; Yang, Jing-Li; Shi, Zhen; Jiang, Shou-da; Wang, Qi

    2016-04-01

    The traditional gas sensor array has been viewed as a simple apparatus for information acquisition in chemosensory systems. Gas sensor arrays frequently undergo impairments in the form of sensor failures that cause significant deterioration of the performance of previously trained pattern recognition models. Reliability monitoring of gas sensor arrays is a challenging and critical issue in the chemosensory system. Because of its importance, we design and implement a status self-validating gas sensor array prototype to enhance the reliability of its measurements. A novel fault detection, isolation, and diagnosis (FDID) strategy is presented in this paper. The principal component analysis-based multivariate statistical process monitoring model can effectively perform fault detection by using the squared prediction error statistic and can locate the faulty sensor in the gas sensor array by using the variables contribution plot. The signal features of gas sensor arrays for different fault modes are extracted by using ensemble empirical mode decomposition (EEMD) coupled with sample entropy (SampEn). The EEMD is applied to adaptively decompose the original gas sensor signals into a finite number of intrinsic mode functions (IMFs) and a residual. The SampEn values of each IMF and the residual are calculated to reveal the multi-scale intrinsic characteristics of the faulty sensor signals. Sparse representation-based classification is introduced to identify the sensor fault type for the purpose of diagnosing deterioration in the gas sensor array. The performance of the proposed strategy is compared with other different diagnostic approaches, and it is fully evaluated in a real status self-validating gas sensor array experimental system. The experimental results demonstrate that the proposed strategy provides an excellent solution to the FDID of status self-validating gas sensor arrays.

  2. An autonomous fault detection, isolation, and recovery system for a 20-kHz electric power distribution test bed

    Science.gov (United States)

    Quinn, Todd M.; Walters, Jerry L.

    1991-01-01

    Future space explorations will require long term human presence in space. Space environments that provide working and living quarters for manned missions are becoming increasingly larger and more sophisticated. Monitor and control of the space environment subsystems by expert system software, which emulate human reasoning processes, could maintain the health of the subsystems and help reduce the human workload. The autonomous power expert (APEX) system was developed to emulate a human expert's reasoning processes used to diagnose fault conditions in the domain of space power distribution. APEX is a fault detection, isolation, and recovery (FDIR) system, capable of autonomous monitoring and control of the power distribution system. APEX consists of a knowledge base, a data base, an inference engine, and various support and interface software. APEX provides the user with an easy-to-use interactive interface. When a fault is detected, APEX will inform the user of the detection. The user can direct APEX to isolate the probable cause of the fault. Once a fault has been isolated, the user can ask APEX to justify its fault isolation and to recommend actions to correct the fault. APEX implementation and capabilities are discussed.

  3. HG ion thruster component testing

    Science.gov (United States)

    Mantenieks, M. A.

    1979-01-01

    Cathodes, isolators, and vaporizers are critical components in determining the performance and lifetime of mercury ion thrusters. The results of life tests of several of these components are reported. A 30-cm thruster CIV test in a bell jar has successfully accumulated over 26,000 hours. The cathode has undergone 65 restarts during the life test without requiring any appreciable increases in starting power. Recently, all restarts have been achieved with only the 44 volt keeper supply with no change required in the starting power. Another ongoing 30-cm Hg thruster cathode test has successfully passed the 10,000 hour mark. A solid-insert, 8-cm thruster cathode has accumulated over 4,000 hours of thruster operation. All starts have been achieved without the use of a high voltage ignitor. The results of this test indicate that the solid impregnated insert is a viable neutralizer cathode for the 8-cm thruster.

  4. 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.

  5. EEMD-based multiscale ICA method for slewing bearing fault detection and diagnosis

    Science.gov (United States)

    Žvokelj, Matej; Zupan, Samo; Prebil, Ivan

    2016-05-01

    A novel multivariate and multiscale statistical process monitoring method is proposed with the aim of detecting incipient failures in large slewing bearings, where subjective influence plays a minor role. The proposed method integrates the strengths of the Independent Component Analysis (ICA) multivariate monitoring approach with the benefits of Ensemble Empirical Mode Decomposition (EEMD), which adaptively decomposes signals into different time scales and can thus cope with multiscale system dynamics. The method, which was named EEMD-based multiscale ICA (EEMD-MSICA), not only enables bearing fault detection but also offers a mechanism of multivariate signal denoising and, in combination with the Envelope Analysis (EA), a diagnostic tool. The multiscale nature of the proposed approach makes the method convenient to cope with data which emanate from bearings in complex real-world rotating machinery and frequently represent the cumulative effect of many underlying phenomena occupying different regions in the time-frequency plane. The efficiency of the proposed method was tested on simulated as well as real vibration and Acoustic Emission (AE) signals obtained through conducting an accelerated run-to-failure lifetime experiment on a purpose-built laboratory slewing bearing test stand. The ability to detect and locate the early-stage rolling-sliding contact fatigue failure of the bearing indicates that AE and vibration signals carry sufficient information on the bearing condition and that the developed EEMD-MSICA method is able to effectively extract it, thereby representing a reliable bearing fault detection and diagnosis strategy.

  6. Bearing Fault Detection Using Multi-Scale Fractal Dimensions Based on Morphological Covers

    Directory of Open Access Journals (Sweden)

    Pei-Lin Zhang

    2012-01-01

    Full Text Available Vibration signals acquired from bearing have been found to demonstrate complicated nonlinear characteristics in literature. Fractal geometry theory has provided effective tools such as fractal dimension for characterizing the vibration signals in bearing faults detection. However, most of the natural signals are not critical self-similar fractals; the assumption of a constant fractal dimension at all scales may not be true. Motivated by this fact, this work explores the application of the multi-scale fractal dimensions (MFDs based on morphological cover (MC technique for bearing fault diagnosis. Vibration signals from bearing with seven different states under four operations conditions are collected to validate the presented MFDs based on MC technique. Experimental results reveal that the vibration signals acquired from bearing are not critical self-similar fractals. The MFDs can provide more discriminative information about the signals than the single global fractal dimension. Furthermore, three classifiers are employed to evaluate and compare the classification performance of the MFDs with other feature extraction methods. Experimental results demonstrate the MFDs to be a desirable approach to improve the performance of bearing fault diagnosis.

  7. 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.

  8. Application of the Goertzel’s algorithm in the airgap mixed eccentricity fault detection

    Directory of Open Access Journals (Sweden)

    Reljić Dejan

    2015-01-01

    Full Text Available In this paper, a suitable method for the on-line detection of the airgap mixed eccentricity fault in a three-phase cage induction motor has been proposed. The method is based on a Motor Current Signature Analysis (MCSA approach, a technique that is often used for an induction motor condition monitoring and fault diagnosis. It is based on the spectral analysis of the stator line current signal and the frequency identification of specific components, which are created as a result of motor faults. The most commonly used method for the current signal spectral analysis is based on the Fast Fourier transform (FFT. However, due to the complexity and memory demands, the FFT algorithm is not always suitable for real-time systems. Instead of the whole spectrum analysis, this paper suggests only the spectral analysis on the expected airgap fault frequencies employing the Goertzel’s algorithm to predict the magnitude of these frequency components. The method is simple and can be implemented in real-time airgap mixed eccentricity monitoring systems without much computational effort. A low-cost data acquisition system, supported by the LabView software, has been used for the hardware and software implementation of the proposed method. The method has been validated by the laboratory experiments on both the line-connected and the inverter-fed three-phase fourpole cage induction motor operated at the rated frequency and under constant load at a few different values. In addition, the results of the proposed method have been verified through the motor’s vibration signal analysis. [Projekat Ministarstva nauke Republike Srbije, br. III42004

  9. Measurement of Instantaneous Angular Displacement Fluctuation and its applications on gearbox fault detection.

    Science.gov (United States)

    Li, Bing; Zhang, Xining; Wu, Tingting

    2018-03-01

    Recently, Instantaneous Angular Speed (IAS) measurement is successfully established and prevalently applied to a wide variety of machines due to the hypothesis that the speed fluctuation of rotating machinery carries plentiful dynamic responses. Nevertheless, exploration and application based on angular signal are still insufficient. Under the same hypothesis, in this paper, we introduced an extended algorithm named Instantaneous Angular Phase Demodulation (IAPD), together with the selection of optimal sideband family to extract the Instantaneous Angular Displacement Fluctuation (IADF) signal. In order to evaluate the performance of IADF signal, an effective approach was demonstrated using IADF signal to address the fault detection and diagnosis issue. After extracting the IADF signal, a much effective method was developed to deal with the large amount of data generated during the signal collection process. Then, we used the well-developed techniques, i.e., empirical mode decomposition (EMD) and envelope analysis, to undertake the signal de-noising and feature extraction task. The effectiveness and capability of the IADF signal were evaluated by two kinds of gearboxes under differentconditions in practice. In particular, the prevalent IAS signal and vibration signal were also involved and testified by the proposed procedure. Experimental results demonstrated that by means of the IADF signal, the combination of EMD and envelope analysis not only provided accurate identification results with a higher signal-to-noise ratio, but was also capable of revealing the fault characteristics significantly and effectively. In contrast, although the IAS signal had the potential ability to diagnose the serious fault, it failed for the slight crack case. Moreover, the same procedure even its improvements, i.e., ensemble empirical mode decomposition and local mean decomposition, all failed to recognize the faults in terms of vibration signals. Copyright © 2018 ISA. Published by

  10. Implementation of fuzzy modeling system for faults detection and diagnosis in three phase induction motor drive system

    Directory of Open Access Journals (Sweden)

    Shorouk Ossama Ibrahim

    2015-05-01

    Full Text Available Induction motors have been intensively utilized in industrial applications, mainly due to their efficiency and reliability. It is necessary that these machines work all the time with its high performance and reliability. So it is necessary to monitor, detect and diagnose different faults that these motors are facing. In this paper an intelligent fault detection and diagnosis for different faults of induction motor drive system is introduced. The stator currents and the time are introduced as inputs to the proposed fuzzy detection and diagnosis system. The direct torque control technique (DTC is adopted as a suitable control technique in the drive system especially, in traction applications, such as Electric Vehicles and Sub-Way Metro that used such a machine. An intelligent modeling technique is adopted as an identifier for different faults; the proposed model introduces the time as an important factor or variable that plays an important role either in fault detection or in decision making for suitable corrective action according to the type of the fault. Experimental results have been obtained to verify the efficiency of the proposed intelligent detector and identifier; a matching between the simulated and experimental results has been noticed.

  11. Helical plasma thruster

    Energy Technology Data Exchange (ETDEWEB)

    Beklemishev, A. D., E-mail: bekl@bk.ru [Budker Institute of Nuclear Physics SB RAS, Novosibirsk (Russian Federation)

    2015-10-15

    A new scheme of plasma thruster is proposed. It is based on axial acceleration of rotating magnetized plasmas in magnetic field with helical corrugation. The idea is that the propellant ionization zone can be placed into the local magnetic well, so that initially the ions are trapped. The E × B rotation is provided by an applied radial electric field that makes the setup similar to a magnetron discharge. Then, from the rotating plasma viewpoint, the magnetic wells of the helically corrugated field look like axially moving mirror traps. Specific shaping of the corrugation can allow continuous acceleration of trapped plasma ions along the magnetic field by diamagnetic forces. The accelerated propellant is expelled through the expanding field of magnetic nozzle. By features of the acceleration principle, the helical plasma thruster may operate at high energy densities but requires a rather high axial magnetic field, which places it in the same class as the VASIMR{sup ®} rocket engine.

  12. Implementation of a Fractional Model-Based Fault Detection Algorithm into a PLC Controller

    Science.gov (United States)

    Kopka, Ryszard

    2014-12-01

    This paper presents results related to the implementation of model-based fault detection and diagnosis procedures into a typical PLC controller. To construct the mathematical model and to implement the PID regulator, a non-integer order differential/integral calculation was used. Such an approach allows for more exact control of the process and more precise modelling. This is very crucial in model-based diagnostic methods. The theoretical results were verified on a real object in the form of a supercapacitor connected to a PLC controller by a dedicated electronic circuit controlled directly from the PLC outputs.

  13. Survey of artificial intelligence methods for detection and identification of component faults in nuclear power plants

    International Nuclear Information System (INIS)

    Reifman, J.

    1997-01-01

    A comprehensive survey of computer-based systems that apply artificial intelligence methods to detect and identify component faults in nuclear power plants is presented. Classification criteria are established that categorize artificial intelligence diagnostic systems according to the types of computing approaches used (e.g., computing tools, computer languages, and shell and simulation programs), the types of methodologies employed (e.g., types of knowledge, reasoning and inference mechanisms, and diagnostic approach), and the scope of the system. The major issues of process diagnostics and computer-based diagnostic systems are identified and cross-correlated with the various categories used for classification. Ninety-five publications are reviewed

  14. Support vector machine based fault detection approach for RFT-30 cyclotron

    Energy Technology Data Exchange (ETDEWEB)

    Kong, Young Bae, E-mail: ybkong@kaeri.re.kr; Lee, Eun Je; Hur, Min Goo; Park, Jeong Hoon; Park, Yong Dae; Yang, Seung Dae

    2016-10-21

    An RFT-30 is a 30 MeV cyclotron used for radioisotope applications and radiopharmaceutical researches. The RFT-30 cyclotron is highly complex and includes many signals for control and monitoring of the system. It is quite difficult to detect and monitor the system failure in real time. Moreover, continuous monitoring of the system is hard and time-consuming work for human operators. In this paper, we propose a support vector machine (SVM) based fault detection approach for the RFT-30 cyclotron. The proposed approach performs SVM learning with training samples to construct the classification model. To compensate the system complexity due to the large-scale accelerator, we utilize the principal component analysis (PCA) for transformation of the original data. After training procedure, the proposed approach detects the system faults in real time. We analyzed the performance of the proposed approach utilizing the experimental data of the RFT-30 cyclotron. The performance results show that the proposed SVM approach can provide an efficient way to control the cyclotron system.

  15. Detection of high-impedance fault in low-voltage DC distribution system via mathematical morphology

    Directory of Open Access Journals (Sweden)

    Yun-Sik Oh

    2016-01-01

    Full Text Available This study presents a method for high-impedance fault (HIF detection in a low-voltage DC (LVDC distribution system via mathematical morphology (MM, which is composed of two elementary transformations, namely, dilation and erosion. Various MM-based filters are used to detect abnormal signals of current waveform. The LVDC distribution system, including power conversion devices, such as AC/DC and DC/DC converters, is modelled with electromagnetic transient program (EMTP software to verify the proposed method. The HIF arc model in the DC system is also implemented with EMTP/MODELS, which is a symbolic language interpreter for EMTP. Simulation results show that the proposed method can be applied to detect HIF effectively in the LVDC distribution system.

  16. Model-Based Fault Detection and Isolation of a Liquid-Cooled Frequency Converter on a Wind Turbine

    DEFF Research Database (Denmark)

    Li, Peng; Odgaard, Peter Fogh; Stoustrup, Jakob

    2012-01-01

    With the rapid development of wind energy technologies and growth of installed wind turbine capacity in the world, the reliability of the wind turbine becomes an important issue for wind turbine manufactures, owners, and operators. The reliability of the wind turbine can be improved by implementing...... system is derived based on energy balance equation. A fault analysis is conducted to determine the severity and occurrence rate of possible component faults and their end effects in the cooling system. A method using unknown input observer is developed in order to detect and isolate the faults based...

  17. Fault detection and isolation of high temperature proton exchange membrane fuel cell stack under the influence of degradation

    DEFF Research Database (Denmark)

    Jeppesen, Christian; Araya, Samuel Simon; Sahlin, Simon Lennart

    2017-01-01

    This study proposes a data-drive impedance-based methodology for fault detection and isolation of low and high cathode stoichiometry, high CO concentration in the anode gas, high methanol vapour concentrations in the anode gas and low anode stoichiometry, for high temperature PEM fuel cells...... methanol vapour concentration in the anode gas fault, which was found to be difficult to distinguish from a normal operational data. The achieved accuracy for faults related to CO pollution, anode- and cathode stoichiometry is 100% success rate. Overall global accuracy on the test data is 94.6%....

  18. Comparative Study of Time-Frequency Decomposition Techniques for Fault Detection in Induction Motors Using Vibration Analysis during Startup Transient

    Directory of Open Access Journals (Sweden)

    Paulo Antonio Delgado-Arredondo

    2015-01-01

    Full Text Available Induction motors are critical components for most industries and the condition monitoring has become necessary to detect faults. There are several techniques for fault diagnosis of induction motors and analyzing the startup transient vibration signals is not as widely used as other techniques like motor current signature analysis. Vibration analysis gives a fault diagnosis focused on the location of spectral components associated with faults. Therefore, this paper presents a comparative study of different time-frequency analysis methodologies that can be used for detecting faults in induction motors analyzing vibration signals during the startup transient. The studied methodologies are the time-frequency distribution of Gabor (TFDG, the time-frequency Morlet scalogram (TFMS, multiple signal classification (MUSIC, and fast Fourier transform (FFT. The analyzed vibration signals are one broken rotor bar, two broken bars, unbalance, and bearing defects. The obtained results have shown the feasibility of detecting faults in induction motors using the time-frequency spectral analysis applied to vibration signals, and the proposed methodology is applicable when it does not have current signals and only has vibration signals. Also, the methodology has applications in motors that are not fed directly to the supply line, in such cases the analysis of current signals is not recommended due to poor current signal quality.

  19. 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

    Wireless Sensor Networks (WSNs) are more and more considered a key enabling technology for the realisation of the Internet of Things (IoT) vision. With the long term goal of designing fault-tolerant IoT systems, this paper proposes a fault detection framework for WSNs with the perspective of energy...... approaches for the comparison of several characteristics, namely, energy efficiency, correlation model, evaluation method, and detection accuracy. The design guidelines given in this paper aim at providing an insight into better design of energy-efficient detection approaches in resource-constraint WSNs....

  20. Fault-tolerant Control of Unmanned Underwater Vehicles with Continuous Faults: Simulations and Experiments

    Directory of Open Access Journals (Sweden)

    Qian Liu

    2009-12-01

    Full Text Available A novel thruster fault diagnosis and accommodation method for open-frame underwater vehicles is presented in the paper. The proposed system consists of two units: a fault diagnosis unit and a fault accommodation unit. In the fault diagnosis unit an ICMAC (Improved Credit Assignment Cerebellar Model Articulation Controllers neural network information fusion model is used to realize the fault identification of the thruster. The fault accommodation unit is based on direct calculations of moment and the result of fault identification is used to find the solution of the control allocation problem. The approach resolves the continuous faulty identification of the UV. Results from the experiment are provided to illustrate the performance of the proposed method in uncertain continuous faulty situation.

  1. Fault detection using transmission tomography - Evaluation on the Experimental Platform of Tournemire

    International Nuclear Information System (INIS)

    Vi-Nhu-Ba, Elise

    2014-01-01

    Deep argillaceous formations have physical properties adapted to the radioactive waste disposal but their permeability properties can be modified by the presence of fractured zones; detection of these faulted zones are thus of primary importance. Several experiments have been led by IRSN in the Experimental Platform of Tournemire where faults with small vertical offsets in the deep argillaceous formation have been identified from underground installations. Some previous studies have shown the difficulty to detect this fractured zone from surface acquisitions using reflection or refraction seismic but also with electrical methods. We here propose a new seismic transmission acquisition geometry in where seismic sources are deployed at the surface and receivers are installed in the underground installations. In the scope to process these data, a new tomography algorithm has been developed in order to control the inversion parameters and also to introduce a priori information. Several synthetic tests have been led to reliably analyze the results in terms of resolution and relevance of the final image. A discontinuity of the seismic velocities in the limestones and argillites of the Tournemire Platform is evidenced for the first time by applying the algorithm to the data recently acquired. This low velocity anomaly is located just above the fracture zone visible from the underground installations and its location is also consistent with observations from the surface. (author)

  2. Fault detection in rotating machines with beamforming: Spatial visualization of diagnosis features

    Science.gov (United States)

    Cardenas Cabada, E.; Leclere, Q.; Antoni, J.; Hamzaoui, N.

    2017-12-01

    Rotating machines diagnosis is conventionally related to vibration analysis. Sensors are usually placed on the machine to gather information about its components. The recorded signals are then processed through a fault detection algorithm allowing the identification of the failing part. This paper proposes an acoustic-based diagnosis method. A microphone array is used to record the acoustic field radiated by the machine. The main advantage over vibration-based diagnosis is that the contact between the sensors and the machine is no longer required. Moreover, the application of acoustic imaging makes possible the identification of the sources of acoustic radiation on the machine surface. The display of information is then spatially continuous while the accelerometers only give it discrete. Beamforming provides the time-varying signals radiated by the machine as a function of space. Any fault detection tool can be applied to the beamforming output. Spectral kurtosis, which highlights the impulsiveness of a signal as function of frequency, is used in this study. The combination of spectral kurtosis with acoustic imaging makes possible the mapping of the impulsiveness as a function of space and frequency. The efficiency of this approach lays on the source separation in the spatial and frequency domains. These mappings make possible the localization of such impulsive sources. The faulty components of the machine have an impulsive behavior and thus will be highlighted on the mappings. The study presents experimental validations of the method on rotating machines.

  3. Fault detection of flywheel system based on clustering and principal component analysis

    Directory of Open Access Journals (Sweden)

    Wang Rixin

    2015-12-01

    Full Text Available Considering the nonlinear, multifunctional properties of double-flywheel with closed-loop control, a two-step method including clustering and principal component analysis is proposed to detect the two faults in the multifunctional flywheels. At the first step of the proposed algorithm, clustering is taken as feature recognition to check the instructions of “integrated power and attitude control” system, such as attitude control, energy storage or energy discharge. These commands will ask the flywheel system to work in different operation modes. Therefore, the relationship of parameters in different operations can define the cluster structure of training data. Ordering points to identify the clustering structure (OPTICS can automatically identify these clusters by the reachability-plot. K-means algorithm can divide the training data into the corresponding operations according to the reachability-plot. Finally, the last step of proposed model is used to define the relationship of parameters in each operation through the principal component analysis (PCA method. Compared with the PCA model, the proposed approach is capable of identifying the new clusters and learning the new behavior of incoming data. The simulation results show that it can effectively detect the faults in the multifunctional flywheels system.

  4. Agent-based algorithm for fault detection and recovery of gyroscope's drift in small satellite missions

    Science.gov (United States)

    Carvajal-Godinez, Johan; Guo, Jian; Gill, Eberhard

    2017-10-01

    Failure detection, isolation, and recovery is an essential requirement of any space mission design. Several spacecraft components, especially sensors, are prone to performance deviation due to intrinsic physical effects. For that reason, innovative approaches for the treatment of faults in onboard sensors are necessary. This work introduces the concept of agent-based fault detection and recovery for sensors used in satellite attitude determination and control. Its focuses on the implementation of an algorithm for addressing linear drift bias in gyroscopes. The algorithm was implemented using an agent-based architecture that can be integrated into the satellite's onboard software. Numerical simulations were carried out to show the effectiveness of this scheme in satellite's operations. The proposed algorithm showed a reduction of up to 50% in the stabilization time for the detumbling maneuver, and also an improvement in the pointing accuracy of up to 20% when it was applied in precise payload pointing procedures. The relevance of this contribution is its added value for optimizing the launch and early operation of small satellite missions, as well as, an enabler for innovative satellite functions, for instance, optical downlink communication.

  5. Event-triggered fault detection for discrete-time T-S fuzzy systems.

    Science.gov (United States)

    Wang, Xiao-Lei; Yang, Guang-Hong

    2018-03-01

    This paper is concerned with the design of piecewise fuzzy diagnostic observers for discrete-time T-S fuzzy systems under an event-triggered (ET) communication mechanism. Considering that the premise variables of the fuzzy diagnostic observer and the system may belong to different local space regions due to the introduction of ET mechanism, a partition method-based piecewise fuzzy diagnostic observer is designed to detect faults. The two-term approximation approach is introduced to approximate the time-varying delay. By transforming the augmented system into an input-output form consisting of two interconnected subsystems, the design condition of the piecewise fuzzy diagnostic observer is obtained by using the scaled small gain (SSG) theorem and a piecewise Lyapunov-Krasovskii functional. Furthermore, the L ∞ /L 2 and L ∞ fault detection (FD) scheme is used to optimize the FD performance. Finally, two simulation examples are provided to show the efficiency of the proposed design method. Copyright © 2018 ISA. Published by Elsevier Ltd. All rights reserved.

  6. Application of the Continuous-Discrete Extended Kalman Filter for Fault Detection in Continuous Glucose Monitors for Type 1 Diabetes

    DEFF Research Database (Denmark)

    Mahmoudi, Zeinab; Boiroux, Dimitri; Hagdrup, Morten

    2016-01-01

    was modelled by a Gaussian random walk. We used a continuous-discrete extended Kalman filter for the fault detection, based on the statistical tests of the filter innovation and the 90-min prediction residuals of the sensor measurements. The spike detection had a sensitivity of 93% and a specificity of 100...

  7. Lessons Learned on Implementing Fault Detection, Isolation, and Recovery (FDIR) in a Ground Launch Environment

    Science.gov (United States)

    Ferrell, Bob A.; Lewis, Mark E.; Perotti, Jose M.; Brown, Barbara L.; Oostdyk, Rebecca L.; Goetz, Jesse W.

    2010-01-01

    This paper's main purpose is to detail issues and lessons learned regarding designing, integrating, and implementing Fault Detection Isolation and Recovery (FDIR) for Constellation Exploration Program (CxP) Ground Operations at Kennedy Space Center (KSC). Part of the0 overall implementation of National Aeronautics and Space Administration's (NASA's) CxP, FDIR is being implemented in three main components of the program (Ares, Orion, and Ground Operations/Processing). While not initially part of the design baseline for the CxP Ground Operations, NASA felt that FDIR is important enough to develop, that NASA's Exploration Systems Mission Directorate's (ESMD's) Exploration Technology Development Program (ETDP) initiated a task for it under their Integrated System Health Management (ISHM) research area. This task, referred to as the FDIIR project, is a multi-year multi-center effort. The primary purpose of the FDIR project is to develop a prototype and pathway upon which Fault Detection and Isolation (FDI) may be transitioned into the Ground Operations baseline. Currently, Qualtech Systems Inc (QSI) Commercial Off The Shelf (COTS) software products Testability Engineering and Maintenance System (TEAMS) Designer and TEAMS RDS/RT are being utilized in the implementation of FDI within the FDIR project. The TEAMS Designer COTS software product is being utilized to model the system with Functional Fault Models (FFMs). A limited set of systems in Ground Operations are being modeled by the FDIR project, and the entire Ares Launch Vehicle is being modeled under the Functional Fault Analysis (FFA) project at Marshall Space Flight Center (MSFC). Integration of the Ares FFMs and the Ground Processing FFMs is being done under the FDIR project also utilizing the TEAMS Designer COTS software product. One of the most significant challenges related to integration is to ensure that FFMs developed by different organizations can be integrated easily and without errors. Software Interface

  8. Early Oscillation Detection for Hybrid DC/DC Converter Fault Diagnosis

    Science.gov (United States)

    Wang, Bright L.

    2011-01-01

    This paper describes a novel fault detection technique for hybrid DC/DC converter oscillation diagnosis. The technique is based on principles of feedback control loop oscillation and RF signal modulations, and Is realized by using signal spectral analysis. Real-circuit simulation and analytical study reveal critical factors of the oscillation and indicate significant correlations between the spectral analysis method and the gain/phase margin method. A stability diagnosis index (SDI) is developed as a quantitative measure to accurately assign a degree of stability to the DC/DC converter. This technique Is capable of detecting oscillation at an early stage without interfering with DC/DC converter's normal operation and without limitations of probing to the converter.

  9. Influence of magnetic saturation effects on the fault detection of induction motors

    Directory of Open Access Journals (Sweden)

    Drozdowski Piotr

    2014-09-01

    Full Text Available In this paper, the influence of impact damage to the induction motors on the zero-sequence voltage and its spectrum is presented. The signals detecting the damages result from a detailed analysis of the formula describing this voltage component which is induced in the stator windings due to core magnetic saturation and the discrete displacement of windings. Its course is affected by the operation of both the stator and the rotor. Other fault detection methods, are known and widely applied by analysing the spectrum of stator currents. The presented method may be a complement to other methods because of the ease of measurements of the zero voltage for star connected motors. Additionally, for converter fed motors the zero sequence voltage eliminates higher time harmonics displaced by 120 degrees. The results of the method application are presented through measurements and explained by the use of a mathematical model of the slip-ring induction motor

  10. Short-Circuit Fault Detection and Classification Using Empirical Wavelet Transform and Local Energy for Electric Transmission Line.

    Science.gov (United States)

    Huang, Nantian; Qi, Jiajin; Li, Fuqing; Yang, Dongfeng; Cai, Guowei; Huang, Guilin; Zheng, Jian; Li, Zhenxin

    2017-09-16

    In order to improve the classification accuracy of recognizing short-circuit faults in electric transmission lines, a novel detection and diagnosis method based on empirical wavelet transform (EWT) and local energy (LE) is proposed. First, EWT is used to deal with the original short-circuit fault signals from photoelectric voltage transformers, before the amplitude modulated-frequency modulated (AM-FM) mode with a compactly supported Fourier spectrum is extracted. Subsequently, the fault occurrence time is detected according to the modulus maxima of intrinsic mode function (IMF₂) from three-phase voltage signals processed by EWT. After this process, the feature vectors are constructed by calculating the LE of the fundamental frequency based on the three-phase voltage signals of one period after the fault occurred. Finally, the classifier based on support vector machine (SVM) which was constructed with the LE feature vectors is used to classify 10 types of short-circuit fault signals. Compared with complementary ensemble empirical mode decomposition with adaptive noise (CEEMDAN) and improved CEEMDAN methods, the new method using EWT has a better ability to present the frequency in time. The difference in the characteristics of the energy distribution in the time domain between different types of short-circuit faults can be presented by the feature vectors of LE. Together, simulation and real signals experiment demonstrate the validity and effectiveness of the new approach.

  11. Open-Switch Fault Detection Method of a Back-to-Back Converter Using NPC Topology for Wind Turbine Systems

    DEFF Research Database (Denmark)

    Lee, June-Seok; Lee, Kyo_Beum; Blaabjerg, Frede

    2015-01-01

    In wind turbine generation (WTG) systems, a back-to-back converter with a neutral-point-clamped (NPC) topology is widely used because this topology has more advantages than a conventional two-level topology, particularly when operating at high power. There are 12 switches in the NPC topology....... An open-switch fault in the NPC rectifier of the back-to-back converter leads to the distortion of the input current and torque vibration in the system. Additionally, an open-switch fault in the NPC inverter of the back-to-back converter causes the distortion of the output current. Furthermore, the WTG...... system can break down in the worst case scenario. To improve the reliability of WTG systems, an open-switch fault detection method for back-to-back converters using the NPC topology is required. This study analyzes effects of inner and outer open-switch faults of the NPC rectifier and inverter...

  12. Online Fault Detection of Permanent Magnet Demagnetization for IPMSMs by Nonsingular Fast Terminal-Sliding-Mode Observer

    Directory of Open Access Journals (Sweden)

    Kai-Hui Zhao

    2014-12-01

    Full Text Available To prevent irreversible demagnetization of a permanent magnet (PM for interior permanent magnet synchronous motors (IPMSMs by flux-weakening control, a robust PM flux-linkage nonsingular fast terminal-sliding-mode observer (NFTSMO is proposed to detect demagnetization faults. First, the IPMSM mathematical model of demagnetization is presented. Second, the construction of the NFTSMO to estimate PM demagnetization faults in IPMSM is described, and a proof of observer stability is given. The fault decision criteria and fault-processing method are also presented. Finally, the proposed scheme was simulated using MATLAB/Simulink and implemented on the RT-LABplatform. A number of robustness tests have been carried out. The scheme shows good performance in spite of speed fluctuations, torque ripples and the uncertainties of stator resistance.

  13. Detection and localization of building insulation faults using optical-fiber DTS system

    Science.gov (United States)

    Papes, Martin; Liner, Andrej; Koudelka, Petr; Siska, Petr; Cubik, Jakub; Kepak, Stanislav; Jaros, Jakub; Vasinek, Vladimir

    2013-05-01

    Nowadays the trends in the construction industry are changing at an incredible speed. The new technologies are still emerging on the market. Sphere of building insulation is not an exception as well. One of the major problems in building insulation is usually its failure, whether caused by unwanted mechanical intervention or improper installation. The localization of these faults is quite difficult, often impossible without large intervention into the construction. As a proper solution for this problem might be utilization of Optical-Fiber DTS system based on stimulated Raman scattering. Used DTS system is primary designed for continuous measurement of the temperature along the optical fiber. This system is using standard optical fiber as a sensor, which brings several advantages in its application. First, the optical fiber is relatively inexpensive, which allows to cover a quite large area for a small cost. The other main advantages of the optical fiber are electromagnetic resistance, small size, safety operation in inflammable or explosive area, easy installation, etc. This article is dealing with the detection and localization of building insulation faults using mentioned system.

  14. Fault detection and isolation of high temperature proton exchange membrane fuel cell stack under the influence of degradation

    Science.gov (United States)

    Jeppesen, Christian; Araya, Samuel Simon; Sahlin, Simon Lennart; Thomas, Sobi; Andreasen, Søren Juhl; Kær, Søren Knudsen

    2017-08-01

    This study proposes a data-drive impedance-based methodology for fault detection and isolation of low and high cathode stoichiometry, high CO concentration in the anode gas, high methanol vapour concentrations in the anode gas and low anode stoichiometry, for high temperature PEM fuel cells. The fault detection and isolation algorithm is based on an artificial neural network classifier, which uses three extracted features as input. Two of the proposed features are based on angles in the impedance spectrum, and are therefore relative to specific points, and shown to be independent of degradation, contrary to other available feature extraction methods in the literature. The experimental data is based on a 35 day experiment, where 2010 unique electrochemical impedance spectroscopy measurements were recorded. The test of the algorithm resulted in a good detectability of the faults, except for high methanol vapour concentration in the anode gas fault, which was found to be difficult to distinguish from a normal operational data. The achieved accuracy for faults related to CO pollution, anode- and cathode stoichiometry is 100% success rate. Overall global accuracy on the test data is 94.6%.

  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. On the effect of test head sound fields on the ability to detect faults in plating

    International Nuclear Information System (INIS)

    Erhard, A.; Seidel, A.; Boehm, R.; Wuestenberg, H.; Bidois, H.

    1995-01-01

    Ferritic reactor pressure vessels and some ferritic pipelines are protected by Austentic plating. In connection with discussions on possible corrosive attack on this plating, the occurrence and growth of cracks, processes for testing this plating have come to the fore again. Apart from the qualification of eddy current processes, such as the pulse eddy current process for example, ultrasonic test techniques are also required. In order to be able to compare measured results from different platings with one another, running time rosettes wer measured. Characteristic sound field data can be determined from this. One tried to derive criteria for the detectability of groove-like faults from this information and to examine them for their applicability in practice. This evaluation of results was supported by statistical methods. (orig.) [de

  17. Detection of broken rotor bar faults in induction motor at low load using neural network.

    Science.gov (United States)

    Bessam, B; Menacer, A; Boumehraz, M; Cherif, H

    2016-09-01

    The knowledge of the broken rotor bars characteristic frequencies and amplitudes has a great importance for all related diagnostic methods. The monitoring of motor faults requires a high resolution spectrum to separate different frequency components. The Discrete Fourier Transform (DFT) has been widely used to achieve these requirements. However, at low slip this technique cannot give good results. As a solution for these problems, this paper proposes an efficient technique based on a neural network approach and Hilbert transform (HT) for broken rotor bar diagnosis in induction machines at low load. The Hilbert transform is used to extract the stator current envelope (SCE). Two features are selected from the (SCE) spectrum (the amplitude and frequency of the harmonic). These features will be used as input for neural network. The results obtained are astonishing and it is capable to detect the correct number of broken rotor bars under different load conditions. Copyright © 2016 ISA. Published by Elsevier Ltd. All rights reserved.

  18. Temperature Gradient in Hall Thrusters

    International Nuclear Information System (INIS)

    Staack, D.; Raitses, Y.; Fisch, N.J.

    2003-01-01

    Plasma potentials and electron temperatures were deduced from emissive and cold floating probe measurements in a 2 kW Hall thruster, operated in the discharge voltage range of 200-400 V. An almost linear dependence of the electron temperature on the plasma potential was observed in the acceleration region of the thruster both inside and outside the thruster. This result calls into question whether secondary electron emission from the ceramic channel walls plays a significant role in electron energy balance. The proportionality factor between the axial electron temperature gradient and the electric field is significantly smaller than might be expected by models employing Ohmic heating of electrons

  19. Estimation of fault geometry of a slow slip event off the Kii Peninsula, southwest of Japan, detected by DONET

    Science.gov (United States)

    Suzuki, K.; Nakano, M.; Hori, T.; Takahashi, N.

    2015-12-01

    The Japan Agency for Marine-Earth Science and Technology installed permanent ocean bottom observation network called Dense Oceanfloor Network System for Earthquakes and Tsunamis (DONET) off the Kii Peninsula, southwest of Japan, to monitor earthquakes and tsunamis. We detected the long-term vertical displacements of sea floor from the ocean-bottom pressure records, starting from March 2013, at several DONET stations (Suzuki et al., 2014). We consider that these displacements were caused by the crustal deformation due to a slow slip event (SSE).  We estimated the fault geometry of the SSE by using the observed ocean-bottom displacements. The ocean-bottom displacements were obtained by removing the tidal components from the pressure records. We also subtracted the average of pressure changes taken over the records at stations connected to each science node from each record in order to remove the contributions due to atmospheric pressure changes and non-tidal ocean dynamic mass variations. Therefore we compared observed displacements with the theoretical ones that was subtracted the average displacement in the fault geometry estimation. We also compared observed and theoretical average displacements for the model evaluation. In this study, the observed average displacements were assumed to be zero. Although there are nine parameters in the fault model, we observed vertical displacements at only four stations. Therefore we assumed three fault geometries; (1) a reverse fault slip along the plate boundary, (2) a strike slip along a splay fault, and (3) a reverse fault slip along the splay fault. We obtained that the model (3) gives the smallest residual between observed and calculated displacements. We also observed that this SSE was synchronized with a decrease in the background seismicity within the area of a nearby earthquake cluster. In the future, we will investigate the relationship between the SSE and the seismicity change.

  20. Detecting impact signal in mechanical fault diagnosis under chaotic and Gaussian background noise

    Science.gov (United States)

    Hu, Jinfeng; Duan, Jie; Chen, Zhuo; Li, Huiyong; Xie, Julan; Chen, Hanwen

    2018-01-01

    In actual fault diagnosis, useful information is often submerged in heavy noise, and the feature information is difficult to extract. Traditional methods, such like stochastic resonance (SR), which using noise to enhance weak signals instead of suppressing noise, failed in chaotic background. Neural network, which use reference sequence to estimate and reconstruct the background noise, failed in white Gaussian noise. To solve these problems, a novel weak signal detection method aimed at the problem of detecting impact signal buried under heavy chaotic and Gaussian background noise is proposed. First, the proposed method obtains the virtual reference sequence by constructing the Hankel data matrix. Then an M-order optimal FIR filter is designed, which can minimize the output power of background noise and pass the weak periodic signal undistorted. Finally, detection and reconstruction of the weak periodic signal are achieved from the output SBNR (signal to background noise ratio). The simulation shows, compared with the stochastic resonance (SR) method, the proposed method can detect the weak periodic signal in chaotic noise background while stochastic resonance (SR) method cannot. Compared with the neural network method, (a) the proposed method does not need a reference sequence while neural network method needs one; (b) the proposed method can detect the weak periodic signal in white Gaussian noise background while the neural network method fails, in chaotic noise background, the proposed method can detect the weak periodic signal under a lower SBNR (about 8-17 dB lower) than the neural network method; (c) the proposed method can reconstruct the weak periodic signal precisely.

  1. Fault detection and diagnosis of induction motors using motor current signature analysis and a hybrid FMM-CART model.

    Science.gov (United States)

    Seera, Manjeevan; Lim, Chee Peng; Ishak, Dahaman; Singh, Harapajan

    2012-01-01

    In this paper, a novel approach to detect and classify comprehensive fault conditions of induction motors using a hybrid fuzzy min-max (FMM) neural network and classification and regression tree (CART) is proposed. The hybrid model, known as FMM-CART, exploits the advantages of both FMM and CART for undertaking data classification and rule extraction problems. A series of real experiments is conducted, whereby the motor current signature analysis method is applied to form a database comprising stator current signatures under different motor conditions. The signal harmonics from the power spectral density are extracted as discriminative input features for fault detection and classification with FMM-CART. A comprehensive list of induction motor fault conditions, viz., broken rotor bars, unbalanced voltages, stator winding faults, and eccentricity problems, has been successfully classified using FMM-CART with good accuracy rates. The results are comparable, if not better, than those reported in the literature. Useful explanatory rules in the form of a decision tree are also elicited from FMM-CART to analyze and understand different fault conditions of induction motors.

  2. Blinded Comparison between an In-Air Reverberation Method and an Electronic Probe Tester in the Detection of Ultrasound Probe Faults.

    Science.gov (United States)

    Dudley, Nicholas J; Woolley, Darren J

    2017-12-01

    The aim of this study was to perform a blinded trial, comparing the results of a visual inspection of the in-air reverberation pattern with the results of an electronic probe tester in detecting ultrasound probe faults. Sixty-two probes were tested. A total of 28 faults were found, 3 only by in-air reverberation assessment and 2 only by the electronic probe tester. The electronic probe tester provided additional information regarding the location of the fault in 74% of the cases in which both methods detected a fault. It is possible to detect the majority of probe faults by visual inspection and in-air reverberation assessment. The latter provides an excellent first-line test, easily performed on a daily basis by equipment users. An electronic probe tester is required if detailed evaluation of faults is necessary. Copyright © 2017 World Federation for Ultrasound in Medicine and Biology. All rights reserved.

  3. Application of the Continuous-Discrete Extended Kalman Filter for Fault Detection in Continuous Glucose Monitors for Type 1 Diabetes

    DEFF Research Database (Denmark)

    Mahmoudi, Zeinab; Boiroux, Dimitri; Hagdrup, Morten

    2016-01-01

    The purpose of this study is the online detection of faults and anomalies of a continuous glucose monitor (CGM). We simulated a type 1 diabetes patient using the Medtronic virtual patient model. The model is a system of stochastic differential equations and includes insulin pharmacokinetics...

  4. Multivariate Principal Component Analysis and Case-Based Reasoning for monitoring, fault detection and diagnosis in a WWTP

    DEFF Research Database (Denmark)

    Ruiz, Magda; Sin, Gürkan; Berjaga, Xavier

    2011-01-01

    The main idea of this paper is to develop a methodology for process monitoring, fault detection and predictive diagnosis of a WasteWater Treatment Plant (WWTP). To achieve this goal, a combination of Multiway Principal Component Analysis (MPCA) and Case-Based Reasoning (CBR) is proposed. First...

  5. Fault detection and identification for a class of continuous piecewise affine systems with unknown subsystems and partitions

    NARCIS (Netherlands)

    Le quang, Thuan; Baldi, S.

    2018-01-01

    This paper establishes a novel online fault detection and identification strategy for a class of continuous piecewise affine (PWA) systems, namely, bimodal and trimodal PWA systems. The main contributions with respect to the state-of-the-art are the recursive nature of the proposed scheme and the

  6. Shared Magnetics Hall Thruster Project

    Data.gov (United States)

    National Aeronautics and Space Administration — In the proposed Phase II program, Busek Co. will demonstrate an innovative methodology for clustering Hall thrusters into a high performance, very high power...

  7. Shared Magnetics Hall Thruster Project

    Data.gov (United States)

    National Aeronautics and Space Administration — In the proposed Phase I program, Busek Co. will demonstrate an innovative methodology for clustering Hall thrusters into a high performance, very high power...

  8. 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...... can occur simultaneously, whereas faults belonging to different fault sets appear disjoint in time. The proposed fault detection and isolation (FDI) scheme consists of three steps. A fault detection (FD) step is followed by a fault set isolation (FSI) step. Here the fault set is isolated wherein...... the faults have occurred. The last step is a fault isolation (FI) of the faults occurring in a specific fault set, i.e. equivalent with the standard FI step....

  9. Electronegative Gas Thruster

    Science.gov (United States)

    Dankanich, John; Polzin, Kurt; Walker, Mitchell

    2015-01-01

    The project is an international collaboration and academic partnership to mature an innovative electric propulsion thruster concept to Technology Research Level-3 (TRL-3) through direct thrust measurement. The project includes application assessment of the technology ranging from small spacecraft to high power. The Plasma propulsion with Electronegative GASES(PEGASES) basic proof of concept has been matured to TRL-2 by Ane Aanesland of Laboratoire de Physique des Plasma at Ecole Polytechnique. The concept has advantages through eliminating the neutralizer requirement and should yield longer life and lower cost over conventional gridded ion engines. The objective of this research is to validate the proof of concept through the first direct thrust measurements and mature the concept to TRL-3.

  10. Final Technical Report Recovery Act: Online Nonintrusive Condition Monitoring and Fault Detection for Wind Turbines

    Energy Technology Data Exchange (ETDEWEB)

    Wei Qiao

    2012-05-29

    The penetration of wind power has increased greatly over the last decade in the United States and across the world. The U.S. wind power industry installed 1,118 MW of new capacity in the first quarter of 2011 alone and entered the second quarter with another 5,600 MW under construction. By 2030, wind energy is expected to provide 20% of the U.S. electricity needs. As the number of wind turbines continues to grow, the need for effective condition monitoring and fault detection (CMFD) systems becomes increasingly important [3]. Online CMFD is an effective means of not only improving the reliability, capacity factor, and lifetime, but it also reduces the downtime, energy loss, and operation and maintenance (O&M) of wind turbines. The goal of this project is to develop novel online nonintrusive CMFD technologies for wind turbines. The proposed technologies use only the current measurements that have been used by the control and protection system of a wind turbine generator (WTG); no additional sensors or data acquisition devices are needed. Current signals are reliable and easily accessible from the ground without intruding on the wind turbine generators (WTGs) that are situated on high towers and installed in remote areas. Therefore, current-based CMFD techniques have great economic benefits and the potential to be adopted by the wind energy industry. Specifically, the following objectives and results have been achieved in this project: (1) Analyzed the effects of faults in a WTG on the generator currents of the WTG operating at variable rotating speed conditions from the perspective of amplitude and frequency modulations of the current measurements; (2) Developed effective amplitude and frequency demodulation methods for appropriate signal conditioning of the current measurements to improve the accuracy and reliability of wind turbine CMFD; (3) Developed a 1P-invariant power spectrum density (PSD) method for effective signature extraction of wind turbine faults with

  11. Rolling bearing fault detection and diagnosis based on composite multiscale fuzzy entropy and ensemble support vector machines

    Science.gov (United States)

    Zheng, Jinde; Pan, Haiyang; Cheng, Junsheng

    2017-02-01

    To timely detect the incipient failure of rolling bearing and find out the accurate fault location, a novel rolling bearing fault diagnosis method is proposed based on the composite multiscale fuzzy entropy (CMFE) and ensemble support vector machines (ESVMs). Fuzzy entropy (FuzzyEn), as an improvement of sample entropy (SampEn), is a new nonlinear method for measuring the complexity of time series. Since FuzzyEn (or SampEn) in single scale can not reflect the complexity effectively, multiscale fuzzy entropy (MFE) is developed by defining the FuzzyEns of coarse-grained time series, which represents the system dynamics in different scales. However, the MFE values will be affected by the data length, especially when the data are not long enough. By combining information of multiple coarse-grained time series in the same scale, the CMFE algorithm is proposed in this paper to enhance MFE, as well as FuzzyEn. Compared with MFE, with the increasing of scale factor, CMFE obtains much more stable and consistent values for a short-term time series. In this paper CMFE is employed to measure the complexity of vibration signals of rolling bearings and is applied to extract the nonlinear features hidden in the vibration signals. Also the physically meanings of CMFE being suitable for rolling bearing fault diagnosis are explored. Based on these, to fulfill an automatic fault diagnosis, the ensemble SVMs based multi-classifier is constructed for the intelligent classification of fault features. Finally, the proposed fault diagnosis method of rolling bearing is applied to experimental data analysis and the results indicate that the proposed method could effectively distinguish different fault categories and severities of rolling bearings.

  12. Detection of stator winding faults in induction motors using three-phase current monitoring.

    Science.gov (United States)

    Sharifi, Rasool; Ebrahimi, Mohammad

    2011-01-01

    The objective of this paper is to propose a new method for the detection of inter-turn short circuits in the stator windings of induction motors. In the previous reported methods, the supply voltage unbalance was the major difficulty, and this was solved mostly based on the sequence component impedance or current which are difficult to implement. Some other methods essentially are included in the offline methods. The proposed method is based on the motor current signature analysis and utilizes three phase current spectra to overcome the mentioned problem. Simulation results indicate that under healthy conditions, the rotor slot harmonics have the same magnitude in three phase currents, while under even 1 turn (0.3%) short circuit condition they differ from each other. Although the magnitude of these harmonics depends on the level of unbalanced voltage, they have the same magnitude in three phases in these conditions. Experiments performed under various load, fault, and supply voltage conditions validate the simulation results and demonstrate the effectiveness of the proposed technique. It is shown that the detection of resistive slight short circuits, without sensitivity to supply voltage unbalance is possible. Copyright © 2010 ISA. Published by Elsevier Ltd. All rights reserved.

  13. Spectral analysis to detection of short circuit fault of solar photovoltaic modules in strings

    International Nuclear Information System (INIS)

    Sevilla-Camacho, P.Y.; Robles-Ocampo, J.B.; Zuñiga-Reyes, Marco A.

    2017-01-01

    This research work presents a method to detect the number of short circuit faulted solar photovoltaic modules in strings of a photovoltaic system by taking into account speed, safety, and non-use of sensors and specialized and expensive equipment. The method consists on apply the spectral analysis and statistical techniques to the alternating current output voltage of a string and detect the number of failed modules through the changes in the amplitude of the component frequency of 12 kHz. For that, the analyzed string is disconnected of the array; and a small pulsed voltage signal of frequency of 12 kHz introduces him under dark condition and controlled temperature. Previous to the analysis, the signal is analogic filtered in order to reduce the direct current signal component. The spectral analysis technique used is the Fast Fourier Transform. The obtained experimental results were validated through simulation of the alternating current equivalent circuit of a solar cell. In all experimental and simulated test, the method allowed to identify correctly the number of photovoltaic modules with short circuit in the analyzed string. (author)

  14. 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....

  15. Oxygen-Methane Thruster, Phase I

    Data.gov (United States)

    National Aeronautics and Space Administration — Orion Propulsion, Inc. proposes to develop an Oxygen and Methane RCS Thruster to advance the technology of alternate fuels. A successful Oxygen/CH4 RCS Thruster will...

  16. Computational Modeling of Hall Thruster Erosion

    Data.gov (United States)

    National Aeronautics and Space Administration — Hall thrusters are being developed by NASA, DOD, and industry to meet a variety of on-board space propulsion needs. Hall thrusters have been operated successfully...

  17. Advanced test methods for SRAMs: effective solutions for dynamic fault detection in nanoscaled technologies

    National Research Council Canada - National Science Library

    Bosio, Alberto

    2010-01-01

    ... for Dynamic Faults in SRAM MemoriesAdvanced Test Solutions for Dynamic Faults in SRAM Memories Authors of the book: Alberto Bosio, Associate Professor, LIRMM/University of Montpellier - France Luigi Dilillo, CNRS Researcher, LIRMM/CNRS - France Patrick Girard, CNRS Research Director, LIRMM/CNRS - France Serge Pravossoudovitch, Professor, LI...

  18. Active Fault Detection and Isolation for Hybrid Systems

    DEFF Research Database (Denmark)

    Gholami, Mehdi; Schiøler, Henrik; Bak, Thomas

    2009-01-01

    models predicted outputs such that their discrepancies are observable by passive fault diagnosis technique. Isolation of different faults is done by implementation a bank of Extended Kalman Filter (EKF) where the convergence criterion for EKF is confirmed by Genetic Algorithm (GA). The method is applied...

  19. Fault detection and diagnosis for complex multivariable processes using neural networks

    International Nuclear Information System (INIS)

    Weerasinghe, M.

    1998-06-01

    Development of a reliable fault diagnosis method for large-scale industrial plants is laborious and often difficult to achieve due to the complexity of the targeted systems. The main objective of this thesis is to investigate the application of neural networks to the diagnosis of non-catastrophic faults in an industrial nuclear fuel processing plant. The proposed methods were initially developed by application to a simulated chemical process prior to further validation on real industrial data. The diagnosis of faults at a single operating point is first investigated. Statistical data conditioning methods of data scaling and principal component analysis are investigated to facilitate fault classification and reduce the complexity of neural networks. Successful fault diagnosis was achieved with significantly smaller networks than using all process variables as network inputs. Industrial processes often manufacture at various operating points, but demonstrated applications of neural networks for fault diagnosis usually only consider a single (primary) operating point. Developing a standard neural network scheme for fault diagnosis at all operating points would be usually impractical due to the unavailability of suitable training data for less frequently used (secondary) operating points. To overcome this problem, the application of a single neural network for the diagnosis of faults operating at different points is investigated. The data conditioning followed the same techniques as used for the fault diagnosis of a single operating point. The results showed that a single neural network could be successfully used to diagnose faults at operating points other than that it is trained for, and the data conditioning significantly improved the classification. Artificial neural networks have been shown to be an effective tool for process fault diagnosis. However, a main criticism is that details of the procedures taken to reach the fault diagnosis decisions are embedded in

  20. A Novel Method for Gearbox Fault Detection Based on Biorthogonal B-spline Wavelet

    Directory of Open Access Journals (Sweden)

    Guangbin ZHANG

    2011-10-01

    Full Text Available Localized defects of gearbox tend to result in periodic impulses in the vibration signal, which contain important information for system dynamics analysis. So parameter identification of impulse provides an effective approach for gearbox fault diagnosis. Biorthogonal B-spline wavelet has the properties of compact support, high vanishing moment and symmetry, which are suitable to signal de-noising, fast calculation, and reconstruction. Thus, a novel time frequency distribution method is present for gear fault diagnosis by biorthogonal B-spline wavelet. Simulation study concerning singularity signal shows that this wavelet is effective in identifying the fault feature with coefficients map and coefficients line. Furthermore, an integrated approach consisting of wavelet decomposition, Hilbert transform and power spectrum density is used in applications. The results indicate that this method can extract the gearbox fault characteristics and diagnose the fault patterns effectively.

  1. Control Surface Fault Diagnosis with Specified Detection Probability - Real Event Experiences

    DEFF Research Database (Denmark)

    Hansen, Søren; Blanke, Mogens

    2013-01-01

    Diagnosis of actuator faults is crucial for aircraft since loss of actuation can have catastrophic consequences. For autonomous aircraft the steps necessary to achieve fault tolerance is limited when only basic and non-redundant sensor and actuators suites are present. Through diagnosis...... that exploits analytical redundancies it is, nevertheless, possible to cheaply enhance the level of safety. This paper presents a method for diagnosing control surface faults by using basic sensors and hardware available on an autonomous aircraft. The capability of fault diagnosis is demonstrated obtaining...... false alarm probability. A data based method is used to determine the validity of the methods proposed. Verification is achieved using real data and shows that the presented diagnosis method is efficient and could have avoided incidents where faults led to loss of aircraft....

  2. Adaptive Fault Detection on Liquid Propulsion Systems with Virtual Sensors: Algorithms and Architectures

    Science.gov (United States)

    Matthews, Bryan L.; Srivastava, Ashok N.

    2010-01-01

    Prior to the launch of STS-119 NASA had completed a study of an issue in the flow control valve (FCV) in the Main Propulsion System of the Space Shuttle using an adaptive learning method known as Virtual Sensors. Virtual Sensors are a class of algorithms that estimate the value of a time series given other potentially nonlinearly correlated sensor readings. In the case presented here, the Virtual Sensors algorithm is based on an ensemble learning approach and takes sensor readings and control signals as input to estimate the pressure in a subsystem of the Main Propulsion System. Our results indicate that this method can detect faults in the FCV at the time when they occur. We use the standard deviation of the predictions of the ensemble as a measure of uncertainty in the estimate. This uncertainty estimate was crucial to understanding the nature and magnitude of transient characteristics during startup of the engine. This paper overviews the Virtual Sensors algorithm and discusses results on a comprehensive set of Shuttle missions and also discusses the architecture necessary for deploying such algorithms in a real-time, closed-loop system or a human-in-the-loop monitoring system. These results were presented at a Flight Readiness Review of the Space Shuttle in early 2009.

  3. Accurate Monitoring and Fault Detection in Wind Measuring Devices through Wireless Sensor Networks

    Directory of Open Access Journals (Sweden)

    Komal Saifullah Khan

    2014-11-01

    Full Text Available Many wind energy projects report poor performance as low as 60% of the predicted performance. The reason for this is poor resource assessment and the use of new untested technologies and systems in remote locations. Predictions about the potential of an area for wind energy projects (through simulated models may vary from the actual potential of the area. Hence, introducing accurate site assessment techniques will lead to accurate predictions of energy production from a particular area. We solve this problem by installing a Wireless Sensor Network (WSN to periodically analyze the data from anemometers installed in that area. After comparative analysis of the acquired data, the anemometers transmit their readings through a WSN to the sink node for analysis. The sink node uses an iterative algorithm which sequentially detects any faulty anemometer and passes the details of the fault to the central system or main station. We apply the proposed technique in simulation as well as in practical implementation and study its accuracy by comparing the simulation results with experimental results to analyze the variation in the results obtained from both simulation model and implemented model. Simulation results show that the algorithm indicates faulty anemometers with high accuracy and low false alarm rate when as many as 25% of the anemometers become faulty. Experimental analysis shows that anemometers incorporating this solution are better assessed and performance level of implemented projects is increased above 86% of the simulated models.

  4. A new methodology for fault detection in rolling element bearings using singular spectrum analysis

    Directory of Open Access Journals (Sweden)

    Bugharbee Hussein Al

    2018-01-01

    Full Text Available This paper proposes a vibration-based methodology for fault detection in rolling element bearings, which is based on pure data analysis via singular spectrum method. The method suggests building a baseline space from feature vectors made of the signals measured in the healthy/baseline bearing condition. The feature vectors are made using the Euclidean norms of the first three PC’s found for the signals measured. Then, the lagged version of any new signal corresponding to a new (possibly faulty condition is projected onto this baseline feature space in order to assess its similarity to the baseline condition. The category of a new signal vector is determined based on the Mahalanobis distance (MD of its feature vector to the baseline space. A validation of the methodology is suggested based on the results from an experimental test rig. The results obtained confirm the effective performance of the suggested methodology. It is made of simple steps and is easy to apply with a perspective to make it automatic and suitable for commercial applications.

  5. Boundary Detection and Enhancement Strategy for Power System Bus Bar Stabilization—Investigation under Fault Conditions for Islanding Operation

    Directory of Open Access Journals (Sweden)

    Aref Pouryekta

    2018-04-01

    Full Text Available Distribution systems can form islands when faults occur. Each island represents a subsection with variable boundaries subject to the location of fault(s in the system. A subsection with variable boundaries is referred to as an island in this paper. For operation in autonomous mode, it is imperative to detect the island configurations and stabilize these subsections. This paper presents a novel scheme for the detection of island boundaries and stabilizing the system during autonomous operation. In the first stage, a boundary detection method is proposed to detect the configuration of the island. In the second stage, a dynamic voltage sensitivity factor (DVSF is proposed to assess the dynamic performance of the system. In the third stage, a wide area load shedding program is adopted based on DVSF to shed the load in weak bus-bars and stabilize the system. The proposed scheme is validated and tested on a generic 18-bus system using a combination of EMTDC/PSCAD and MATLAB software’s.

  6. Multi-Sensor Data Fusion Using a Relevance Vector Machine Based on an Ant Colony for Gearbox Fault Detection

    Directory of Open Access Journals (Sweden)

    Zhiwen Liu

    2015-08-01

    Full Text Available Sensors play an important role in the modern manufacturing and industrial processes. Their reliability is vital to ensure reliable and accurate information for condition based maintenance. For the gearbox, the critical machine component in the rotating machinery, the vibration signals collected by sensors are usually noisy. At the same time, the fault detection results based on the vibration signals from a single sensor may be unreliable and unstable. To solve this problem, this paper proposes an intelligent multi-sensor data fusion method using the relevance vector machine (RVM based on an ant colony optimization algorithm (ACO-RVM for gearboxes’ fault detection. RVM is a sparse probability model based on support vector machine (SVM. RVM not only has higher detection accuracy, but also better real-time accuracy compared with SVM. The ACO algorithm is used to determine kernel parameters of RVM. Moreover, the ensemble empirical mode decomposition (EEMD is applied to preprocess the raw vibration signals to eliminate the influence caused by noise and other unrelated signals. The distance evaluation technique (DET is employed to select dominant features as input of the ACO-RVM, so that the redundancy and inference in a large amount of features can be removed. Two gearboxes are used to demonstrate the performance of the proposed method. The experimental results show that the ACO-RVM has higher fault detection accuracy than the RVM with normal the cross-validation (CV.

  7. A novel VSP method for fault proximity detection using low velocity waveguides

    Energy Technology Data Exchange (ETDEWEB)

    Cao, S.; Greenhalgh, S. (Flinders University of South Australia, Adelaide, SA (Australia). School of Earth Sciences)

    A potentially useful seismic technique for fault detection has been tested by finite-difference modelling of elastic wave propagation in heterogeneous media. It employs a surface-to-borehole vertical seismic profiling (VSP) geometry. Geophones are lowered into the borehole and seismic sources are fired at or near the ground surface. Small disruptions to a low-velocity zone (LVZ) bounded by higher velocity media can be detected by the technique with the analysis of seismic arrival patterns recorded at geophones in a nearby borehole. A disruption in the LVZ will act as a secondary source when an incident wave strikes it. Some of the energy radiated from the secondary source can be captured by the LVZ and will propagate as guided waves. Both offset VSP and walkaway VSP recording geometries are considered for the numerical simulation. In the offset geometry, a spread of geophones is deployed in the borehole around the coal seam while a single source is fired. Both disrupted and continuous LVZ models have been considered for numerical simulations. Explosive sources (P-wave energy only) are used. Synthetic seismograms show that channel waves can be excited by the scattered energy at a LVZ disruption. The maximum amplitude of the channel waves is comparable to those of direct body waves, which clearly indicates that the disruption in the LVZ can act as an efficient source to generate guided waves. In the walkaway VSP geometry, a single geophone is fixed in the LVZ and a spread of sources is fired on the surface away from the borehole. Synthetic seismograms demonstrate that the apex of the channel-wave arrivals pinpoints the location of the disruption of the LVZ for a layered model. Although this technique has parallels with in-seam seismic coal exploration, it does not require that the source be placed within the low-velocity channel. This method is equally applicable to any exploration target which relates to a LVZ. 7 refs., 4 figs.

  8. 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...... which faults have occurred....

  9. 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. PMID:22400008

  10. 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.

  11. Development of a variable structure-based fault detection and diagnosis strategy applied to an electromechanical system

    Science.gov (United States)

    Gadsden, S. Andrew; Kirubarajan, T.

    2017-05-01

    Signal processing techniques are prevalent in a wide range of fields: control, target tracking, telecommunications, robotics, fault detection and diagnosis, and even stock market analysis, to name a few. Although first introduced in the 1950s, the most popular method used for signal processing and state estimation remains the Kalman filter (KF). The KF offers an optimal solution to the estimation problem under strict assumptions. Since this time, a number of other estimation strategies and filters were introduced to overcome robustness issues, such as the smooth variable structure filter (SVSF). In this paper, properties of the SVSF are explored in an effort to detect and diagnosis faults in an electromechanical system. The results are compared with the KF method, and future work is discussed.

  12. Development of test sequences and diagnoses for the detection of faults in asynchronous combinatorial and sequential circuits

    International Nuclear Information System (INIS)

    Chicheportiche, Armand.

    1976-10-01

    The advent of microelectronics raises serious problems for industry concerning the development and servicing of digital systems. The method is based on the structural analysis of digital electronic circuits. It makes use of the mathematical properties of the ''logical structure''. It aims at the systematic elaboration of test sequences for asynchronous combinatorial and sequential circuits. Moreover, it enables the determination of diagnosis for fault detection [fr

  13. Enhanced Performance of Cylindrical Hall Thrusters

    International Nuclear Information System (INIS)

    Raitses, Y.; Smirnov, A.; Fisch, N.J.

    2007-01-01

    The cylindrical thruster differs significantly in its underlying physical mechanisms from the conventional annular Hall thruster. It features high ionization efficiency, quiet operation, ion acceleration in a large volume-to-surface ratio channel, and performance comparable with the state-of-the-art conventional Hall thrusters. Very significant plume narrowing, accompanied by the increase of the energetic ion fraction and improvement of ion focusing, led to 50-60% increase of the thruster anode efficiency. These improvements were achieved by overrunning the discharge current in the magnetized thruster plasma

  14. Classification and Detection of Wind Turbine Pitch Faults Through SCADA Data Analysis

    Directory of Open Access Journals (Sweden)

    Peter Matthews

    2013-01-01

    Full Text Available The development of wind turbine pitch faults leads to increased mechanical component degradation, severe reduction of asset performance, and a direct increase in annual maintenance costs for the operator. This paper presents a highly accurate data driven classification system for the diagnosis of wind turbine pitch faults. Early diagnosis of these faults can enable operators to move from traditional corrective or time based maintenance towards a predictive or proactive maintenance strategy, whilst simultaneously mitigating risks and requiring no further capital expenditure. Our approach provides transparent, human-readable rules for maintenance operators which have been validated by an independent domain expert. Data from 8 wind turbines was collected every 10 minutes over a period of 28 months with 10 attributes utilised to diagnose pitch faults. Three fault classes are identified, each represented by 6000 instances in each of the testing and training sets. Of the turbines, 4 are used to train the system with a further 4 for validation. Repeated random sampling of the majority fault class was used to reduce computational overheads whilst retaining information content and balancing the training and validation sets to remove majority class bias. A classification accuracy of 85.50% was achieved with 14 human readable rules generated via the RIPPER inductive rule learner. Of these, 11 were described as “useful and intuitive” by an independent domain-expert. An expert system was developed utilising the model along with domain knowledge, resulting in a pitch fault diagnostic accuracy of 87.05% along with a 42.12% reduction in pitch fault alarms.

  15. Observer-based FDI for Gain Fault Detection in Ship Propulsion Benchmark

    DEFF Research Database (Denmark)

    Lootsma, T.F.; Izadi-Zamanabadi, Roozbeh; Nijmeijer, H.

    2001-01-01

    A geometric approach for input-affine nonlinear systems is briefly described and then applied to a ship propulsion benchmark. The obtained results are used to design a diagnostic nonlinear observer for successful FDI of the diesel engine gain fault.......A geometric approach for input-affine nonlinear systems is briefly described and then applied to a ship propulsion benchmark. The obtained results are used to design a diagnostic nonlinear observer for successful FDI of the diesel engine gain fault....

  16. Observer-based FDI for Gain Fault Detection in Ship Propulsion Benchmark

    DEFF Research Database (Denmark)

    Lootsma, T.F.; Izadi-Zamanabadi, Roozbeh; Nijmeijer, H.

    2001-01-01

    A geometric approach for input-affine nonlinear systems is briefly described and then applied to a ship propulsion benchmark. The obtained results are used to design a diagnostic nonlinear observer for successful FDI of the diesel engine gain fault......A geometric approach for input-affine nonlinear systems is briefly described and then applied to a ship propulsion benchmark. The obtained results are used to design a diagnostic nonlinear observer for successful FDI of the diesel engine gain fault...

  17. Magnetoelectrostatic thruster physical geometry tests

    Science.gov (United States)

    Ramsey, W. D.

    1981-01-01

    Inert gas tests are conducted with several magnetoelectrostatic containment discharge chamber geometries. The configurations tested include three discharge chamber lengths; three boundary magnet patterns; two different flux density magnet materials; hemispherical and conical shaped thrusters having different surface-to-volume ratios; and two and three grid ion optics. Argon mass utilizations of 60 to 79% are attained at 210 to 280 eV/ion in different test configurations. Short hemi thruster configurations are found to produce 70 to 92% xenon mass utilization at 185 to 220 eV/ion.

  18. Multiple-Fault Detection Methodology Based on Vibration and Current Analysis Applied to Bearings in Induction Motors and Gearboxes on the Kinematic Chain

    Directory of Open Access Journals (Sweden)

    Juan Jose Saucedo-Dorantes

    2016-01-01

    Full Text Available Gearboxes and induction motors are important components in industrial applications and their monitoring condition is critical in the industrial sector so as to reduce costs and maintenance downtimes. There are several techniques associated with the fault diagnosis in rotating machinery; however, vibration and stator currents analysis are commonly used due to their proven reliability. Indeed, vibration and current analysis provide fault condition information by means of the fault-related spectral component identification. This work presents a methodology based on vibration and current analysis for the diagnosis of wear in a gearbox and the detection of bearing defect in an induction motor both linked to the same kinematic chain; besides, the location of the fault-related components for analysis is supported by the corresponding theoretical models. The theoretical models are based on calculation of characteristic gearbox and bearings fault frequencies, in order to locate the spectral components of the faults. In this work, the influence of vibrations over the system is observed by performing motor current signal analysis to detect the presence of faults. The obtained results show the feasibility of detecting multiple faults in a kinematic chain, making the proposed methodology suitable to be used in the application of industrial machinery diagnosis.

  19. Active Fault Isolation in MIMO Systems

    DEFF Research Database (Denmark)

    Niemann, Hans Henrik; Poulsen, Niels Kjølstad

    2014-01-01

    Active fault isolation of parametric faults in closed-loop MIMO system s are considered in this paper. The fault isolation consists of two steps. T he first step is group- wise fault isolation. Here, a group of faults is isolated from other pos sible faults in the system. The group-wise fault...... isolation is based directly on the input/output s ignals applied for the fault detection. It is guaranteed that the fault group includes the fault that had occurred in the system. The second step is individual fault isolation in the fault group . Both types of isolation are obtained by applying dedicated...

  20. 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