WorldWideScience

Sample records for based fault detection

  1. Verification-based Software-fault Detection

    OpenAIRE

    Gladisch, Christoph David

    2011-01-01

    Software is used in many safety- and security-critical systems. Software development is, however, an error-prone task. In this dissertation new techniques for the detection of software faults (or software "bugs") are described which are based on a formal deductive verification technology. The described techniques take advantage of information obtained during verification and combine verification technology with deductive fault detection and test generation in a very unified way.

  2. DATA-MINING BASED FAULT DETECTION

    Institute of Scientific and Technical Information of China (English)

    Ma Hongguang; Han Chongzhao; Wang Guohua; Xu Jianfeng; Zhu Xiaofei

    2005-01-01

    This paper presents a fault-detection method based on the phase space reconstruction and data mining approaches for the complex electronic system. The approach for the phase space reconstruction of chaotic time series is a combination algorithm of multiple autocorrelation and Γ-test, by which the quasi-optimal embedding dimension and time delay can be obtained.The data mining algorithm, which calculates the radius of gyration of unit-mass point around the centre of mass in the phase space, can distinguish the fault parameter from the chaotic time series output by the tested system. The experimental results depict that this fault detection method can correctly detect the fault phenomena of electronic system.

  3. Nonlinear Model-Based Fault Detection for a Hydraulic Actuator

    NARCIS (Netherlands)

    Van Eykeren, L.; Chu, Q.P.

    2011-01-01

    This paper presents a model-based fault detection algorithm for a specific fault scenario of the ADDSAFE project. The fault considered is the disconnection of a control surface from its hydraulic actuator. Detecting this type of fault as fast as possible helps to operate an aircraft more cost effect

  4. Exact, almost and delayed fault detection: An observer based approach

    DEFF Research Database (Denmark)

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

    1999-01-01

    This paper consider the problem of fault detection and isolation in continuous- and discrete-time systems while using zero or almost zero threshold. A number of different fault detections and isolation problems using exact or almost exact disturbance decoupling are formulated. Solvability...... conditions are given for the formulated design problems together with methods for appropriate design of observer based fault detectors. The l-step delayed fault detection problem is also considered for discrete-time systems . Moreover, certain indirect fault detection methods such as unknown input observers...

  5. Analysis of fault detection method based on predictive filter approach

    Institute of Scientific and Technical Information of China (English)

    LI Ji; ZHANG Hongyue

    2005-01-01

    A new detection method for component faults based on predictive filters together with the fault detectability, false alarm rate, missed alarm rate and upper bound of detection time are proposed. The efficiency of the method is illustrated by a simulation example of a second-order system. It is shown that the fault detection method using predictive filters has a small delay, a low false alarm rate and a low missed alarm rate. Furthermore the filter can give accurate estimates of states even after a fault occurs. The real-time estimation provided by this method can also be used for fault tolerant control.

  6. Model Based Incipient Fault Detection for Gear Drives

    Institute of Scientific and Technical Information of China (English)

    2001-01-01

    This paper presents the method of model based incipient fault detection for gear drives,this method is based on parity space method. It can generate the robust residual that is maximally sensitive to the fault caused by the change of the parameters. The example of simulation shows the application of the method, and the residual waves have different characteristics due to different parameter changes; one can detect and isolate the fault based on the different characteristics.

  7. 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...

  8. 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...... gearbox. Only the generator speed measurement which is available in even simple wind turbine control systems is used as input. Consequently this proposed scheme does not need additional sensors and computers for monitoring the condition of the wind gearbox. The scheme is evaluated on a wide-spread wind...

  9. Distance Based Fault detection in wireless sensor network

    Directory of Open Access Journals (Sweden)

    Ayasha Siddiqua

    2013-05-01

    Full Text Available Wireless Sensor Network (WSNs have become a new information collection and monitoring solution for a variety of application. In WSN, sensor nodes have strong hardware and software restrictionin terms of processing power, memory capability, power supply and communication throughput. Due to these restrictions, fault may occur in sensor. This paper presents a distance based fault detection (DBFDmethod for wireless sensor network using the average of confidence level and sensed data of sensor node. Simulation results show that sensor nodes with permanent faults and without fault which was judged as faulty are identified with high accuracy for a wide range of fault rate, and keep false alarm rate for different levels of sensor fault model and also correct nodes are identified by accuracy.

  10. Convolutional Neural Network Based Fault Detection for Rotating Machinery

    Science.gov (United States)

    Janssens, Olivier; Slavkovikj, Viktor; Vervisch, Bram; Stockman, Kurt; Loccufier, Mia; Verstockt, Steven; Van de Walle, Rik; Van Hoecke, Sofie

    2016-09-01

    Vibration analysis is a well-established technique for condition monitoring of rotating machines as the vibration patterns differ depending on the fault or machine condition. Currently, mainly manually-engineered features, such as the ball pass frequencies of the raceway, RMS, kurtosis an crest, are used for automatic fault detection. Unfortunately, engineering and interpreting such features requires a significant level of human expertise. To enable non-experts in vibration analysis to perform condition monitoring, the overhead of feature engineering for specific faults needs to be reduced as much as possible. Therefore, in this article we propose a feature learning model for condition monitoring based on convolutional neural networks. The goal of this approach is to autonomously learn useful features for bearing fault detection from the data itself. Several types of bearing faults such as outer-raceway faults and lubrication degradation are considered, but also healthy bearings and rotor imbalance are included. For each condition, several bearings are tested to ensure generalization of the fault-detection system. Furthermore, the feature-learning based approach is compared to a feature-engineering based approach using the same data to objectively quantify their performance. The results indicate that the feature-learning system, based on convolutional neural networks, significantly outperforms the classical feature-engineering based approach which uses manually engineered features and a random forest classifier. The former achieves an accuracy of 93.61 percent and the latter an accuracy of 87.25 percent.

  11. On Identifiability of Bias-Type Actuator-Sensor Faults in Multiple-Model-Based Fault Detection and Identification

    Science.gov (United States)

    Joshi, Suresh M.

    2012-01-01

    This paper explores a class of multiple-model-based fault detection and identification (FDI) methods for bias-type faults in actuators and sensors. These methods employ banks of Kalman-Bucy filters to detect the faults, determine the fault pattern, and estimate the fault values, wherein each Kalman-Bucy filter is tuned to a different failure pattern. Necessary and sufficient conditions are presented for identifiability of actuator faults, sensor faults, and simultaneous actuator and sensor faults. It is shown that FDI of simultaneous actuator and sensor faults is not possible using these methods when all sensors have biases.

  12. 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....

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

    International Nuclear Information System (INIS)

    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)

  14. Sparsity-based algorithm for detecting faults in rotating machines

    Science.gov (United States)

    He, Wangpeng; Ding, Yin; Zi, Yanyang; Selesnick, Ivan W.

    2016-05-01

    This paper addresses the detection of periodic transients in vibration signals so as to detect faults in rotating machines. For this purpose, we present a method to estimate periodic-group-sparse signals in noise. The method is based on the formulation of a convex optimization problem. A fast iterative algorithm is given for its solution. A simulated signal is formulated to verify the performance of the proposed approach for periodic feature extraction. The detection performance of comparative methods is compared with that of the proposed approach via RMSE values and receiver operating characteristic (ROC) curves. Finally, the proposed approach is applied to single fault diagnosis of a locomotive bearing and compound faults diagnosis of motor bearings. The processed results show that the proposed approach can effectively detect and extract the useful features of bearing outer race and inner race defect.

  15. Image Fault Area Detection Algorithm Based on Visual Perception

    Directory of Open Access Journals (Sweden)

    Peng-Lu

    2011-02-01

    Full Text Available If the natural scenes decomposed by basic ICA which simulates visual perception then the arrangement in space of its basis functions are in disorder. This result is contradicted with physiological mechanisms of vision. So, a new compute model is proposed to simulate two important mechanisms of vision which are visual cortex receptive field topology construct and synchronous oscillation among neuron group. To solve the problem of train image fault detection, a novel algorithm was proposed based on above compute model. The experiment results show that, the algorithm can increase fault detection rate effectively compared with traditional methods which absence of above two important mechanisms of vision.

  16. 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.

  17. Vibration based fault detection techniques for mechanical structures

    International Nuclear Information System (INIS)

    Fault detection techniques for mechanical structures and their application are becoming more important in recent years in the field of structure health monitoring. The intention of this paper is to present available state of the art methods that could be implemented in mechanical structures. Global based methods that contribute on detection, isolation and analysis of fault from changes in vibration characteristics of the structure are presented. Techniques are based on the idea that modal frequencies, mode shapes and modal damping as model properties of the structure can be determine as function of physical properties. In addition, if a fault appears in mechanical structure, this can be recognized as changes in the physical properties, which leads to cause changes in the modal properties of the structure. (Author)

  18. RepTFD: Replay Based Transient Fault Detection

    OpenAIRE

    Li, Lei; Chen, Tianshi; Chen, Yunji; Li, Ling; Wu, Ruiyang

    2012-01-01

    The advances in IC process make future chip multiprocessors (CMPs) more and more vulnerable to transient faults. To detect transient faults, previous core-level schemes provide redundancy for each core separately. As a result, they may leave transient faults in the uncore parts, which consume over 50% area of a modern CMP, escaped from detection. This paper proposes RepTFD, the first core-level transient fault detection scheme with 100% coverage. Instead of providing redundancy for each core ...

  19. DSP-Based Sensor Fault Detection and Post Fault-Tolerant Control of an Induction Motor-Based Electric Vehicle

    Directory of Open Access Journals (Sweden)

    Bekheïra Tabbache

    2012-01-01

    Full Text Available This paper deals with sensor fault detection within a reconfigurable direct torque control of an induction motor-based electric vehicle. The proposed strategy concerns current, voltage, and speed sensors faults that are detected and followed by post fault-tolerant control to allow the vehicle continuous operation. The proposed approach is validated through experiments on an induction motor drive and simulations on an electric vehicle using a European urban and extraurban driving cycle.

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

    DEFF Research Database (Denmark)

    Odgaard, Peter Fogh; Mataji, Babak

    2008-01-01

    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......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 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...

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

    DEFF Research Database (Denmark)

    Lootsma, T.F.

    With the rise in automation the increase in fault detectionand isolation & reconfiguration is inevitable. Interest in fault detection and isolation (FDI) for nonlinear systems has grown significantly in recent years. The design of FDI is motivated by the need for knowledge about occurring faults......-tolerance can be applied to ordinary industrial processes that are not categorized as high risk applications, but where high availability is desirable. The quality of fault-tolerant control is totally dependent on the quality of the underlying algorithms. They detect possible faults, and later reconfigure...... control software to handle the effects of the particular fault event. In the past mainly linear FDI methods were developed, but as most industrial plants show nonlinear behavior, nonlinear methods for fault diagnosis could probably perform better. This thesis considers the design of FDI for nonlinear...

  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...

  3. 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....

  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...

  5. 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 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....

  6. 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...

  7. Real-time fault detection method based on belief rule base for aircraft navigation system

    Institute of Scientific and Technical Information of China (English)

    Zhao Xin; Wang Shicheng; Zhang Jinsheng; Fan Zhiliang; Min Haibo

    2013-01-01

    Real-time and accurate fault detection is essential to enhance the aircraft navigation system's reliability and safety.The existent detection methods based on analytical model draws back at simultaneously detecting gradual and sudden faults.On account of this reason,we propose an online detection solution based on non-analytical model.In this article,the navigation system fault detection model is established based on belief rule base (BRB),where the system measuring residual and its changing rate are used as the inputs of BRB model and the fault detection function as the output.To overcome the drawbacks of current parameter optimization algorithms for BRB and achieve online update,a parameter recursive estimation algorithm is presented for online BRB detection model based on expectation maximization (EM) algorithm.Furthermore,the proposed method is verified by navigation experiment.Experimental results show that the proposed method is able to effectively realize online parameter evaluation in navigation system fault detection model.The output of the detection model can track the fault state very well,and the faults can be diagnosed in real time and accurately.In addition,the detection ability,especially in the probability of false detection,is superior to offline optimization method,and thus the system reliability has great improvement.

  8. Model-based fault detection and isolation for intermittently active faults with application to motion-based thruster fault detection and isolation for spacecraft

    Science.gov (United States)

    Wilson, Edward (Inventor)

    2008-01-01

    The present invention is a method for detecting and isolating fault modes in a system having a model describing its behavior and regularly sampled measurements. The models are used to calculate past and present deviations from measurements that would result with no faults present, as well as with one or more potential fault modes present. Algorithms that calculate and store these deviations, along with memory of when said faults, if present, would have an effect on the said actual measurements, are used to detect when a fault is present. Related algorithms are used to exonerate false fault modes and finally to isolate the true fault mode. This invention is presented with application to detection and isolation of thruster faults for a thruster-controlled spacecraft. As a supporting aspect of the invention, a novel, effective, and efficient filtering method for estimating the derivative of a noisy signal is presented.

  9. 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

    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...

  10. 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....

  11. Discrete wavelet transform-based fault diagnosis for driving system of pipeline detection robot arm

    Institute of Scientific and Technical Information of China (English)

    Deng Huiyu; Wang Xinli; Ma Peisun

    2005-01-01

    A real-time wavelet multi-resolution analysis (MRA)-based fault detection algorithm is proposed. The first stage detailed MRA signals extracted from the original signals were used as the criteria for fault detection. By measuring sharp variations in the detailed MRA signals, faults in the motor driving system of pipeline detection robot arm could be detected. The fault type was then identified by comparison of the three-phase MRA sharp variations. The effects of the faults were examined. The simulation results show that this algorithm is effective and robust, it is promising for fault detection in a robot's joint driving system. The method is simple, rapid and it can operate in real time.

  12. Sliding mode based fault detection, reconstruction and fault tolerant control scheme for motor systems.

    Science.gov (United States)

    Mekki, Hemza; Benzineb, Omar; Boukhetala, Djamel; Tadjine, Mohamed; Benbouzid, Mohamed

    2015-07-01

    The fault-tolerant control problem belongs to the domain of complex control systems in which inter-control-disciplinary information and expertise are required. This paper proposes an improved faults detection, reconstruction and fault-tolerant control (FTC) scheme for motor systems (MS) with typical faults. For this purpose, a sliding mode controller (SMC) with an integral sliding surface is adopted. This controller can make the output of system to track the desired position reference signal in finite-time and obtain a better dynamic response and anti-disturbance performance. But this controller cannot deal directly with total system failures. However an appropriate combination of the adopted SMC and sliding mode observer (SMO), later it is designed to on-line detect and reconstruct the faults and also to give a sensorless control strategy which can achieve tolerance to a wide class of total additive failures. The closed-loop stability is proved, using the Lyapunov stability theory. Simulation results in healthy and faulty conditions confirm the reliability of the suggested framework. PMID:25747198

  13. 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...

  14. Wavelet Based Fault Detection, Classification in Transmission System with TCSC Controllers

    Directory of Open Access Journals (Sweden)

    G.Satyanarayana,

    2015-08-01

    Full Text Available This paper presents simulation results of the application of distance relays for the protection of transmission systems employing flexible alternating current transmission controllers such as Thyristor Controlled Series Capacitor (TCSC. The complete digital simulation of TCSC within a transmission system is performed in the MATLAB/Simulink environment using the Power System Block set (PSB. This paper presents an efficient method based on wavelet transforms both fault detection and classification which is almost independent of fault impedance, fault location and fault inception angle of transmission line fault currents with FACTS controllers.

  15. Fault detection and identification based on combining logic and model in a wall-climbing robot

    Institute of Scientific and Technical Information of China (English)

    Yong JIANG; Hongguang WANG; Lijin FANG; Mingyang ZHAO

    2009-01-01

    A combined logic- and model-based approach to fault detection and identification (FDI) in a suction foot control system of a wall-climbing robot is presented in this paper. For the control system, some fault models are derived by kinematics analysis. Moreover, the logic relations of the system states are known in advance. First, a fault tree is used to analyze the system by evaluating the basic events (elementary causes), which can lead to a root event (a particular fault). Then, a multiple-model adaptive estimation algorithm is used to detect and identify the model-known faults. Finally, based on the system states of the robot and the results of the estimation, the model-unknown faults are also identified using logical reasoning. Experiments show that the proposed approach based on the combination of logical reasoning and model estimating is efficient in the FDI of the robot.

  16. 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 ...

  17. A New UKF Based Fault Detection Method in Non-linear Systems

    Institute of Scientific and Technical Information of China (English)

    GE Zhe-xue; YANG Yong-min; HU Zheng

    2006-01-01

    To detect the bias fault in stochastic non-linear dynamic systems, a new Unscented Kalman Filtering(UKF) based real-time recursion detection method is brought forward with the consideration of the flaws of traditional Extended Kalman Filtering(EKF). It uses the UKF as the residual generation method and the Weighted-Sum Squared Residual (WSSR) as the fault detection strategy. The simulation results are provided which demonstrate better effectiveness and a higher detection ratio of the developed methods.

  18. Performance based fault diagnosis

    DEFF Research Database (Denmark)

    Niemann, Hans Henrik

    2002-01-01

    Different aspects of fault detection and fault isolation in closed-loop systems are considered. It is shown that using the standard setup known from feedback control, it is possible to formulate fault diagnosis problems based on a performance index in this general standard setup. It is also shown...

  19. A Fault Detection Method of Rolling Bearing Based on Wavelet Packet-cepstrum

    Directory of Open Access Journals (Sweden)

    Tingting Leng

    2013-04-01

    Full Text Available In this study, we put forward a fault detection method of rolling bearing based on the wavelet packet-cepstrum. Firstly, the original signal is decomposed using the wavelet packet. Secondly, calculate the energy of the decomposed sub-band reconstruction signal and select the relatively band which is concentrated on the fault energy. Finally, calculate cepstrum of the reconstruction signal to detect fault. The actual normal and fault data of the rolling bearing's outer ring is analyzed in applying this method in the MATLAB simulation circumstance. The result shows that the outer ring's failure frequency measured by the experiment is consistent with the theoretical calculation result.

  20. Study and Design of Diaphragm Pump Vibration Detection Fault Diagnosis System Based on FFT

    OpenAIRE

    Jia Yin; Jiande Wu; Xuyi Yuan; Xiaodong Wang; Yugang Fan

    2013-01-01

    This study has proposed a fault diagnosis system based on vibration detection. The system mainly includes four modules: signal acquisition module, signal processing module, state identification module, fault diagnosis and alarm module. The system uses CMSS 2200 acceleration sensor to collect vibration signals, processing spectrum with FFT (Fast Fourier Transform) which is used effectively in current industry and finally achieve fault diagnosis and prediction for diaphragm pump. Through collec...

  1. 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

    This paper presents and compares model-based and data-driven fault detection approaches for coal mill systems. The first approach detects faults with an optimal unknown input observer developed from a simplified energy balance model. Due to the time-consuming effort in developing a first principles...... 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...... between process measurement and the NOC model prediction is used for fault detection. A hybrid approach, where a data-driven model is employed to derive an optimal unknown input observer, is also implemented. The three methods are evaluated with case studies on coal mill data, which includes a fault...

  2. ASCS Online Fault Detection and Isolation Based on an Improved MPCA

    Institute of Scientific and Technical Information of China (English)

    PENG Jianxin; LIU Haiou; HU Yuhui; XI Junqiang; CHEN Huiyan

    2014-01-01

    Multi-way principal component analysis (MPCA) has received considerable attention and been widely used in process monitoring. A traditional MPCA algorithm unfolds multiple batches of historical data into a two-dimensional matrix and cut the matrix along the time axis to form subspaces. However, low efficiency of subspaces and difficult fault isolation are the common disadvantages for the principal component model. This paper presents a new subspace construction method based on kernel density estimation function that can effectively reduce the storage amount of the subspace information. The MPCA model and the knowledge base are built based on the new subspace. Then, fault detection and isolation with the squared prediction error (SPE) statistic and the Hotelling (T2) statistic are also realized in process monitoring. When a fault occurs, fault isolation based on the SPE statistic is achieved by residual contribution analysis of different variables. For fault isolation of subspace based on the T2 statistic, the relationship between the statistic indicator and state variables is constructed, and the constraint conditions are presented to check the validity of fault isolation. Then, to improve the robustness of fault isolation to unexpected disturbances, the statistic method is adopted to set the relation between single subspace and multiple subspaces to increase the corrective rate of fault isolation. Finally fault detection and isolation based on the improved MPCA is used to monitor the automatic shift control system (ASCS) to prove the correctness and effectiveness of the algorithm. The research proposes a new subspace construction method to reduce the required storage capacity and to prove the robustness of the principal component model, and sets the relationship between the state variables and fault detection indicators for fault isolation.

  3. ASCS online fault detection and isolation based on an improved MPCA

    Science.gov (United States)

    Peng, Jianxin; Liu, Haiou; Hu, Yuhui; Xi, Junqiang; Chen, Huiyan

    2014-09-01

    Multi-way principal component analysis (MPCA) has received considerable attention and been widely used in process monitoring. A traditional MPCA algorithm unfolds multiple batches of historical data into a two-dimensional matrix and cut the matrix along the time axis to form subspaces. However, low efficiency of subspaces and difficult fault isolation are the common disadvantages for the principal component model. This paper presents a new subspace construction method based on kernel density estimation function that can effectively reduce the storage amount of the subspace information. The MPCA model and the knowledge base are built based on the new subspace. Then, fault detection and isolation with the squared prediction error (SPE) statistic and the Hotelling ( T 2) statistic are also realized in process monitoring. When a fault occurs, fault isolation based on the SPE statistic is achieved by residual contribution analysis of different variables. For fault isolation of subspace based on the T 2 statistic, the relationship between the statistic indicator and state variables is constructed, and the constraint conditions are presented to check the validity of fault isolation. Then, to improve the robustness of fault isolation to unexpected disturbances, the statistic method is adopted to set the relation between single subspace and multiple subspaces to increase the corrective rate of fault isolation. Finally fault detection and isolation based on the improved MPCA is used to monitor the automatic shift control system (ASCS) to prove the correctness and effectiveness of the algorithm. The research proposes a new subspace construction method to reduce the required storage capacity and to prove the robustness of the principal component model, and sets the relationship between the state variables and fault detection indicators for fault isolation.

  4. Fault detection and isolation for self powered neutron detectors based on Principal Component Analysis

    International Nuclear Information System (INIS)

    Highlights: • The methodology of Principal Component Analysis (PCA) is utilized to detect faults occurred in self powered neutron detectors. • The square prediction error based on the PCA model is employed to detect the SPND fault. • The Detector Validity Index (DVI) based on the reconstruction is employed to isolate the faulty SPND. • The fault detection and isolation scheme is validated with four types of simulated SPND faults. - Abstract: The self powered neutron detectors (SPNDs) play an important role in nuclear reactor monitoring. The 3-D power distribution and parameters used to evaluate the operation condition of reactor and the margin of safety can be determined using the measurement of SPNDs through power mapping procedure. Faulty SPNDs that are either completely or partially failed (hard fault or soft fault) provide incorrect information for monitoring. Correct detection and isolation of the faulty SPNDs are of primary importance to the efficient operation and management of the nuclear reactor. In this study, the methodology of Principal Component Analysis (PCA) is utilized to construct the mathematical models among various detectors at different axial location within the same string. The data used to build the mathematical models are generated by advanced neutronics code SMART rather than measurements. The square prediction error based on the model and the Detector Validity Index (DVI) based on the reconstruction are employed, respectively, to detect the SPND fault and to isolate the faulty SPNDs. The fault detection and isolation scheme is validated with four types of simulated SPND faults, i.e. bias, drifting, precision degradation and complete failure. The simulation results show that the proposed PCA based method can be used in the nuclear reactor to ensure that faulty SPNDs can be detected quickly

  5. APPROACH TO FAULT ON-LINE DETECTION AND DIAGNOSIS BASED ON NEURAL NETWORKS FOR ROBOT IN FMS

    Institute of Scientific and Technical Information of China (English)

    1998-01-01

    Based on radial basis function (RBF) neural networks, the healthy working model of each sub-system of robot in FMS is established. A new approach to fault on-line detection and diagnosis according to neural networks model is presented. Fault double detection based on neural network model and threshold judgement and quick fault identification based on multi-layer feedforward neural networks are applied, which can meet quickness and reliability of fault detection and diagnosis for robot in FMS.

  6. Dynamic Reconstruction-Based Fuzzy Neural Network Method for Fault Detection in Chaotic System

    Institute of Scientific and Technical Information of China (English)

    YANG Hongying; YE Hao; WANG Guizeng

    2008-01-01

    This paper presents a method for detecting weak fault signals in chaotic systems based on the chaotic dynamics reconstruction technique and the fuzzy neural system (FNS). The Grassberger-Procaccia algorithm and least squares regression were used to calculate the correlation dimension for the model order estimate. Based on the model order, an appropriately structured FNS model was designed to predict system faults. Through reasonable analysis of predicted errors, the disturbed signal can be extracted efficiently and correctly from the chaotic background. Satisfactory results were obtained by using several kinds of simula-tive faults which were extracted from the practical chaotic fault systems. Experimental results demonstra tethat the proposed approach has good prediction accuracy and can deal with data having a -40 dB signal to noise ratio (SNR). The low SNR requirement makes the approach a powerful tool for early fault detection.

  7. 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...

  8. Mine-hoist fault-condition detection based on the wavelet packet transform and kernel PCA

    Institute of Scientific and Technical Information of China (English)

    XIA Shi-xiong; NIU Qiang; ZHOU Yong; ZHANG Lei

    2008-01-01

    A new algorithm was developed to correctly identify fault conditions and accurately monitor fault development in a mine hoist. The new method is based on the Wavelet Packet Transform (WPT) and kernel PCA (Kernel Principal Component Analysis, KPCA). For non-linear monitoring systems the key to fault detection is the extracting of main features. The wavelet packet transform is a novel technique of signal processing that possesses excellent characteristics of time-frequency localization. It is suitable for analysing time-varying or transient signals. KPCA maps the original input features into a higher dimension feature space through a non-linear mapping. The principal components are then found in the higher dimension feature space. The KPCA transformation was applied to extracting the main nonlinear features from experimental fault feature data after wavelet packet transformation. The results show that the proposed method affords credible fault detection and identification.

  9. Robust PCA-Based Abnormal Traffic Flow Pattern Isolation and Loop Detector Fault Detection

    Institute of Scientific and Technical Information of China (English)

    JIN Xuexiang; ZHANG Yi; LI Li; HU Jianming

    2008-01-01

    One key function of intelligent transportation systems is to automatically detect abnormal traffic phenomena and to help further investigations of the cause of the abnormality. This paper describes a robust principal components analysis (RPCA)-based abnormal traffic flow pattern isolation and loop detector fault detection method. The results show that RPCA is a useful tool to distinguish regular traffic flow from abnor-mal traffic flow patterns caused by accidents and loop detector faults. This approach gives an effective traffic flow data pre-processing method to reduce the human effort in finding potential loop detector faults. The method can also be used to further investigate the causes of the abnormality.

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

    DEFF Research Database (Denmark)

    Li, Hui; Yang, Chao; Hu, Yaogang;

    2014-01-01

    Fault-tolerant control of current sensors is studied in this paper to improve the reliability of a doubly fed induction generator (DFIG). A fault-tolerant control system of current sensors is presented for the DFIG, which consists of a new current observer and an improved current sensor fault...... detection algorithm, and fault-tolerant control system are investigated by simulation. The results indicate that the outputs of the observer and the sensor are highly coherent. The fault detection algorithm can efficiently detect both soft and hard faults in current sensors, and the fault-tolerant control...... system can effectively tolerate both types of faults. © 2013 Published by Elsevier Ltd. All rights reserved....

  11. Enhanced Fault Detection of Rolling Element Bearing Based on Cepstrum Editing and Stochastic Resonance

    Science.gov (United States)

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

    2012-05-01

    By signal pre-whitening based on cepstrum editing,the envelope analysis can be done over the full bandwidth of the pre-whitened signal, and this enhances the bearing characteristic frequencies. The bearing faults detection could be enhanced without knowledge of the optimum frequency bands to demodulate, however, envelope analysis over full bandwidth brings more noise interference. Stochastic resonance (SR), which is now often used in weak signal detection, is an important nonlinear effect. By normalized scale transform, SR can be applied in weak signal detection of machinery system. In this paper, signal pre-whitening based on cepstrum editing and SR theory are combined to enhance the detection of bearing fault. The envelope spectrum kurtosis of bearing fault characteristic components is used as indicators of bearing faults. Detection results of planted bearing inner race faults on a test rig show the enhanced detecting effects of the proposed method. And the indicators of bearing inner race faults enhanced by SR are compared to the ones without enhancement to validate the proposed method.

  12. Observer-based fault detection scheme for a class of discrete time-delay systems

    Institute of Scientific and Technical Information of China (English)

    Zhong Maiying(钟麦英); Zhang Chenghui(张承慧); Ding Steven X; Lam James

    2004-01-01

    In this contribution, robust fault detection problems for discrete time-delay systems with l2-norm bounded un-known inputs are studied. The basic idea of our study is first to introduce a state-memoryless observer-based fault detec-tion filter (FDF) as the residual generator and then to formulate such a FDF design problem as an H∞ optimization prob-lem in the sense of increasing the sensitivity of residual to the faults, while simultaneously enhancing the robustness of residual to unknown input as well as plant input. The main results consist of the formulation of such a residual generation optimization problem, solvability conditions and the derivation of an analytic solution. The residual evaluation problem is also considered, which includes the determination of residual evaluation function and threshold. A numerical example is used to demonstrate the proposed fault detection scheme.

  13. Sparse representation based latent components analysis for machinery weak fault detection

    Science.gov (United States)

    Tang, Haifeng; Chen, Jin; Dong, Guangming

    2014-06-01

    Weak machinery fault detection is a difficult task because of two main reasons (1) At the early stage of fault development, signature of fault related component performs incompletely and is quite different from that at the apparent failure stage. In most instances, it seems almost identical with the normal operating state. (2) The fault feature is always submerged and distorted by relatively strong background noise and macro-structural vibrations even if the fault component already performs completely, especially when the structure of fault components and interference are close. To solve these problems, we should penetrate into the underlying structure of the signal. Sparse representation provides a class of algorithms for finding succinct representations of signal that capture higher-level features in the data. With the purpose of extracting incomplete or seriously overwhelmed fault components, a sparse representation based latent components decomposition method is proposed in this paper. As a special case of sparse representation, shift-invariant sparse coding algorithm provides an effective basis functions learning scheme for capturing the underlying structure of machinery fault signal by iteratively solving two convex optimization problems: an L1-regularized least squares problem and an L2-constrained least squares problem. Among these basis functions, fault feature can be probably contained and extracted if optimal latent component is filtered. The proposed scheme is applied to analyze vibration signals of both rolling bearings and gears. Experiment of accelerated lifetime test of bearings validates the proposed method's ability of detecting early fault. Besides, experiments of fault bearings and gears with heavy noise and interference show the approach can effectively distinguish subtle differences between defect and interference. All the experimental data are analyzed by wavelet shrinkage and basis pursuit de-noising (BPDN) method for comparison.

  14. Fault Detection and Recovery for Full Range of Hydrogen Sensor Based on Relevance Vector Machine

    Institute of Scientific and Technical Information of China (English)

    Kai Song; Bing Wang; Ming Diao; Hongquan Zhang; Zhenyu Zhang

    2015-01-01

    In order to improve the reliability of hydrogen sensor, a novel strategy for full range of hydrogen sensor fault detection and recovery is proposed in this paper. Three kinds of sensors are integrated to realize the measurement for full range of hydrogen concentration based on relevance vector machine ( RVM ) . Failure detection of hydrogen sensor is carried out by using the variance detection method. When a sensor fault is detected, the other fault⁃free sensors can recover the fault data in real⁃time by using RVM predictor accounting for the relevance of sensor data. Analysis, together with both simulated and experimental results, a full⁃range hydrogen detection and hydrogen sensor self⁃validating experiment is presented to demonstrate that the proposed strategy is superior at accuracy and runtime compared with the conventional methods. Results show that the proposed methodology provides a better solution to the full range of hydrogen detection and the reliability improvement of hydrogen sensor.

  15. 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.

  16. 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.

  17. 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

  18. 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

    advanced fault detection and isolation schemes. In this paper, an observer-based fault detection and isolation method for the cooling system in a liquid-cooled frequency converter on a wind turbine which is built up in a scalar version in the laboratory is presented. A dynamic model of the scale cooling...... on the developed dynamical model. The designed fault detection and isolation algorithm is applied on a set of measured experiment data in which different faults are artificially introduced to the scaled cooling system. The experimental results conclude that the different faults are successfully detected...

  19. Rolling Bearing Fault Detection Based on the Teager Energy Operator and Elman Neural Network

    Directory of Open Access Journals (Sweden)

    Hongmei Liu

    2013-01-01

    Full Text Available This paper presents an approach to bearing fault diagnosis based on the Teager energy operator (TEO and Elman neural network. The TEO can estimate the total mechanical energy required to generate signals, thereby resulting in good time resolution and self-adaptability to transient signals. These attributes reflect the advantage of detecting signal impact characteristics. To detect the impact characteristics of the vibration signals of bearing faults, we used the TEO to extract the cyclical impact caused by bearing failure and applied the wavelet packet to reduce the noise of the Teager energy signal. This approach also enabled the extraction of bearing fault feature frequencies, which were identified using the fast Fourier transform of Teager energy. The feature frequencies of the inner and outer faults, as well as the ratio of resonance frequency band energy to total energy in the Teager spectrum, were extracted as feature vectors. In order to avoid a frequency leak error, the weighted Teager spectrum around the fault frequency was extracted as feature vector. These vectors were then used to train the Elman neural network and improve the robustness of the diagnostic algorithm. Experimental results indicate that the proposed approach effectively detects bearing faults under variable conditions.

  20. 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.

  1. 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.

  2. 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

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

    International Nuclear Information System (INIS)

    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. Study and Design of Diaphragm Pump Vibration Detection Fault Diagnosis System Based on FFT

    Directory of Open Access Journals (Sweden)

    Jia Yin

    2013-02-01

    Full Text Available This study has proposed a fault diagnosis system based on vibration detection. The system mainly includes four modules: signal acquisition module, signal processing module, state identification module, fault diagnosis and alarm module. The system uses CMSS 2200 acceleration sensor to collect vibration signals, processing spectrum with FFT (Fast Fourier Transform which is used effectively in current industry and finally achieve fault diagnosis and prediction for diaphragm pump. Through collection and analysis of the history signal data, set threshold value in the fault diagnosis system. According to the characteristics of different types, set the corresponding effective threshold value. The simulation results show that, the spectrum after FFT transformation processing, can really and effectively reflect equipment operating condition of the diaphragm. This system is not only simple and stable, but also can predict pump failure effectively, so that it reduces equipment downtime, plan maintenance time and unplanned maintenance time.

  5. 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. PMID:27038887

  6. Fault detection of excavator's hydraulic system based on dynamic principal component analysis

    Institute of Scientific and Technical Information of China (English)

    HE Qing-hua; HE Xiang-yu; ZHU Jian-xin

    2008-01-01

    In order to improve reliability of the excavator's hydraulic system, a fault detection approach based on dynamic principal component analysis(PCA) was proposed. Dynamic PCA is an extension of PCA, which can effectively extract the dynamic relations among process variables. With this approach, normal samples were used as training data to develop a dynamic PCA model in the first step. Secondly, the dynamic PCA model decomposed the testing data into projections to the principal component subspace(PCS) and residual subspace(RS). Thirdly, T2 statistic and Q statistic performed as indexes of fault detection in PCS and RS, respectively.Several simulated faults were introduced to validate the approach. The results show that the dynamic PCA model developed is able to detect overall faults by using T2 statistic and Q statistic. By simulation analysis, the proposed approach achieves an accuracy of 95% for 20 test sample sets, which shows that the fault detection approach can be effectively applied to the excavator's hydraulic system.

  7. 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.

  8. Model-based robust estimation and fault detection for MEMS-INS/GPS integrated navigation systems

    Institute of Scientific and Technical Information of China (English)

    Miao Lingjuan; Shi Jing

    2014-01-01

    In micro-electro-mechanical system based inertial navigation system (MEMS-INS)/global position system (GPS) integrated navigation systems, there exist unknown disturbances and abnormal measurements. In order to obtain high estimation accuracy and enhance detection sensitivity to faults in measurements, this paper deals with the problem of model-based robust esti-mation (RE) and fault detection (FD). A filter gain matrix and a post-filter are designed to obtain a RE and FD algorithm with current measurements, which is different from most of the existing pri-ori filters using measurements in one-step delay. With the designed filter gain matrix, the H-infinity norm of the transfer function from noise inputs to estimation error outputs is limited within a certain range;with the designed post-filter, the residual signal is robust to disturbances but sensitive to faults. Therefore, the algorithm can guarantee small estimation errors in the presence of distur-bances and have high sensitivity to faults. The proposed method is evaluated in an integrated navigation system, and the simulation results show that it is more effective in position estimation and fault signal detection than priori RE and FD algorithms.

  9. Application of Residual-Based EWMA Control Charts for Detecting Faults in Variable-Air-Volume Air Handling Unit System

    OpenAIRE

    Haitao Wang

    2016-01-01

    An online robust fault detection method is presented in this paper for VAV air handling unit and its implementation. Residual-based EWMA control chart is used to monitor the control processes of air handling unit and detect faults of air handling unit. In order to provide a level of robustness with respect to modeling errors, control limits are determined by incorporating time series model uncertainty in EWMA control chart. The fault detection method proposed was tested and validated using re...

  10. 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.

  11. Virtual Sensor Based Fault Detection and Classification on a Plasma Etch Reactor

    CERN Document Server

    Sofge, D A

    2007-01-01

    The SEMATECH sponsored J-88-E project teaming Texas Instruments with NeuroDyne (et al.) focused on Fault Detection and Classification (FDC) on a Lam 9600 aluminum plasma etch reactor, used in the process of semiconductor fabrication. Fault classification was accomplished by implementing a series of virtual sensor models which used data from real sensors (Lam Station sensors, Optical Emission Spectroscopy, and RF Monitoring) to predict recipe setpoints and wafer state characteristics. Fault detection and classification were performed by comparing predicted recipe and wafer state values with expected values. Models utilized include linear PLS, Polynomial PLS, and Neural Network PLS. Prediction of recipe setpoints based upon sensor data provides a capability for cross-checking that the machine is maintaining the desired setpoints. Wafer state characteristics such as Line Width Reduction and Remaining Oxide were estimated on-line using these same process sensors (Lam, OES, RFM). Wafer-to-wafer measurement of thes...

  12. An online tacholess order tracking technique based on generalized demodulation for rolling bearing fault detection

    Science.gov (United States)

    Wang, Yi; Xu, Guanghua; Luo, Ailing; Liang, Lin; Jiang, Kuosheng

    2016-04-01

    Vibration analysis has been proved to be an effective and powerful tool for the condition monitoring and fault diagnosis of rolling bearings. During the past decades, the conventional envelope analysis has been one of the main approaches in vibration signal processing. However, the envelope analysis is based on stationary assumption, thus it is not applicable to the fault diagnosis of bearings under rotating speed variation conditions. This constraint limits the bearing diagnosis in industrial applications. In recent years, order tracking methods based on time-frequency representation have been proposed for bearing fault detection under speed variation operating conditions. However, the methods are only applicable for offline bearing fault detection. Aiming at the shortcomings of the current tacholess order tracking techniques, an online tacholess order tracking method is proposed in this paper. The proposed method is on the basis of extracting the instantaneous tachometer information from the collected vibration signal itself continuously, and resampling the original signal with equal angle increment. The envelope order spectrum is used for bearing fault identification. The effectiveness of the proposed method has been validated by both simulated and experimental bearing vibration signals.

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

    International Nuclear Information System (INIS)

    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

  14. 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.

  15. Online Sensor Fault Detection Based on an Improved Strong Tracking Filter

    OpenAIRE

    Lijuan Wang; Lifeng Wu; Yong Guan; Guohui Wang

    2015-01-01

    We propose a method for online sensor fault detection that is based on the evolving Strong Tracking Filter (STCKF). The cubature rule is used to estimate states to improve the accuracy of making estimates in a nonlinear case. A residual is the difference in value between an estimated value and the true value. A residual will be regarded as a signal that includes fault information. The threshold is set at a reasonable level, and will be compared with residuals to determine whether or not the s...

  16. Network Power Fault Detection

    OpenAIRE

    Siviero, Claudio

    2013-01-01

    Network power fault detection. At least one first network device is instructed to temporarily disconnect from a power supply path of a network, and at least one characteristic of the power supply path of the network is measured at a second network device connected to the network while the at least one first network device is temporarily disconnected from the network

  17. Frequency based Wind Turbine Gearbox Fault Detection applied to a 750 kW Wind Turbine

    DEFF Research Database (Denmark)

    Odgaard, Peter Fogh; Nejad, Amir R.

    2014-01-01

    turbines. One of the critical components in modern wind turbines is the gearbox. Failures in the gearbox are costly both due to the cost of the gearbox itself, but also due to lost power generation during repair of it. Wind turbine gearboxes are consequently monitored by condition monitoring systems...... operating in parallel with the control system, and also uses additional sensors measuring different accelerations and noises, etc. In this paper gearbox data from high fidelity gearbox model of a 750 kW wind turbine gearbox, simulated with and without faults are used to shown the potential of frequency...... based detection schemes applied on measurements normally available in a wind controller system. This paper shows that two given faults in the gearbox can be detected using a frequency based detection approach applied to sensor signals normally available in the wind turbine control system. This means...

  18. Simultaneous State and Parameter Estimation Based Actuator Fault Detection and Diagnosis for an Unmanned Helicopter

    Directory of Open Access Journals (Sweden)

    Wu Chong

    2015-03-01

    Full Text Available Simultaneous state and parameter estimation based actuator fault detection and diagnosis (FDD for single-rotor unmanned helicopters (UHs is investigated in this paper. A literature review of actuator FDD for UHs is given firstly. Based on actuator healthy coefficients (AHCs, which are introduced to represent actuator faults, a combined dynamic model is established with the augmented state containing both the flight state and AHCs. Then the actuator fault detection and diagnosis problem is transformed into a general nonlinear estimation one: given control inputs and the measured flight state contaminated by measurement noises, estimate both the flight state and AHCs recursively in each time-step, which is also known as the simultaneous state and parameter estimation problem. The estimated AHCs can further be used for fault tolerant control (FTC. Based on the existing widely used nonlinear estimation methods such as the unscented Kalman filter (UKF and the extended set-membership filter (ESMF, three kinds of adaptive schemes (KF-UKF, MIT-UKF and MIT-ESMF are proposed by our team to improve the actuator FDD performance. A comprehensive comparative study on these different estimation methods is given in detail to illustrate their advantages and disadvantages when applied to unmanned helicopter actuator FDD.

  19. Sliding mode observer based incipient sensor fault detection with application to high-speed railway traction device.

    Science.gov (United States)

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

    2016-07-01

    This paper considers incipient sensor fault detection issue for a class of nonlinear systems with "observer unmatched" uncertainties. A particular fault detection sliding mode observer is designed for the augmented system formed by the original system and incipient sensor faults. The designed parameters are obtained using LMI and line filter techniques to guarantee that the generated residuals are robust to uncertainties and that sliding motion is not destroyed by faults. Then, three levels of novel adaptive thresholds are proposed based on the reduced order sliding mode dynamics, which effectively improve incipient sensor faults detectability. Case study of on the traction system in China Railway High-speed is presented to demonstrate the effectiveness of the proposed incipient senor faults detection schemes. PMID:27156675

  20. Sliding mode observer based incipient sensor fault detection with application to high-speed railway traction device.

    Science.gov (United States)

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

    2016-07-01

    This paper considers incipient sensor fault detection issue for a class of nonlinear systems with "observer unmatched" uncertainties. A particular fault detection sliding mode observer is designed for the augmented system formed by the original system and incipient sensor faults. The designed parameters are obtained using LMI and line filter techniques to guarantee that the generated residuals are robust to uncertainties and that sliding motion is not destroyed by faults. Then, three levels of novel adaptive thresholds are proposed based on the reduced order sliding mode dynamics, which effectively improve incipient sensor faults detectability. Case study of on the traction system in China Railway High-speed is presented to demonstrate the effectiveness of the proposed incipient senor faults detection schemes.

  1. 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.

  2. Fault Detection Based on Multi-Scale Local Binary Patterns Operator and Improved Teaching-Learning-Based Optimization Algorithm

    OpenAIRE

    Hongjian Zhang; Ping He; Xudong Yang

    2015-01-01

    Aiming to effectively recognize train center plate bolt loss faults, this paper presents an improved fault detection method. A multi-scale local binary pattern operator containing the local texture information of different radii is designed to extract more efficient discrimination information. An improved teaching-learning-based optimization algorithm is established to optimize the classification results in the decision level. Two new phases including the worst recombination phase and the cuc...

  3. 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...

  4. 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.

  5. Information Based Fault Diagnosis

    DEFF Research Database (Denmark)

    Niemann, Hans Henrik; Poulsen, Niels Kjølstad

    2008-01-01

    Fault detection and isolation, (FDI) of parametric faults in dynamic systems will be considered in this paper. An active fault diagnosis (AFD) approach is applied. The fault diagnosis will be investigated with respect to different information levels from the external inputs to the systems...

  6. An underwater ship fault detection method based on Sonar image processing

    Science.gov (United States)

    Hong, Shi; Fang-jian, Shan; Bo, Cong; Wei, Qiu

    2016-02-01

    For the research of underwater ship fault detection method in conditions of sailing on the ocean especially in poor visibility muddy sea, a fault detection method under the assist of sonar image processing was proposed. Firstly, did sonar image denoising using the algorithm of pulse coupled neural network (PCNN); secondly, edge feature extraction for the image after denoising was carried out by morphological wavelet transform; Finally, interested regions Using relevant tracking method were taken, namely fault area mapping. The simulation results presented here proved the feasibility and effectiveness of the sonar image processing in underwater fault detection system.

  7. 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.

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

    Directory of Open Access Journals (Sweden)

    Bingyong Yan

    2015-01-01

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

  9. 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.

  10. 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)....

  11. Robust fault detection filter design

    Science.gov (United States)

    Douglas, Randal Kirk

    The detection filter is a specially tuned linear observer that forms the residual generation part of an analytical redundancy system designed for model-based fault detection and identification. The detection filter has an invariant state subspace structure that produces a residual with known and fixed directional characteristics in response to a known design fault direction. In addition to a parameterization of the detection filter gain, three methods are given for improving performance in the presence of system disturbances, sensor noise, model mismatch and sensitivity to small parameter variations. First, it is shown that by solving a modified algebraic Riccati equation, a stabilizing detection filter gain is found that bounds the H-infinity norm of the transfer matrix from system disturbances and sensor noise to the detection filter residual. Second, a specially chosen expanded-order detection filter is formed with fault detection properties identical to a set of independent reduced-order filters that have no structural constraints. This result is important to the practitioner because the difficult problem of finding a detection filter insensitive to disturbances and sensor noise is converted to the easier problem of finding a set of uncoupled noise insensitive filters. Furthermore, the statistical properties of the reduced-order filter residuals are easier to find than the statistical properties of the structurally constrained detection filter residual. Third, an interpretation of the detection filter as a special case of the dual of the restricted decoupling problem leads to a new detection filter eigenstructure assignment algorithm. The new algorithm places detection filter left eigenvectors, which annihilate the detection spaces, rather than right eigenvectors, which span the detection spaces. This allows for a more flexible observer based fault detection system structure that could not be formulated as a detection filter. Furthermore, the link to the dual

  12. 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.

  13. 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.

  14. 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.

  15. Fault detection in non-linear systems based on type-2 fuzzy logic

    Science.gov (United States)

    Safarinejadian, Behrooz; Ghane, Parisa; Monirvaghefi, Hossein

    2015-02-01

    This paper presents a new method for fault detection (FD) based on interval type-2 fuzzy sets. The main idea is based on a confident span using interval type-2 fuzzy systems. An estimate for upper and lower bounds of output has been taken using the designing of an optimal fuzzy system through clustering. Finally the method has been tested in two non-linear systems, a two-tank with a fluid flow and pH neutralisation process, and it is compared with a well-known method named ANFIS. Furthermore, the mathematical model and the results of simulations prove the effectiveness, usefulness and applications of our new method.

  16. 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.

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

    International Nuclear Information System (INIS)

    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

  18. 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...

  19. 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.

  20. Automatic learning of state machines for fault detection systems in discrete event based distributed systems

    OpenAIRE

    Neuner, Oliver

    2011-01-01

    The electronic components in modern automobiles build up a distributed system with so called electronic control units connected via bus systems. As more safety- and security-relevant functions are implemented in such systems, the more important fault detection becomes. A promising approach to fault detection is to build a system model from state machines and compare its predictions with properties observed in a real system. In the automobile, potential are communication characteristics betwee...

  1. Fault Detection in Complex Distribution Network Based on Hilbert-Huang Transform

    Directory of Open Access Journals (Sweden)

    Zhongjian Kang

    2013-01-01

    Full Text Available Traditional distribution network fault location methods often cannot be effectively applied for the structure of the branch in complex distribution network. A new accurate fault location for the single-phase-ground fault in complex distribution network with structure of the branch based on Hilbert-Huang transform was proposed in this paper. First, the distribution network was modeled. The faults on each branch were simulated. The energy characteristics under the branch in a particular frequency band were identified by HHT. Then these energy characteristics were used to train artificial neural networks (ANN.When the energy characteristics of actual fault are inputted, the trained neural network can output the malfunction branch. When the fault branch was determined, using the online fault feature matching method, combined with the genetic algorithm, the precise determination of the distance to fault location in the fault branch can be completed. With combinations of signal processing-Hilbert-Huang transform, artificial neural network and genetic algorithm, the entirely new method was proposed to deal with the problem of fault location in distribution network in this article. The results showed that the method has a good precision and apply to the small current grounding system.

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

    Science.gov (United States)

    Kong, Young Bae; Lee, Eun Je; Hur, Min Goo; Park, Jeong Hoon; Park, Yong Dae; Yang, Seung Dae

    2016-10-01

    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.

  3. Fault diagnosis based on controller modification

    DEFF Research Database (Denmark)

    Niemann, Hans Henrik

    2015-01-01

    faults can be detected and isolated using active methods, where an auxiliary input is applied. Using active methods for the diagnosis of parametric faults in closed-loop systems, the amplitude of the applied auxiliary input need to be increased to be able to detect and isolate the faults in a reasonable...... with both fault detection and isolation will be discussed. Also passive fault diagnosis methods based on controller modification will be discussed...

  4. A new statistical modeling and detection method for rolling element bearing faults based on alpha-stable distribution

    Science.gov (United States)

    Yu, Gang; Li, Changning; Zhang, Jianfeng

    2013-12-01

    Due to limited information given by traditional local statistics, a new statistical modeling method for rolling element bearing fault signals is proposed based on alpha-stable distribution. In order to fully take advantages of complete information provided by alpha-stable distribution, this paper focuses on testing the validity of the proposed statistical model. A number of hypothetical test methods were applied to practical bearing fault vibration signals with different fault types and degrees. Through testing on the consistency of three alpha-stable parameter estimation methods, and the probability density function fitting level between fault signals and their corresponding hypothetical alpha-stable distributions, it can be concluded that such a non-Gaussian model is sufficient to thoroughly describe the statistical characteristics of bearing fault signals with impulsive behaviors, and consequently the alpha-stable hypothesis is verified. In the meantime, a new bearing fault detection method based on kurtogram and α parameter of the alpha-stable model is proposed, experimental results have shown that the proposed method has better performance on detecting incipient bearing faults than that based on the traditional kurtogram.

  5. 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.

  6. Cluster-based and cellular approach to fault detection and recovery in wireless sensor network

    Directory of Open Access Journals (Sweden)

    Abolfazl Akbari

    2010-02-01

    Full Text Available In the past few years wireless sensor networks have received a greater interest in application such as disaster management, border protection, combat field reconnaissance, and security surveillance. Sensor nodes are expected to operate autonomously in unattended environments and potentially in large numbers. Failures are inevitable in wireless sensor networks due to inhospitable environment and unattended deployment. The data communication and various network operations cause energy depletion in sensor nodes and therefore, it is common for sensor nodes to exhaust its energy completely and stop operating. This may cause connectivity and data loss. Therefore, it is necessary that network failures aredetected in advance and appropriate measures are taken to sustain network operation. In this paper we survey cellular architecture and cluster-based to sustain network operation in the event of failure cause of energy-drained nodes. The failure detection and recovery technique recovers the cluster structure in less than one-fourth of the time taken by the Gupta algorithm and is also proven to be 70% more energyefficient than the same. The cluster-based failure detection and recovery scheme proves to be an efficient and quick solution to robust and scalable sensor network for long and sustained operation. In cellular architecture the network is partitioned into a virtual grid of cells to perform fault detection and recoverylocally with minimum energy consumption. Fault detection and recovery in a distributed manner allows the failure report to be forwarded across cells. Also this algorithm has been compared with some existingrelated work and proven to be more energy efficient.

  7. 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....

  8. A Robust Fault Detection and Isolation Scheme Based on Unknown Input Observers for Discrete Time-delay System with Disturbance

    Institute of Scientific and Technical Information of China (English)

    WANG Hong-yu; TIAN Zuo-hua; SHI Song-jiao; WENG Zheng-xin

    2008-01-01

    This paper proposes a robust fault detection and isolation (FDI) scheme for discrete time-delay system with disturbance. The FDI scheme can not only detect but also isolate the faults. The lifting method is exploited to transform the discrete time-delay system into the non-time-delay form. A generalized structured residual set is designed based on the unknown input observer (UIO). For each residual generator, one of the system input signals together with the corresponding actuator fault and the disturbance signals are treated as an unknown input term. The residual signals can not only be robust against the disturbance, but also be of the capacity to isolate the actuator faults. The proposed method has been verified by a numerical example.

  9. Fault Detection Based on Multi-Scale Local Binary Patterns Operator and Improved Teaching-Learning-Based Optimization Algorithm

    Directory of Open Access Journals (Sweden)

    Hongjian Zhang

    2015-09-01

    Full Text Available Aiming to effectively recognize train center plate bolt loss faults, this paper presents an improved fault detection method. A multi-scale local binary pattern operator containing the local texture information of different radii is designed to extract more efficient discrimination information. An improved teaching-learning-based optimization algorithm is established to optimize the classification results in the decision level. Two new phases including the worst recombination phase and the cuckoo search phase are incorporated to improve the diversity of the population and enhance the exploration. In the worst recombination phase, the worst solution is updated by a crossover recombination operation to prevent the premature convergence. The cuckoo search phase is adopted to escape the local optima. Experimental results indicate that the recognition accuracy is up to 98.9% which strongly demonstrates the effectiveness and reliability of the proposed detection method.

  10. 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.

  11. Design of a novel knowledge-based fault detection and isolation scheme.

    Science.gov (United States)

    Zhao, Qing; Xu, Zhihan

    2004-04-01

    In this paper, a real-time fault detection and isolation (FDI) scheme for dynamical systems is developed, by integrating the signal processing technique with neural network design. Wavelet analysis is applied to capture the fault-induced transients of the measured signals in real-time, and the decomposed signals are pre-processed to extract details about a fault. A Regional Self-Organizing feature Map (R-SOM) neural network is synthesized to classify the fault types. The R-SOM neural network adopts two regions adjustment in the learning algorithm, thus it has high precision in clustering and matching, especially when the noise, disturbance and other uncertainties exist in the systems. As a result, the proposed FDI scheme is robust and accurate. The design is implemented on a stirred tank system and satisfactory online testing results are obtained.

  12. 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

  13. Fault Detection and Isolation using Eigenstructure Assignment

    DEFF Research Database (Denmark)

    Jørgensen, R.B.; Patton, R.J.; 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....

  14. Dynamic Neural Network-Based Pulsed Plasma Thruster (PPT) Fault Detection and Isolation for Formation Flying of Satellites

    Science.gov (United States)

    Valdes, A.; Khorasani, K.

    The main objective of this paper is to develop a dynamic neural network-based fault detection and isolation (FDI) scheme for the Pulsed Plasma Thrusters (PPTs) that are used in the Attitude Control Subsystem (ACS) of satellites that are tasked to perform a formation flying mission. By using data collected from the relative attitudes of the formation flying satellites our proposed "High Level" FDI scheme can detect the pair of thrusters which is faulty, however fault isolation cannot be accomplished. Based on the "High Level" FDI scheme and the DNN-based "Low Level" FDI scheme developed earlier by the authors, an "Integrated" DNN-based FDI scheme is then proposed. To demonstrate the FDI capabilities of the proposed schemes various fault scenarios are simulated.

  15. Compound faults detection of rolling element bearing based on the generalized demodulation algorithm under time-varying rotational speed

    Science.gov (United States)

    Zhao, Dezun; Li, Jianyong; Cheng, Weidong; Wen, Weigang

    2016-09-01

    Multi-fault detection of the rolling element bearing under time-varying rotational speed presents a challenging issue due to its complexity, disproportion and interaction. Computed order analysis (COA) is one of the most effective approaches to remove the influences of speed fluctuation, and detect all the features of multi-fault. However, many interference components in the envelope order spectrum may lead to false diagnosis results, in addition, the deficiencies of computational accuracy and efficiency also cannot be neglected. To address these issues, a novel method for compound faults detection of rolling element bearing based on the generalized demodulation (GD) algorithm is proposed in this paper. The main idea of the proposed method is to exploit the unique property of the generalized demodulation algorithm in transforming an interested instantaneous frequency trajectory of compound faults bearing signal into a line paralleling to the time axis, and then the FFT algorithm can be directly applied to the transformed signal. This novel method does not need angular resampling algorithm which is the key step of the computed order analysis, and is hence free from the deficiencies of computational error and efficiency. On the other hand, it only acts on the instantaneous fault characteristic frequency trends in envelope signal of multi-fault bearing which include rich fault information, and is hence free from irrelevant items interferences. Both simulated and experimental faulty bearing signal analysis validate that the proposed method is effective and reliable on the compound faults detection of rolling element bearing under variable rotational speed conditions. The comprehensive comparison with the computed order analysis further shows that the proposed method produces higher accurate results in less computation time.

  16. Spatiotemporal Correlation Based Fault-Tolerant Event Detection in Wireless Sensor Networks

    OpenAIRE

    Kezhong Liu; Yang Zhuang; Zhibo Wang; Jie Ma

    2015-01-01

    Reliable event detection is one of the most important objectives in wireless sensor networks (WSNs), especially in the presence of faulty nodes. Existing fault-tolerant event detection approaches usually take the probability of faulty nodes into account and fusion techniques to weaken the influence of faulty readings are usually developed. Through extensive experiments, we discover a phenomenon that event detection accuracy degrades quickly when the faulty sensors ratio reaches a critical val...

  17. 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.

  18. A Mode-Shape-Based Fault Detection Methodology for Cantilever Beams

    Science.gov (United States)

    Tejada, Arturo

    2009-01-01

    An important goal of NASA's Internal Vehicle Health Management program (IVHM) is to develop and verify methods and technologies for fault detection in critical airframe structures. A particularly promising new technology under development at NASA Langley Research Center is distributed Bragg fiber optic strain sensors. These sensors can be embedded in, for instance, aircraft wings to continuously monitor surface strain during flight. Strain information can then be used in conjunction with well-known vibrational techniques to detect faults due to changes in the wing's physical parameters or to the presence of incipient cracks. To verify the benefits of this technology, the Formal Methods Group at NASA LaRC has proposed the use of formal verification tools such as PVS. The verification process, however, requires knowledge of the physics and mathematics of the vibrational techniques and a clear understanding of the particular fault detection methodology. This report presents a succinct review of the physical principles behind the modeling of vibrating structures such as cantilever beams (the natural model of a wing). It also reviews two different classes of fault detection techniques and proposes a particular detection method for cracks in wings, which is amenable to formal verification. A prototype implementation of these methods using Matlab scripts is also described and is related to the fundamental theoretical concepts.

  19. Fault detection and diagnosis of permanent-magnetic DC motors based on current analysis and BP neural networks

    Institute of Scientific and Technical Information of China (English)

    LIU Man-lan; ZHU Chun-bo; WANG Tie-cheng

    2005-01-01

    In order to guarantee quality during mass serial production of motors, a convenient approach on how to detect and diagnose the faults of a permanent-magnetic DC motor based on armature current analysis and BP neural networks was presented in this paper. The fault feature vector was directly established by analyzing the armature current. Fault features were extracted from the current using various signal processing methods including Fourier analysis, wavelet analysis and statistical methods. Then an advanced BP neural network was used to finish decision-making and separate fault patterns. Finally, the accuracy of the method in this paper was verified by analyzing the mechanism of faults theoretically. The consistency between the experimental results and the theoretical analysis shows that four kinds of representative faults of low power permanent-magnetic DC motors can be diagnosed conveniently by this method. These four faults are brush fray, open circuit of components, open weld of components and short circuit between armature coils. This method needs fewer hardware instruments than the conventional method and whole procedures can be accomplished by several software packages developed in this paper.

  20. 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 ...

  1. Linear discriminant analysis for welding fault detection

    International Nuclear Information System (INIS)

    This work presents a new method for real time welding fault detection in industry based on Linear Discriminant Analysis (LDA). A set of parameters was calculated from one second blocks of electrical data recorded during welding and based on control data from reference welds under good conditions, as well as faulty welds. Optimised linear combinations of the parameters were determined with LDA and tested with independent data. Short arc welds in overlap joints were studied with various power sources, shielding gases, wire diameters, and process geometries. Out-of-position faults were investigated. Application of LDA fault detection to a broad range of welding procedures was investigated using a similarity measure based on Principal Component Analysis. The measure determines which reference data are most similar to a given industrial procedure and the appropriate LDA weights are then employed. Overall, results show that Linear Discriminant Analysis gives an effective and consistent performance in real-time welding fault detection.

  2. A Model-free Approach to Fault Detection of Continuous-time Systems Based on Time Domain Data

    Institute of Scientific and Technical Information of China (English)

    Ping Zhang; Steven X. Ding

    2007-01-01

    In this paper, a model-free approach is presented to design an observer-based fault detection system of linear continuoustime systems based on input and output data in the time domain. The core of the approach is to directly identify parameters of the observer-based residual generator based on a numerically reliable data equation obtained by filtering and sampling the input and output signals.

  3. Performance Analysis of Faults Detection in Wind Turbine Generator Based on High-Resolution Frequency Estimation Methods

    Directory of Open Access Journals (Sweden)

    CHAKKOR SAAD

    2014-05-01

    Full Text Available Electrical energy production based on wind power has become the most popular renewable resources in the recent years because it gets reliable clean energy with minimum cost. The major challenge for wind turbines is the electrical and the mechanical failures which can occur at any time causing prospective breakdowns and damages and therefore it leads to machine downtimes and to energy production loss. To circumvent this problem, several tools and techniques have been developed and used to enhance fault detection and diagnosis to be found in the stator current signature for wind turbines generators. Among these methods, parametric or super-resolution frequency estimation methods, which provides typical spectrum estimation, can be useful for this purpose. Facing on the plurality of these algorithms, a comparative performance analysis is made to evaluate robustness based on differents metrics: accuracy, dispersion, computation cost, perturbations and faults severity. Finally, simulation results in Matlab with most occurring faults indicate that ESPRIT and R-MUSIC algorithms have high capability of correctly identifying the frequencies of fault characteristic components, a performance ranking had been carried out to demonstrate the efficiency of the studied methods in faults detecting.

  4. A microprocessor-based digital feeder monitor with high-impedance fault detection

    Energy Technology Data Exchange (ETDEWEB)

    Patterson, R.; Tyska, W. [GE Protection and Control, Malvern, PA (United States); Russell, B.D. [Texas A& M Univ., College Station, TX (United States)] [and others

    1994-12-31

    The high impedance fault detection technology developed at Texas A&M University after more than a decade of research, funded in large part by the Electric Power Research Institute, has been incorporated into a comprehensive monitoring device for overhead distribution feeders. This digital feeder monitor (DFM) uses a high waveform sampling rate for the ac current and voltage inputs in conjunction with a high-performance reduced instruction set (RISC) microprocessor to obtain the frequency response required for arcing fault detection and power quality measurements. Expert system techniques are employed to assure security while maintaining dependability. The DFM is intended to be applied at a distribution substation to monitor one feeder. The DFM is packaged in a non-drawout case which fits the panel cutout for a GE IAC overcurrent relay to facilitate retrofits at the majority of sites were electromechanical overcurrent relays already exist.

  5. 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.

  6. 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...

  7. Multi-Sensor Data Fusion Using a Relevance Vector Machine Based on an Ant Colony for Gearbox Fault Detection.

    Science.gov (United States)

    Liu, Zhiwen; Guo, Wei; Tang, Zhangchun; Chen, Yongqiang

    2015-01-01

    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). PMID:26334280

  8. 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.

  9. 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.

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

    OpenAIRE

    Li Sun; Junyi Dong; Donghai Li; Yuqiong Zhang

    2014-01-01

    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 d...

  11. Fault detection and fault-tolerant control using sliding modes

    CERN Document Server

    Alwi, Halim; Tan, Chee Pin

    2011-01-01

    ""Fault Detection and Fault-tolerant Control Using Sliding Modes"" is the first text dedicated to showing the latest developments in the use of sliding-mode concepts for fault detection and isolation (FDI) and fault-tolerant control in dynamical engineering systems. It begins with an introduction to the basic concepts of sliding modes to provide a background to the field. This is followed by chapters that describe the use and design of sliding-mode observers for FDI using robust fault reconstruction. The development of a class of sliding-mode observers is described from first principles throug

  12. Fault Detection and Diagnosis for Gas Turbines Based on a Kernelized Information Entropy Model

    Directory of Open Access Journals (Sweden)

    Weiying Wang

    2014-01-01

    Full Text Available Gas turbines are considered as one kind of the most important devices in power engineering and have been widely used in power generation, airplanes, and naval ships and also in oil drilling platforms. However, they are monitored without man on duty in the most cases. It is highly desirable to develop techniques and systems to remotely monitor their conditions and analyze their faults. In this work, we introduce a remote system for online condition monitoring and fault diagnosis of gas turbine on offshore oil well drilling platforms based on a kernelized information entropy model. Shannon information entropy is generalized for measuring the uniformity of exhaust temperatures, which reflect the overall states of the gas paths of gas turbine. In addition, we also extend the entropy to compute the information quantity of features in kernel spaces, which help to select the informative features for a certain recognition task. Finally, we introduce the information entropy based decision tree algorithm to extract rules from fault samples. The experiments on some real-world data show the effectiveness of the proposed algorithms.

  13. Fault detection and diagnosis for gas turbines based on a kernelized information entropy model.

    Science.gov (United States)

    Wang, Weiying; Xu, Zhiqiang; Tang, Rui; Li, Shuying; Wu, Wei

    2014-01-01

    Gas turbines are considered as one kind of the most important devices in power engineering and have been widely used in power generation, airplanes, and naval ships and also in oil drilling platforms. However, they are monitored without man on duty in the most cases. It is highly desirable to develop techniques and systems to remotely monitor their conditions and analyze their faults. In this work, we introduce a remote system for online condition monitoring and fault diagnosis of gas turbine on offshore oil well drilling platforms based on a kernelized information entropy model. Shannon information entropy is generalized for measuring the uniformity of exhaust temperatures, which reflect the overall states of the gas paths of gas turbine. In addition, we also extend the entropy to compute the information quantity of features in kernel spaces, which help to select the informative features for a certain recognition task. Finally, we introduce the information entropy based decision tree algorithm to extract rules from fault samples. The experiments on some real-world data show the effectiveness of the proposed algorithms. PMID:25258726

  14. Fault detection and diagnosis for gas turbines based on a kernelized information entropy model.

    Science.gov (United States)

    Wang, Weiying; Xu, Zhiqiang; Tang, Rui; Li, Shuying; Wu, Wei

    2014-01-01

    Gas turbines are considered as one kind of the most important devices in power engineering and have been widely used in power generation, airplanes, and naval ships and also in oil drilling platforms. However, they are monitored without man on duty in the most cases. It is highly desirable to develop techniques and systems to remotely monitor their conditions and analyze their faults. In this work, we introduce a remote system for online condition monitoring and fault diagnosis of gas turbine on offshore oil well drilling platforms based on a kernelized information entropy model. Shannon information entropy is generalized for measuring the uniformity of exhaust temperatures, which reflect the overall states of the gas paths of gas turbine. In addition, we also extend the entropy to compute the information quantity of features in kernel spaces, which help to select the informative features for a certain recognition task. Finally, we introduce the information entropy based decision tree algorithm to extract rules from fault samples. The experiments on some real-world data show the effectiveness of the proposed algorithms.

  15. 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.

  16. Fault detection of flywheel system based on clustering and principal component analysis

    Institute of Scientific and Technical Information of China (English)

    Wang Rixin; Gong Xuebing; Xu Minqiang; Li Yuqing

    2015-01-01

    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 rela-tionship 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.

  17. Online Distributed Fault Detection of Sensor Measurements

    Institute of Scientific and Technical Information of China (English)

    GAO Jianliang; XU Yongjun; LI Xiaowei

    2007-01-01

    In wireless sensor networks (WSNs), a faulty sensor may produce incorrect data and transmit them to the other sensors. This would consume the limited energy and bandwidth of WSNs. Furthermore, the base station may make inappropriate decisions when it receives the incorrect data sent by the faulty sensors. To solve these problems, this paper develops an online distributed algorithm to detect such faults by exploring the weighted majority vote scheme. Considering the spatial correlations in WSNs, a faulty sensor can diagnose itself through utilizing the spatial and time information provided by its neighbor sensors. Simulation results show that even when as many as 30% of the sensors are faulty, over 95% of faults can be correctly detected with our algorithm. These results indicate that the proposed algorithm has excellent performance in detecting fault of sensor measurements in WSNs.

  18. Fault Detection under Fuzzy Model Uncertainty

    Institute of Scientific and Technical Information of China (English)

    Marek Kowal; Józef Korbicz

    2007-01-01

    The paper tackles the problem of robust fault detection using Takagi-Sugeno fuzzy models. A model-based strategy is employed to generate residuals in order to make a decision about the state of the process. Unfortunately, such a method is corrupted by model uncertainty due to the fact that in real applications there exists a model-reality mismatch. In order to ensure reliable fault detection the adaptive threshold technique is used to deal with the mentioned problem. The paper focuses also on fuzzy model design procedure. The bounded-error approach is applied to generating the rules for the model using available measurements. The proposed approach is applied to fault detection in the DC laboratory engine.

  19. Simulation Research of Fault Model of Detecting Rotor Dynamic Eccentricity in Brushless DC Motor Based on Motor Current Signature Analysis

    Institute of Scientific and Technical Information of China (English)

    2011-01-01

    The Brushless Direct Current (BLDC) motor is widely used in aerospace area, CNC machines and servo systems that require the high control accuracy Once the faults occur in the motor, it will cause great damage to the whole system. Mechanical faults are common in electric machines, and account for up to 50%-60% of the faults. Approximately, 80% of the mechanical faults lead to the eccentricity. So it is necessary to monitor the health condition of the motor to ensure the faults can be detected earlier and measures will be taken to imorove the reliability.

  20. 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.

  1. 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....

  2. Undestructive fault detection of power transformers through the transfer function

    International Nuclear Information System (INIS)

    In this paper, we present a new undestructive fault detection method for power transformers. The proposed method is based on changes of the pattern of transfer functions according to various faults of power transformers. While the sweep signal is put into the secondary side of the transformer, we measure currents and voltages on the primary and secondary sides. The transfer function founded by the signal processing of measured signals is compared so that how it deviate from the normal transfer function. Based on these data, the fault detection algorithm determine in which phase the faults occur and identify the type of faults by calculating the change of the transfer function.

  3. Fundamental problems in fault detection and identification

    DEFF Research Database (Denmark)

    Saberi, A.; Stoorvogel, A. A.; Sannuti, P.;

    2000-01-01

    A number of different fundamental problems in fault detection and fault identification are formulated in this paper. The fundamental problems include exact, almost, generic and class-wise fault detection and identification. Necessary and sufficient conditions for the solvability of the fundamental...

  4. 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.

  5. Fault Detection and Location by Static Switches in Microgrids Using Wavelet Transform and Adaptive Network-Based Fuzzy Inference System

    Directory of Open Access Journals (Sweden)

    Ying-Yi Hong

    2014-04-01

    Full Text Available Microgrids are a highly efficient means of embedding distributed generation sources in a power system. However, if a fault occurs inside or outside the microgrid, the microgrid should be immediately disconnected from the main grid using a static switch installed at the secondary side of the main transformer near the point of common coupling (PCC. The static switch should have a reliable module implemented in a chip to detect/locate the fault and activate the breaker to open the circuit immediately. This paper proposes a novel approach to design this module in a static switch using the discrete wavelet transform (DWT and adaptive network-based fuzzy inference system (ANFIS. The wavelet coefficient of the fault voltage and the inference results of ANFIS with the wavelet energy of the fault current at the secondary side of the main transformer determine the control action (open or close of a static switch. The ANFIS identifies the faulty zones inside or outside the microgrid. The proposed method is applied to the first outdoor microgrid test bed in Taiwan, with a generation capacity of 360.5 kW. This microgrid test bed is studied using the real-time simulator eMegaSim developed by Opal-RT Technology Inc. (Montreal, QC, Canada. The proposed method based on DWT and ANFIS is implemented in a field programmable gate array (FPGA by using the Xilinx System Generator. Simulation results reveal that the proposed method is efficient and applicable in the real-time control environment of a power system.

  6. Norm based Threshold Selection for Fault Detectors

    DEFF Research Database (Denmark)

    Rank, Mike Lind; Niemann, Henrik

    1998-01-01

    The design of fault detectors for fault detection and isolation (FDI) in dynamic systems is considered from a norm based point of view. An analysis of norm based threshold selection is given based on different formulations of FDI problems. Both the nominal FDI problem as well as the uncertain FDI...... problem are considered. Based on this analysis, a performance index based on norms of the involved transfer functions is given. The performance index allows us also to optimize the structure of the fault detection filter directly...

  7. Hybrid fault tolerance techniques to detect transient faults in embedded processors

    CERN Document Server

    Azambuja, José Rodrigo; Becker, Jürgen

    2014-01-01

    This book describes fault tolerance techniques based on software and hardware to create hybrid techniques. They are able to reduce overall performance degradation and increase error detection when associated with applications implemented in embedded processors. Coverage begins with an extensive discussion of the current state-of-the-art in fault tolerance techniques. The authors then discuss the best trade-off between software-based and hardware-based techniques and introduce novel hybrid techniques. Proposed techniques increase existing fault detection rates up to 100%, while maintaining low performance overheads in area and application execution time. • Discusses the effects of radiation on modern integrated circuits; • Provides a comprehensive overview of state-of-the art fault tolerance techniques based on software, hardware, and hybrid techniques; • Introduces novel hybrid fault tolerance techniques for reconfigurable FPGAs and ASICs; • Performs fault injection campaigns by simulation, bitstream ...

  8. Field testing of component-level model-based fault detection methods for mixing boxes and VAV fan systems

    Energy Technology Data Exchange (ETDEWEB)

    Xu, Peng; Haves, Philip

    2002-05-16

    An automated fault detection and diagnosis tool for HVAC systems is being developed, based on an integrated, life-cycle, approach to commissioning and performance monitoring. The tool uses component-level HVAC equipment models implemented in the SPARK equation-based simulation environment. The models are configured using design information and component manufacturers' data and then fine-tuned to match the actual performance of the equipment by using data measured during functional tests of the sort using in commissioning. This paper presents the results of field tests of mixing box and VAV fan system models in an experimental facility and a commercial office building. The models were found to be capable of representing the performance of correctly operating mixing box and VAV fan systems and detecting several types of incorrect operation.

  9. Detection of weak transient signals based on wavelet packet transform and manifold learning for rolling element bearing fault diagnosis

    Science.gov (United States)

    Wang, Yi; Xu, Guanghua; Liang, Lin; Jiang, Kuosheng

    2015-03-01

    The kurtogram-based methods have been proved powerful and practical to detect and characterize transient components in a signal. The basic idea of the kurtogram-based methods is to use the kurtosis as a measure to discover the presence of transient impulse components and to indicate the frequency band where these occur. However, the performance of the kurtogram-based methods is poor due to the low signal-to-noise ratio. As the weak transient signal with a wide spread frequency band can be easily masked by noise. Besides, selecting signal just in one frequency band will leave out some transient features. Aiming at these shortcomings, different frequency bands signal fusion is adopted in this paper. Considering that manifold learning aims at discovering the nonlinear intrinsic structure which embedded in high dimensional data, this paper proposes a waveform feature manifold (WFM) method to extract the weak signature from waveform feature space which obtained by binary wavelet packet transform. Minimum permutation entropy is used to select the optimal parameter in a manifold learning algorithm. A simulated bearing fault signal and two real bearing fault signals are used to validate the improved performance of the proposed method through the comparison with the kurtogram-based methods. The results show that the proposed method outperforms the kurtogram-based methods and is effective in weak signature extraction.

  10. 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....

  11. 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...

  12. A Fault Detection and Isolation Scheme Based on Parity Space Method for Discrete Time-delay System

    Institute of Scientific and Technical Information of China (English)

    WANG Hong-yu; TIAN Zuo-hua; SHI Song-jiao; WENG Zheng-xin

    2008-01-01

    A Fault detection and isolation (FDI) scheme for discrete time-delay system is proposed in this paper, which can not only detect but also isolate the faults. A time delay operator ▽ is introduced to resolve the problem brought by the time-delay system. The design and computation for the FDI system is carried by computer math tool Maple, which can easily deal with the symbolic computation. Residuals in the form of parity space can be deduced from the recursion of the system equations. Further mote, a generalized residual set is created using the freedom of the parity space redundancy. Thus, both fault detection and fault isolation have been accomplished. The proposed method has been verified by a numerical example.

  13. Kalman Filter Application to Symmetrical Fault Detection during Power Swing

    DEFF Research Database (Denmark)

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

    2016-01-01

    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 predicting capabi...

  14. Design of a multi-model observer-based estimator for Fault Detection and Isolation (FDI) strategy: application to a chemical reactor

    OpenAIRE

    Y. Chetouani

    2008-01-01

    This study presents a FDI strategy for nonlinear dynamic systems. It shows a methodology of tackling the fault detection and isolation issue by combining a technique based on the residuals signal and a technique using the multiple Kalman filters. The usefulness of this combination is the on-line implementation of the set of models, which represents the normal mode and all dynamics of faults, if the statistical decision threshold on the residuals exceeds a fixed value. In other cases, one Exte...

  15. Detection of Bias, Drift, Freeze and Abrupt Sensor Failure using Intelligent Dedicated Observer Based Fault Detection and Isolation for Three Interacting Tank Process

    Directory of Open Access Journals (Sweden)

    C. Amritha

    2013-12-01

    Full Text Available This paper presents a design of MANFIS (MultipleAdaptive Neuro Fuzzy Inference System based sensor FaultDetection and Isolation (FDI scheme for a three interacting tanksystem. Three pairs of dedicated observers are designed toestimate the three states of the system. The observers designedare fuzzy systems whose optimal membership functions and rulebase are determined by neural networks. The difference betweenthe estimated and measured value is called as residuals.Decision functions are determined from the residuals. Thesefunctions are compared to a threshold value, when the value ofthese functions exceed a particular threshold, the presence offault is indicated. The FDI designed is implemented to detectsensor bias, abrupt sensor failure, sensor drift and sensor freezetypes of sensor faults.

  16. A new fault detection method for computer networks

    International Nuclear Information System (INIS)

    Over the past few years, fault detection for computer networks has attracted extensive attentions for its importance in network management. Most existing fault detection methods are based on active probing techniques which can detect the occurrence of faults fast and precisely. But these methods suffer from the limitation of traffic overhead, especially in large scale networks. To relieve traffic overhead induced by active probing based methods, a new fault detection method, whose key is to divide the detection process into multiple stages, is proposed in this paper. During each stage, only a small region of the network is detected by using a small set of probes. Meanwhile, it also ensures that the entire network can be covered after multiple detection stages. This method can guarantee that the traffic used by probes during each detection stage is small sufficiently so that the network can operate without severe disturbance from probes. Several simulation results verify the effectiveness of the proposed method

  17. An SVM-based solution for fault detection in wind turbines.

    Science.gov (United States)

    Santos, Pedro; Villa, Luisa F; Reñones, Aníbal; Bustillo, Andres; Maudes, Jesús

    2015-03-09

    Research into fault diagnosis in machines with a wide range of variable loads and speeds, such as wind turbines, is of great industrial interest. Analysis of the power signals emitted by wind turbines for the diagnosis of mechanical faults in their mechanical transmission chain is insufficient. A successful diagnosis requires the inclusion of accelerometers to evaluate vibrations. This work presents a multi-sensory system for fault diagnosis in wind turbines, combined with a data-mining solution for the classification of the operational state of the turbine. The selected sensors are accelerometers, in which vibration signals are processed using angular resampling techniques and electrical, torque and speed measurements. Support vector machines (SVMs) are selected for the classification task, including two traditional and two promising new kernels. This multi-sensory system has been validated on a test-bed that simulates the real conditions of wind turbines with two fault typologies: misalignment and imbalance. Comparison of SVM performance with the results of artificial neural networks (ANNs) shows that linear kernel SVM outperforms other kernels and ANNs in terms of accuracy, training and tuning times. The suitability and superior performance of linear SVM is also experimentally analyzed, to conclude that this data acquisition technique generates linearly separable datasets.

  18. 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

    of insulation degradation of one turn in the winding of a PMSWG. Cosimulation method by combining finite element model and external circuits is used. Hilbert–Huang transformation is applied to detect the very early stage fault in interturn insulation by analyzing the stator current. Detection results show...

  19. 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...

  20. Subsurface faults detection based on magnetic anomalies investigation: A field example at Taba protectorate, South Sinai

    Science.gov (United States)

    Khalil, Mohamed H.

    2016-08-01

    Quantitative interpretation of the magnetic data particularly in a complex dissected structure necessitates using of filtering techniques. In Taba protectorate, Sinai synthesis of different filtering algorithms was carried out to distinct and verifies the subsurface structure and estimates the depth of the causative magnetic sources. In order to separate the shallow-seated structure, filters of the vertical derivatives (VDR), Butterworth high-pass (BWHP), analytic signal (AS) amplitude, and total horizontal derivative of the tilt derivative (TDR_THDR) were conducted. While, filters of the apparent susceptibility and Butterworth low-pass (BWLP) were conducted to identify the deep-seated structure. The depths of the geological contacts and faults were calculated by the 3D Euler deconvolution. Noteworthy, TDR_THDR was independent of geomagnetic inclination, significantly less susceptible to noise, and more sensitive to the details of the shallow superimposed structures. Whereas, the BWLP proved high resolution capabilities in attenuating the shorter wavelength of the near surface anomalies and emphasizing the longer wavelength derived from deeper causative structure. 3D Euler deconvolution (SI = 0) was quite amenable to estimate the depths of superimposed subsurface structure. The pattern, location, and trend of the deduced shallow and deep faults were conformed remarkably to the addressed fault system.

  1. 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...

  2. 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...... faults in display cabinets under the wide operational conditions that display cabinets are exposed to. The approach described uses a non- linear parity equation comparing the heat transfer rates of the air and the refrigerant. The paper presents the detection method and discusses the application of the...

  3. 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...... 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....

  4. Designing Expert System for Detecting Faults in Cloud Environment

    Directory of Open Access Journals (Sweden)

    Marzieh Shabdiz

    2013-11-01

    Full Text Available Many fault detection techniques for detecting faults in rule bases system have appeared in the literature. These techniques assume that the rule base is static. This paper presents a new approach by designing Expert system for detecting faults in dynamic environment, such as cloud. Cloud resources are usually not only shared by multiple users but are also dynamically re-allocated per demand. Therefore, rules may be added/deleted in response to certain events happening in the integrated system being controlled by the rules. The approach makes use of spanning trees and Complementary sets to check a dynamic rule base for different kinds of faults underlying directed graph and devises a new method with scripting language on web based tools. This is performed as rules are being added to the dynamic rule base one at a time without the need to rebuild the structures and update rules and paths by expert system.

  5. A quantum annealing approach for fault detection and diagnosis of graph-based systems

    Science.gov (United States)

    Perdomo-Ortiz, A.; Fluegemann, J.; Narasimhan, S.; Biswas, R.; Smelyanskiy, V. N.

    2015-02-01

    Diagnosing the minimal set of faults capable of explaining a set of given observations, e.g., from sensor readouts, is a hard combinatorial optimization problem usually tackled with artificial intelligence techniques. We present the mapping of this combinatorial problem to quadratic unconstrained binary optimization (QUBO), and the experimental results of instances embedded onto a quantum annealing device with 509 quantum bits. Besides being the first time a quantum approach has been proposed for problems in the advanced diagnostics community, to the best of our knowledge this work is also the first research utilizing the route Problem → QUBO → Direct embedding into quantum hardware, where we are able to implement and tackle problem instances with sizes that go beyond previously reported toy-model proof-of-principle quantum annealing implementations; this is a significant leap in the solution of problems via direct-embedding adiabatic quantum optimization. We discuss some of the programmability challenges in the current generation of the quantum device as well as a few possible ways to extend this work to more complex arbitrary network graphs.

  6. A Novel Method for Adaptive Multiresonance Bands Detection Based on VMD and Using MTEO to Enhance Rolling Element Bearing Fault Diagnosis

    Directory of Open Access Journals (Sweden)

    Xingxing Jiang

    2016-01-01

    Full Text Available Vibration signals of the defect rolling element bearings are usually immersed in strong background noise, which make it difficult to detect the incipient bearing defect. In our paper, the adaptive detection of the multiresonance bands in vibration signal is firstly considered based on variational mode decomposition (VMD. As a consequence, the novel method for enhancing rolling element bearing fault diagnosis is proposed. Specifically, the method is conducted by the following three steps. First, the VMD is introduced to decompose the raw vibration signal. Second, the one or more modes with the information of fault-related impulses are selected through the kurtosis index. Third, Multiresolution Teager Energy Operator (MTEO is employed to extract the fault-related impulses hidden in the vibration signal and avoid the negative value phenomenon of Teager Energy Operator (TEO. Meanwhile, the physical meaning of MTEO is also discovered in this paper. In addition, an idea of combining the multiresonance bands is constructed to further enhance the fault-related impulses. The simulation studies and experimental verifications confirm that the proposed method is effective for identifying the multiresonance bands and enhancing rolling element bearing fault diagnosis by comparing with Hilbert transform, EMD-based demodulation, and fast Kurtogram analysis.

  7. Multivariate Principal Component Analysis and Case-Based Reasoning for monitoring, fault detection and diagnosis in a WWTP.

    Science.gov (United States)

    Ruiz, Magda; Sin, Gürkan; Berjaga, Xavier; Colprim, Jesús; Puig, Sebastià; Colomer, Joan

    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, MPCA is used to reduce the multi-dimensional nature of online process data, which summarises most of the variance of the process data in a few (new) variables. Next, the outputs of MPCA (t-scores, Q-statistic) are provided as inputs (descriptors) to the CBR method, which is employed to identify problems and propose appropriate solutions (hence diagnosis) based on previously stored cases. The methodology is evaluated on a pilot-scale SBR performing nitrogen, phosphorus and COD removal and to help to diagnose abnormal situations in the process operation. Finally, it is believed that the methodology is a promising tool for automatic diagnosis and real-time warning, which can be used for daily management of plant operation. PMID:22335109

  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. Qualitative Fault Detection and Hazard Analysis Based on Signed Directed Graphs for Large-Scale Complex Systems

    OpenAIRE

    Yang, Fan; Xiao, Deyun; Shah, Sirish L.

    2010-01-01

    In this chapter, after the introduction of the SDG concept and modeling methods, the inference approaches aiming at the fault detection and hazard analysis, especially the SDG description of control systems, have been analyzed from theory to practice. The classical control methods on the basis of feedback idea are in common use, so the modeling and analysis of the systems under these control methods, have been discussed. When a control system is transformed into an SDG model, the direction of...

  10. Chaotic Extension Neural Network Theory-Based XXY Stage Collision Fault Detection Using a Single Accelerometer Sensor

    OpenAIRE

    Chin-Tsung Hsieh; Her-Terng Yau; Shang-Yi Wu; Huo-Cheng Lin

    2014-01-01

    The collision fault detection of a XXY stage is proposed for the first time in this paper. The stage characteristic signals are extracted and imported into the master and slave chaos error systems by signal filtering from the vibratory magnitude of the stage. The trajectory diagram is made from the chaos synchronization dynamic error signals E 1 and E 2. The distance between characteristic positive and negative centers of gravity, as well as the maximum and minimum distances of trajectory dia...

  11. Rule based Fault Detection & Diagnosis for high performance buildings: application to a positive energy building in France

    OpenAIRE

    Gavan, Valentin; Perehinec, Anna; Agapoff, Sergeï; Derouineau, Stéphanie

    2016-01-01

    International audience Fault Detection & Diagnosis (FDD) is at the heart of the PERFORMER European FP7 project that aims at reducing the gap between expected and actual energy performance. The purpose of the article is to describe the FDD methodology used within the Performer project and illustrate the benefits of its application to a positive energy building. The rules were defined according to the Building Manager's requirements and were applied to the data collected from the BMS. The pr...

  12. Additional Fault Detection Test Case Prioritization

    Directory of Open Access Journals (Sweden)

    Ritika Jain

    2013-07-01

    Full Text Available Regression testing is used to confirm that previous bugs have been fixed and that new bugs have not been introduced. Thus regression testing is done during maintenance phase and applied whenever a new version of a program is obtained by modifying an existing version. To perform a regression testing a set of new test cases and old test cases that were previously developed by software engineers are reused. This test suite is exhaustive in nature and it may take long time to rerun all test cases. Thus regression testing is too expensive and the number of test cases increases stridently as the software evolves. In present work, an additional fault detection test case prioritization technique is presented that prioritizes test cases in regression test suite based on number of concealed faults detected by test cases. Both noncost cognizant and cost cognizant prioritization of test cases have been performed using proposed technique and efficiency of prioritized suite is assessed using APFD and APFDc metric respectively.

  13. 基于本体的车辆故障检测研究%Research on Vehicle Fault Detection Based on Ontology

    Institute of Scientific and Technical Information of China (English)

    王松; 李程; 吴子尧

    2012-01-01

    Aiming at maintenance difficulty of vehicle equipment, this paper brought forward an ontology - based method to detect vehicle fault. From the perspective of ontology knowledge, it analyzed the knowledge model of fault detection, and e- laborated the principle of induction and its credibility, edited the rules by SWRL language. It can effectively solve the vehicle fault detection problem and make the method of vehicle fault detection more simple and intelligent.%针对我军车辆装备维修困难等问题,提出了一种基于本体理论检测车辆故障的方法。从本体知识的角度出发,分析了故障检测的知识模型,对故障检测推理规则以及规则的可信度进行了阐述,同时运用SWRL语言进行了规则的解析。这种方法能够有效地解决车辆故障检测问题,使车辆故障检测更加简单化和智能化。

  14. 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...... the frequency for the auxiliary input is selected. This gives that it is possible to apply an auxiliary input with a reduced amplitude. An example is included to show the results....

  15. Fault Detection in WSNs - An Energy Efficiency Perspective Towards Human-Centric WSNs

    DEFF Research Database (Denmark)

    Orfanidis, Charalampos; Zhang, Yue; Dragoni, Nicola

    2015-01-01

    has a significant impact on the design of a WSN and the adopted fault detection method. This paper investigates a number of fault detection approaches and proposes a fault detection framework based on an energy efficiency perspective. The analysis and design guidelines given in this paper aims...

  16. 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....

  17. Monitoring and Fault Detection in Photovoltaic Systems Based On Inverter Measured String I-V Curves

    DEFF Research Database (Denmark)

    Spataru, Sergiu; Sera, Dezso; Kerekes, Tamas;

    2015-01-01

    Most photovoltaic (PV) string inverters have the hardware capability to measure at least part of the current-voltage (I-V) characteristic curve of the PV strings connected at the input. However, this intrinsic capability of the inverters is not used, since I-V curve measurement and monitoring...... functions are not implemented in the inverter control software. In this paper, we aim to show how such a functionality can be useful for PV system monitoring purposes, to detect the presence and cause of power-loss in the PV strings, be it due to shading, degradation of the PV modules or balance-of-system...... components through increased series resistance losses, or shunting of the PV modules. To achieve this, we propose and experimentally demonstrate three complementary PV system monitoring methods that make use of the I-V curve measurement capability of a commercial string inverter. The first method is suitable...

  18. Norm based design of fault detectors

    DEFF Research Database (Denmark)

    Rank, Mike Lind; Niemann, Hans Henrik

    1999-01-01

    The design of fault detectors for fault detection and isolation (FDI) in dynamic systems is considered in this paper from a norm based point of view. An analysis of norm based threshold selection is given based on different formulations of FDI problems. Both the nominal FDI problem as well as the...... uncertain FDI problem is considered. With reference to this analysis, a performance index based on norms of the involved transfer functions is given. A method for designing FDI filters which will minimize the performance index is also given....

  19. 基于 LDA 模型的滚动轴承故障类型检测%Fault Type Detection for Rolling Bearings Based on LDA Model

    Institute of Scientific and Technical Information of China (English)

    朱韶平

    2014-01-01

    A new method for fault type detection on rolling bearings based on LDA model is proposed by combining the LDA algorithm with a wavelet packet transform.Firstly the wavelet packet transform is used to extract the energy char-acteristics and containing fault information characteristics of vibration signals for rolling bearings,and the fault informa-tion characteristics is expressed as visual word vector by using the "bag of words"model .Then the fault types of roll-ing bearings are identified with the LDA model.The experiments show that the method can accurately extract the fault information characteristics of rolling bearings and rapidly obtain detection of faults and their types of bearings.Com-pared with SVMand other methods,it features higher accuracy,stronger robustness and better detection results.%将小波包变换与 LDA 算法相结合,提出了一种基于 LDA 模型的滚动轴承故障类型检测新方法。首先通过小波包变换提取轴承振动信号的能量特征及其所包含的故障信息特征,并用“词袋”模型将故障信息特征表示成视觉词向量,然后利用 LDA 模型对轴承故障类型进行判别。试验表明,该方法能精确提取轴承的故障信息特征,快速检测出轴承的故障类型,与 SVM等方法相比检测精度更高,鲁棒性更强,具有很好的故障检测效果。

  20. A fault detection service for wide area distributed computations.

    Energy Technology Data Exchange (ETDEWEB)

    Stelling, P.

    1998-06-09

    The potential for faults in distributed computing systems is a significant complicating factor for application developers. While a variety of techniques exist for detecting and correcting faults, the implementation of these techniques in a particular context can be difficult. Hence, we propose a fault detection service designed to be incorporated, in a modular fashion, into distributed computing systems, tools, or applications. This service uses well-known techniques based on unreliable fault detectors to detect and report component failure, while allowing the user to tradeoff timeliness of reporting against false positive rates. We describe the architecture of this service, report on experimental results that quantify its cost and accuracy, and describe its use in two applications, monitoring the status of system components of the GUSTO computational grid testbed and as part of the NetSolve network-enabled numerical solver.

  1. Fault detection in rotor bearing systems using time frequency techniques

    Science.gov (United States)

    Chandra, N. Harish; Sekhar, A. S.

    2016-05-01

    Faults such as misalignment, rotor cracks and rotor to stator rub can exist collectively in rotor bearing systems. It is an important task for rotor dynamic personnel to monitor and detect faults in rotating machinery. In this paper, the rotor startup vibrations are utilized to solve the fault identification problem using time frequency techniques. Numerical simulations are performed through finite element analysis of the rotor bearing system with individual and collective combinations of faults as mentioned above. Three signal processing tools namely Short Time Fourier Transform (STFT), Continuous Wavelet Transform (CWT) and Hilbert Huang Transform (HHT) are compared to evaluate their detection performance. The effect of addition of Signal to Noise ratio (SNR) on three time frequency techniques is presented. The comparative study is focused towards detecting the least possible level of the fault induced and the computational time consumed. The computation time consumed by HHT is very less when compared to CWT based diagnosis. However, for noisy data CWT is more preferred over HHT. To identify fault characteristics using wavelets a procedure to adjust resolution of the mother wavelet is presented in detail. Experiments are conducted to obtain the run-up data of a rotor bearing setup for diagnosis of shaft misalignment and rotor stator rubbing faults.

  2. Enhanced Fault Detection and Isolation in Modern Flight Actuators

    OpenAIRE

    Ossmann, Daniel

    2013-01-01

    Due to their central location in the control system, actuation systems of primary control surfaces in modern, augmented aircraft must show an increased reliability. A traditional approach is based on hardware redundancy. In this way, modern actuation systems of one single control surface consist of up to two actuators and three sensors. These different dynamic subsystems are all prone to faults themselves and can be monitored. This paper presents the setup of a fault detection and diagnosis (...

  3. A Prognostic Method for Fault Detection in Wind Turbine Drivetrains

    DEFF Research Database (Denmark)

    Nejada, Amir R.; Odgaard, Peter Fogh; Gao, Zhen;

    2014-01-01

    In this paper, a prognostic method is presented for fault detection in gears and bearings in wind turbine drivetrains. This method is based on angular velocity measurements from the gearbox input shaft and the output to the generator, using two additional angular velocity sensors on the intermedi......In this paper, a prognostic method is presented for fault detection in gears and bearings in wind turbine drivetrains. This method is based on angular velocity measurements from the gearbox input shaft and the output to the generator, using two additional angular velocity sensors...... on the intermediate shafts inside the gearbox. An angular velocity error function is defined and compared in the faulty and fault-free conditions in frequency domain. Faults can be detected from the change in the energy level of the frequency spectrum of an error function. The method is demonstrated by detecting...... bearing faults in three locations: the high-speed shaft stage, the planetary stage and the intermediate-speed shaft stage. Simulations of the faulty and fault-free cases are performed on a gearbox model implemented in multibody dynamic simulation software. The global loads on the gearbox are obtained from...

  4. A Unified Nonlinear Adaptive Approach for Detection and Isolation of Engine Faults

    Science.gov (United States)

    Tang, Liang; DeCastro, Jonathan A.; Zhang, Xiaodong; Farfan-Ramos, Luis; Simon, Donald L.

    2010-01-01

    A challenging problem in aircraft engine health management (EHM) system development is to detect and isolate faults in system components (i.e., compressor, turbine), actuators, and sensors. Existing nonlinear EHM methods often deal with component faults, actuator faults, and sensor faults separately, which may potentially lead to incorrect diagnostic decisions and unnecessary maintenance. Therefore, it would be ideal to address sensor faults, actuator faults, and component faults under one unified framework. This paper presents a systematic and unified nonlinear adaptive framework for detecting and isolating sensor faults, actuator faults, and component faults for aircraft engines. The fault detection and isolation (FDI) architecture consists of a parallel bank of nonlinear adaptive estimators. Adaptive thresholds are appropriately designed such that, in the presence of a particular fault, all components of the residual generated by the adaptive estimator corresponding to the actual fault type remain below their thresholds. If the faults are sufficiently different, then at least one component of the residual generated by each remaining adaptive estimator should exceed its threshold. Therefore, based on the specific response of the residuals, sensor faults, actuator faults, and component faults can be isolated. The effectiveness of the approach was evaluated using the NASA C-MAPSS turbofan engine model, and simulation results are presented.

  5. 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 p...

  6. Parity space-based fault diagnosis of CCBII braking system

    Institute of Scientific and Technical Information of China (English)

    黄志武; 杨迎泽; 王晶; 李赟

    2013-01-01

    Fault diagnosis is a key issue of the CCBII(computer controlled brake II) braking system, because the CCBII braking system is very complicated and nonlinear, which may exhibit isolated and multi-component coupled faults. A parity space-based method was proposed for fault diagnosis of CCBII braking systems. Firstly, the mathematical models were established according to three function modules of CCBII braking systems where the air fluid theory was utilized. Then, parity vector and threshold function were designed for each output of the system so as to identify more system faults. Fault character matrix was built based on the causal relationship between the output and the fault according to the system function and internal structure. Finally, fault detection and isolation can be realized by the comparison of the observed system output and the fault character matrix. Simulation results show that the proposed method is entirely feasible and effective.

  7. ATO Fault Detection Based on UIO Method%基于UIO方法的列车自动驾驶信号故障检测研究

    Institute of Scientific and Technical Information of China (English)

    王呈; 唐涛; 罗仁士

    2013-01-01

    列车自动驾驶ATO (Automatic Train Operation)状态故障检测是实现CBTC (Communication Based Train Control)系统车载ATO控制功能可靠性的必要条件之一.针对影响列车自动驾驶功能与性能的速度、位置量测传感器故障及执行器故障进行故障建模,利用基于未知输入观测器UIO的故障检测方法对存在未知干扰与噪声情况下的ATO状态故障进行检测.通过构建残差与设置阈值来降低对未知干扰或噪声的敏感度,从而降低故障报错率.仿真结果表明,本文所提出的方法能够完成对列车控制状态3种故障模式的检测,该检测信息不仅能够给已经装备ATO的车辆提供实时优化曲线生成所需的参数,也可以为仍使用人工驾驶的列车进行预警,并为车辆维护业务提供有用信息.%On-board sensor fault detection turns out essential for the realization of functional reliability of driverless Automatic Train Operation (ATO)of Communication Based Train Control(CBTC)system.In this paper three scenarios of sensor faults affecting the functionality and performance of ATO were modelled.A model based fault detection isolation (FDI) scheme,namely,the unknown input observer (UIO) method,was employed to detect the possible ATO faults in existence of unknown disturbances and noises.Furthermore,residuals and thresholds were set to reduce the sensitivity to unknown disturbances and noises and so to reduce the false alarm rate.The simulation results show as follows:The proposed method can accomplish detection of three fault modes of train control;the detection information serves to offer vehicles with ATO devices the parameters for generation of the real-time optimized schedule curve and to facilitate early warning with man-driving vehicles;the detection information are also useful in maintenance of vehicles.

  8. 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...

  9. Fuzzy-Expert Diagnostics for Detecting and Locating Internal Faults in Three Phase Induction Motors

    Institute of Scientific and Technical Information of China (English)

    DONG Mingchui; CHEANG Takson; SEKAR Booma Devi; CHAN Sileong

    2008-01-01

    Internal faults in three phase induction motors can result in serious performance degradation and eventual system failures if not properly detected and treated in time. Artificial intelligence techniques, the core of soft-computing, have numerous advantages over conventional fault diagnostic approaches; therefore, a soft-computing system was developed to detect and diagnose electric motor faults. The fault diagnostic system for three-phase induction motors samples the fault symptoms and then uses a fuzzy-expert forward inference model to identify the fault. This paper describes how to define the membership functions and fuzzy sets based on the fault symptoms and how to construct the hierarchical fuzzy inference nets with the propagation of probabilities concerning the uncertainty of faults. The designed hierarchical fuzzy inference nets efficiently detect and diagnose the fault type and exact location in a three phase induction motor. The validity and effectiveness of this approach is clearly shown from obtained testing results.

  10. 基于SCADA的供水运行故障检测系统%The Operation Fault Detection System for Water Supply Based on SCADA

    Institute of Scientific and Technical Information of China (English)

    周永峰

    2012-01-01

      The structure, composition, communication mode of Supervisory Control and Data Acquisition(SCADA) for Guangzhou Water Supply are analyzed in this paper. According to research of the fault detection system, an operation fault detection system of water supply based on SCADA is built up. The system makes use of the historical data of SCADA, and is combined with the network state and data transformation operation elements. The fault detection system improves the accuracy of the data and reliability.%  介绍了广州市自来水公司的数据采集与监控(supervisory control and data acquisition,SCADA)系统的结构、组成和通信方式,并根据国内外相关故障检测系统的研究进展,设计了一套基于SCADA的供水运行故障检测系统,利用对SCADA系统历史数据的检测,结合网络状态、数据转换程序运行情况等元素进行故障检测。故障检测系统投入运行后,提高了数据的准确性和系统的可靠性。

  11. Parameter estimation and reliable fault detection of electric motors

    Institute of Scientific and Technical Information of China (English)

    Dusan PROGOVAC; Le Yi WANG; George YIN

    2014-01-01

    Accurate model identification and fault detection are necessary for reliable motor control. Motor-characterizing parameters experience substantial changes due to aging, motor operating conditions, and faults. Consequently, motor parameters must be estimated accurately and reliably during operation. Based on enhanced model structures of electric motors that accommodate both normal and faulty modes, this paper introduces bias-corrected least-squares (LS) estimation algorithms that incorporate functions for correcting estimation bias, forgetting factors for capturing sudden faults, and recursive structures for efficient real-time implementation. Permanent magnet motors are used as a benchmark type for concrete algorithm development and evaluation. Algorithms are presented, their properties are established, and their accuracy and robustness are evaluated by simulation case studies under both normal operations and inter-turn winding faults. Implementation issues from different motor control schemes are also discussed.

  12. Design of a multi-model observer-based estimator for Fault Detection and Isolation (FDI strategy: application to a chemical reactor

    Directory of Open Access Journals (Sweden)

    Y. Chetouani

    2008-12-01

    Full Text Available This study presents a FDI strategy for nonlinear dynamic systems. It shows a methodology of tackling the fault detection and isolation issue by combining a technique based on the residuals signal and a technique using the multiple Kalman filters. The usefulness of this combination is the on-line implementation of the set of models, which represents the normal mode and all dynamics of faults, if the statistical decision threshold on the residuals exceeds a fixed value. In other cases, one Extended Kalman Filter (EKF is enough to estimate the process state. After describing the system architecture and the proposed FDI methodology, we present a realistic application in order to show the technique's potential. An algorithm is described and applied to a chemical process like a perfectly stirred chemical reactor functioning in a semi-batch mode. The chemical reaction used is an oxido reduction one, the oxidation of sodium thiosulfate by hydrogen peroxide.

  13. 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. 

  14. Latest Progress of Fault Detection and Localization in Complex Electrical Engineering

    Science.gov (United States)

    Zhao, Zheng; Wang, Can; Zhang, Yagang; Sun, Yi

    2014-01-01

    In the researches of complex electrical engineering, efficient fault detection and localization schemes are essential to quickly detect and locate faults so that appropriate and timely corrective mitigating and maintenance actions can be taken. In this paper, under the current measurement precision of PMU, we will put forward a new type of fault detection and localization technology based on fault factor feature extraction. Lots of simulating experiments indicate that, although there are disturbances of white Gaussian stochastic noise, based on fault factor feature extraction principal, the fault detection and localization results are still accurate and reliable, which also identifies that the fault detection and localization technology has strong anti-interference ability and great redundancy.

  15. 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.

  16. Multimode Process Fault Detection Using Local Neighborhood Similarity Analysis☆

    Institute of Scientific and Technical Information of China (English)

    Xiaogang Deng; Xuemin Tian

    2014-01-01

    Traditional data driven fault detection methods assume unimodal distribution of process data so that they often perform not wel in chemical process with multiple operating modes. In order to monitor the multimode chemical process effectively, this paper presents a novel fault detection method based on local neighborhood similarity analysis (LNSA). In the proposed method, prior process knowledge is not required and only the multimode normal operation data are used to construct a reference dataset. For online monitoring of process state, LNSA applies moving window technique to obtain a current snapshot data window. Then neighborhood searching technique is used to acquire the corresponding local neighborhood data window from the reference dataset. Similarity analysis between snapshot and neighborhood data windows is performed, which includes the calculation of principal component analysis (PCA) similarity factor and distance similarity factor. The PCA similarity factor is to capture the change of data direction while the distance similarity factor is used for monitoring the shift of data center position. Based on these similarity factors, two monitoring statistics are built for multimode process fault detection. Final y a simulated continuous stirred tank system is used to demonstrate the effectiveness of the proposed method. The simulation results show that LNSA can detect multimode process changes effectively and performs better than traditional fault detection methods.

  17. Fault detection and diagnosis using neural network approaches

    Science.gov (United States)

    Kramer, Mark A.

    1992-01-01

    Neural networks can be used to detect and identify abnormalities in real-time process data. Two basic approaches can be used, the first based on training networks using data representing both normal and abnormal modes of process behavior, and the second based on statistical characterization of the normal mode only. Given data representative of process faults, radial basis function networks can effectively identify failures. This approach is often limited by the lack of fault data, but can be facilitated by process simulation. The second approach employs elliptical and radial basis function neural networks and other models to learn the statistical distributions of process observables under normal conditions. Analytical models of failure modes can then be applied in combination with the neural network models to identify faults. Special methods can be applied to compensate for sensor failures, to produce real-time estimation of missing or failed sensors based on the correlations codified in the neural network.

  18. Fault Detection and Isolation (Fdi Via Neural Networks

    Directory of Open Access Journals (Sweden)

    Neeraj Prakash Srivastava,

    2014-01-01

    Full Text Available Recent approaches to fault detection and isolation for dynamic systems using methods of integrating quantitative and qualitative model information, based upon soft computing (SC methods are used. In this study, the use of SC methods is considered an important extension to the quantitative model-based approach for residual generation in FDI. When quantitative models are not readily available, a correctly trained neural network (NN can be used as a non-linear dynamic model of the system. However, the neural network does not easily provide insight into model. This main difficulty can be overcome using qualitative modeling or rule-based inference methods. The paper presents the properties of several methods of combining quantitative and qualitative system information and their practical value for fault diagnosis of Neural network. Keywords: Soft computing methods, fault-diagnosis, FDI

  19. Internal Leakage Fault Detection and Tolerant Control of Single-Rod Hydraulic Actuators

    Directory of Open Access Journals (Sweden)

    Jianyong Yao

    2014-01-01

    Full Text Available The integration of internal leakage fault detection and tolerant control for single-rod hydraulic actuators is present in this paper. Fault detection is a potential technique to provide efficient condition monitoring and/or preventive maintenance, and fault tolerant control is a critical method to improve the safety and reliability of hydraulic servo systems. Based on quadratic Lyapunov functions, a performance-oriented fault detection method is proposed, which has a simple structure and is prone to implement in practice. The main feature is that, when a prescribed performance index is satisfied (even a slight fault has occurred, there is no fault alarmed; otherwise (i.e., a severe fault has occurred, the fault is detected and then a fault tolerant controller is activated. The proposed tolerant controller, which is based on the parameter adaptive methodology, is also prone to realize, and the learning mechanism is simple since only the internal leakage is considered in parameter adaptation and thus the persistent exciting (PE condition is easily satisfied. After the activation of the fault tolerant controller, the control performance is gradually recovered. Simulation results on a hydraulic servo system with both abrupt and incipient internal leakage fault demonstrate the effectiveness of the proposed fault detection and tolerant control method.

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

    International Nuclear Information System (INIS)

    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 mm3/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

  1. 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...

  2. Faults in clays their detection and properties

    International Nuclear Information System (INIS)

    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

  3. Online Doppler Effect Elimination Based on Unequal Time Interval Sampling for Wayside Acoustic Bearing Fault Detecting System.

    Science.gov (United States)

    Ouyang, Kesai; Lu, Siliang; Zhang, Shangbin; Zhang, Haibin; He, Qingbo; Kong, Fanrang

    2015-08-27

    The railway occupies a fairly important position in transportation due to its high speed and strong transportation capability. As a consequence, it is a key issue to guarantee continuous running and transportation safety of trains. Meanwhile, time consumption of the diagnosis procedure is of extreme importance for the detecting system. However, most of the current adopted techniques in the wayside acoustic defective bearing detector system (ADBD) are offline strategies, which means that the signal is analyzed after the sampling process. This would result in unavoidable time latency. Besides, the acquired acoustic signal would be corrupted by the Doppler effect because of high relative speed between the train and the data acquisition system (DAS). Thus, it is difficult to effectively diagnose the bearing defects immediately. In this paper, a new strategy called online Doppler effect elimination (ODEE) is proposed to remove the Doppler distortion online by the introduced unequal interval sampling scheme. The steps of proposed strategy are as follows: The essential parameters are acquired in advance. Then, the introduced unequal time interval sampling strategy is used to restore the Doppler distortion signal, and the amplitude of the signal is demodulated as well. Thus, the restored Doppler-free signal is obtained online. The proposed ODEE method has been employed in simulation analysis. Ultimately, the ODEE method is implemented in the embedded system for fault diagnosis of the train bearing. The results are in good accordance with the bearing defects, which verifies the good performance of the proposed strategy.

  4. Online Doppler Effect Elimination Based on Unequal Time Interval Sampling for Wayside Acoustic Bearing Fault Detecting System

    Directory of Open Access Journals (Sweden)

    Kesai Ouyang

    2015-08-01

    Full Text Available The railway occupies a fairly important position in transportation due to its high speed and strong transportation capability. As a consequence, it is a key issue to guarantee continuous running and transportation safety of trains. Meanwhile, time consumption of the diagnosis procedure is of extreme importance for the detecting system. However, most of the current adopted techniques in the wayside acoustic defective bearing detector system (ADBD are offline strategies, which means that the signal is analyzed after the sampling process. This would result in unavoidable time latency. Besides, the acquired acoustic signal would be corrupted by the Doppler effect because of high relative speed between the train and the data acquisition system (DAS. Thus, it is difficult to effectively diagnose the bearing defects immediately. In this paper, a new strategy called online Doppler effect elimination (ODEE is proposed to remove the Doppler distortion online by the introduced unequal interval sampling scheme. The steps of proposed strategy are as follows: The essential parameters are acquired in advance. Then, the introduced unequal time interval sampling strategy is used to restore the Doppler distortion signal, and the amplitude of the signal is demodulated as well. Thus, the restored Doppler-free signal is obtained online. The proposed ODEE method has been employed in simulation analysis. Ultimately, the ODEE method is implemented in the embedded system for fault diagnosis of the train bearing. The results are in good accordance with the bearing defects, which verifies the good performance of the proposed strategy.

  5. Online Doppler Effect Elimination Based on Unequal Time Interval Sampling for Wayside Acoustic Bearing Fault Detecting System.

    Science.gov (United States)

    Ouyang, Kesai; Lu, Siliang; Zhang, Shangbin; Zhang, Haibin; He, Qingbo; Kong, Fanrang

    2015-01-01

    The railway occupies a fairly important position in transportation due to its high speed and strong transportation capability. As a consequence, it is a key issue to guarantee continuous running and transportation safety of trains. Meanwhile, time consumption of the diagnosis procedure is of extreme importance for the detecting system. However, most of the current adopted techniques in the wayside acoustic defective bearing detector system (ADBD) are offline strategies, which means that the signal is analyzed after the sampling process. This would result in unavoidable time latency. Besides, the acquired acoustic signal would be corrupted by the Doppler effect because of high relative speed between the train and the data acquisition system (DAS). Thus, it is difficult to effectively diagnose the bearing defects immediately. In this paper, a new strategy called online Doppler effect elimination (ODEE) is proposed to remove the Doppler distortion online by the introduced unequal interval sampling scheme. The steps of proposed strategy are as follows: The essential parameters are acquired in advance. Then, the introduced unequal time interval sampling strategy is used to restore the Doppler distortion signal, and the amplitude of the signal is demodulated as well. Thus, the restored Doppler-free signal is obtained online. The proposed ODEE method has been employed in simulation analysis. Ultimately, the ODEE method is implemented in the embedded system for fault diagnosis of the train bearing. The results are in good accordance with the bearing defects, which verifies the good performance of the proposed strategy. PMID:26343657

  6. 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.

  7. Fault Detection on the Software Implementation of CLEFIA Lightweight Cipher

    Directory of Open Access Journals (Sweden)

    Wei Li

    2012-08-01

    Full Text Available CLEFIA is an efficient lightweight cipher that delivers advanced copyright protection and authentication in computer networks. It is also applied in the secure protocol for transmission including SSL and TLS. Since it was proposed in 2007, some work about its security against differential fault analysis has been devoted to reducing the number of faults and to improving the time complexity of this attack. This attack is very efficient when a single fault is injected into the last several rounds of the CLEFIA, and it allows to recover the whole secret key. Thus, it is an open question whether detecting the faults injected into the CLEFIA with low overhead of space and time tolerance. In this paper, we present a fault detection of the CLEFIA block cipher in the single-byte fault model. Our result in this study could detect the faults with negligible cost when faults are even injected into the last four rounds. 

  8. Fault Detection and Isolation in Centrifugal Pumps

    DEFF Research Database (Denmark)

    Kallesøe, Carsten

    Centrifugal pumps are used in a variety of different applications, such as water supply, wastewater, and different industrial applications. Some pump installations are crucial for the applications to work. Failures can lead to substantial economic losses and can influence the life of many people...... 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...... are tested on an industrial test setup, showing the usability of the algorithms on a real centrifugal pump....

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

    Directory of Open Access Journals (Sweden)

    Weigen Chen

    2013-01-01

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

  10. Bond graph model-based fault diagnosis of hybrid systems

    CERN Document Server

    Borutzky, Wolfgang

    2015-01-01

    This book presents a bond graph model-based approach to fault diagnosis in mechatronic systems appropriately represented by a hybrid model. The book begins by giving a survey of the fundamentals of fault diagnosis and failure prognosis, then recalls state-of-art developments referring to latest publications, and goes on to discuss various bond graph representations of hybrid system models, equations formulation for switched systems, and simulation of their dynamic behavior. The structured text: • focuses on bond graph model-based fault detection and isolation in hybrid systems; • addresses isolation of multiple parametric faults in hybrid systems; • considers system mode identification; • provides a number of elaborated case studies that consider fault scenarios for switched power electronic systems commonly used in a variety of applications; and • indicates that bond graph modelling can also be used for failure prognosis. In order to facilitate the understanding of fault diagnosis and the presented...

  11. Envelope order tracking for fault detection in rolling element bearings

    Science.gov (United States)

    Guo, Yu; Liu, Ting-Wei; Na, Jing; Fung, Rong-Fong

    2012-12-01

    An envelope order tracking analysis scheme is proposed in the paper for the fault detection of rolling element bearing (REB) under varying-speed running condition. The developed method takes the advantages of order tracking, envelope analysis and spectral kurtosis. The fast kurtogram algorithm is utilized to obtain both optimal center frequency and bandwidth of the band-pass filter based on the maximum spectral kurtosis. The envelope containing vibration features of the incipient REB fault can be extracted adaptively. The envelope is re-sampled by the even-angle sampling scheme, and thus the non-stationary signal in the time domain is represented as a quasi-stationary signal in the angular domain. As a result, the frequency-smear problem can be eliminated in order spectrum and the fault diagnosis of REB in the varying-speed running condition of the rotating machinery is achieved. Experiments are conducted to verify the validity of the proposed method.

  12. Optimal Robust Fault Detection for Linear Discrete Time Systems

    Directory of Open Access Journals (Sweden)

    Nike Liu

    2008-01-01

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

  13. Design of H(infinity) robust fault detection filter for linear uncertain time-delay systems.

    Science.gov (United States)

    Bai, Leishi; Tian, Zuohua; Shi, Songjiao

    2006-10-01

    In this paper, the robust fault detection filter design problem for linear time-delay systems with both unknown inputs and parameter uncertainties is studied. Using a multiobjective optimization technique, a new performance index is introduced, which takes into account the robustness of the fault detection filter against disturbances and sensitivity to faults simultaneously. The reference residual model is then designed based on this performance index to formulate the robust fault detection filter design problem as an H(infinity) model-matching problem. By applying robust H(infinity) optimization control technique, the existence condition of the robust fault detection filter for linear time-delay systems with both unknown inputs and parameter uncertainties is presented in terms of linear matrix inequality formulation, independently of time delay. In order to detect the fault, an adaptive threshold which depends on the inputs is finally determined. An illustrative design example is used to demonstrate the validity of the proposed approach.

  14. 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...

  15. Wind Turbine Gearbox Fault Diagnosis Method Based on Riemannian Manifold

    Directory of Open Access Journals (Sweden)

    Shoubin Wang

    2014-01-01

    Full Text Available As multivariate time series problems widely exist in social production and life, fault diagnosis method has provided people with a lot of valuable information in the finance, hydrology, meteorology, earthquake, video surveillance, medical science, and other fields. In order to find faults in time sequence quickly and efficiently, this paper presents a multivariate time series processing method based on Riemannian manifold. This method is based on the sliding window and uses the covariance matrix as a descriptor of the time sequence. Riemannian distance is used as the similarity measure and the statistical process control diagram is applied to detect the abnormity of multivariate time series. And the visualization of the covariance matrix distribution is used to detect the abnormity of mechanical equipment, leading to realize the fault diagnosis. With wind turbine gearbox faults as the experiment object, the fault diagnosis method is verified and the results show that the method is reasonable and effective.

  16. 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...... normal and faulty conditions. Thus a fault detection system and a fault tolerant controller has been designed and combined. The fault tolerant control system has then been tested and compared to the reference system and shows improvement on all measures....

  17. 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.

  18. Digital electronic engine control fault detection and accommodation flight evaluation

    Science.gov (United States)

    Baer-Ruedhart, J. L.

    1984-01-01

    The capabilities and performance of various fault detection and accommodation (FDA) schemes in existing and projected engine control systems were investigated. Flight tests of the digital electronic engine control (DEEC) in an F-15 aircraft show discrepancies between flight results and predictions based on simulation and altitude testing. The FDA methodology and logic in the DEEC system, and the results of the flight failures which occurred to date are described.

  19. 基于流程模拟的化工故障检测技术%Chemical process fault detection technology based on process simulation

    Institute of Scientific and Technical Information of China (English)

    李秀喜; 袁延江

    2014-01-01

    提出了一种使用MATLAB仿真工具箱Simulink与动态模拟软件Aspen Dynamics相互调用来实现化工过程监测的方法。该方法具有以下优点:Aspen Dynamics能够快速建立精确的动态模型,具有完善的物性数据库,同时可以方便根据实际的化工过程对模型进行调整;使用Simulink仿真工具箱可以实时采集数据作为模型输入,同时完成对数据的必要处理。为了检测方法的可行性,将其应用于一个虚拟精馏过程来检验监测效果,结果表明,其可以实现对存在生产计划变更过程的故障监测和无生产计划变更过程中故障的监测。%A chemical process monitoring method using Simulink Toolbox of MATLAB to invoke Aspen Dynamics to dynamically simulate the chemical process was proposed. In most of the previous literatures about model-based fault detection, the mechanistic model should be built by hand,which is very time-consuming and requires the user to have a high professional quality. Using this method can build dynamics simulation for chemical in a quick and accurate way using Aspen Dynamics even the people who don’t have high professional quality in chemical engineering, meanwhile, the data collected from the factory often require correction, using Simulink Toolbox can conveniently correct the model data and measured data, at the same time the real-time factory data was collected as input data for dynamics simulation in order to achieve real-time process monitoring. The method was tested using a virtual distillation process in Aspen Dynamics, the results show that it can detect faults in chemical process with and without production change. Because the fault data also comes from a distillation process using Aspen Dynamics, the simulate data and the measured data without process fault are very similar, the method to correct the simulation data was not include.

  20. 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.

  1. A Fault Dictionary-Based Fault Diagnosis Approach for CMOS Analog Integrated Circuits

    Directory of Open Access Journals (Sweden)

    Mouna Karmani

    2011-10-01

    Full Text Available In this paper, we propose a simulation-before-test (SBT fault diagnosis methodology based on the use of afault dictionary approach. This technique allows the detection and localization of the most likely defects ofopen-circuit type occurring in Complementary Metal–Oxide–Semiconductor (CMOS analog integratedcircuits (ICs interconnects. The fault dictionary is built by simulating the most likely defects causing thefaults to be detected at the layout level. Then, for each injected fault, the spectre’s frequency responses andthe power consumption obtained by simulation are stored in a table which constitutes the fault dictionary.In fact, each line in the fault dictionary constitutes a fault signature used to identify and locate aconsidered defect. When testing, the circuit under test is excited with the same stimulus, and the responsesobtained are compared to the stored ones. To prove the efficiency of the proposed technique, a full customCMOS operational amplifier is implemented in 0.25 μm technology and the most likely faults of opencircuittype are deliberately injected and simulated at the layout level.

  2. An adaptive envelope spectrum technique for bearing fault detection

    International Nuclear Information System (INIS)

    In this work, an adaptive envelope spectrum (AES) technique is proposed for bearing fault detection, especially for analyzing signals with transient events. The proposed AES technique first modulates the signal using the empirical mode decomposition to formulate the representative intrinsic mode functions (IMF), and then a novel IMF reconstruction method is proposed based on a correlation analysis of the envelope spectra. The reconstructed signal is post-processed by using an adaptive filter to enhance impulsive signatures, where the filter length is optimized by the proposed sparsity analysis technique. Bearing health conditions are diagnosed by examining bearing characteristic frequency information on the envelope power spectrum. The effectiveness of the proposed fault detection technique is verified by a series of experimental tests corresponding to different bearing conditions. (paper)

  3. Automatic fault detection on BIPV systems without solar irradiation data

    CERN Document Server

    Leloux, Jonathan; Luna, Alberto; Desportes, Adrien

    2014-01-01

    BIPV systems are small PV generation units spread out over the territory, and whose characteristics are very diverse. This makes difficult a cost-effective procedure for monitoring, fault detection, performance analyses, operation and maintenance. As a result, many problems affecting BIPV systems go undetected. In order to carry out effective automatic fault detection procedures, we need a performance indicator that is reliable and that can be applied on many PV systems at a very low cost. The existing approaches for analyzing the performance of PV systems are often based on the Performance Ratio (PR), whose accuracy depends on good solar irradiation data, which in turn can be very difficult to obtain or cost-prohibitive for the BIPV owner. We present an alternative fault detection procedure based on a performance indicator that can be constructed on the sole basis of the energy production data measured at the BIPV systems. This procedure does not require the input of operating conditions data, such as solar ...

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

    International Nuclear Information System (INIS)

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

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

    Energy Technology Data Exchange (ETDEWEB)

    Jung, Woo Sik [Sejong University, Seoul (Korea, Republic of)

    2015-05-15

    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.

  6. 基于RVM回归的姿控系统多故障检测%Multi-fault detection for attitude control system based on RVM regression

    Institute of Scientific and Technical Information of China (English)

    胡迪; 董云峰

    2011-01-01

    针对卫星姿控系统故障具有并发性和多发性的特点,敏感器和执行机构均可能发生故障,依据相关向量机(RVM)回归理论,采用基于模型辩识,残差评价的方法实现姿控系统多故障检测.通过对太阳敏感器、陀螺和反作用轮的历史输入输出数据建立RVM回归模型,考虑到建模精确度直接影响到检测精确度,对比分析最小二乘支持向量机回归( LSSVR)的回归模型,并给出了二者辩识精确度对比结果.对比结果表明,RVM较LSSVR具有较好的建模精确度.将RVM回归模型应用于太阳敏感器、陀螺和反作用轮的单一故障和多故障检测过程中,仿真结果表明,RVM回归能有效实现姿控系统的多故障检测.%As satellite attitude control system ( ACS) failures have concurrent and multiple features, both the sensors and actuators may occur faults. Model identification and residual evaluation were applied to ACS fault detection based on relevance vector machine ( RVM) theory. RVM regression was utilized to perform offline regression modeling for the satellite's sun sensor, gyro and reaction wheel, and obtain the regression modeling through analyzing input/output history data stream. As a result, the accuracy of modeling identification was affected by regression model. A comparison between least square support vector regression (LSSVR) and RVM regression was analyzed. The simulation result shows that the RVM is much better than LSSVR. Different scenarios with sun sensor, gyro and reaction wheel to realize concurrent and multiple fault detection were simulated. The result shows that the RVM regression is convenient to the attitude control system.

  7. Fault detection filter design for stochastic time-delay systems with sensor faults

    Science.gov (United States)

    Li, Xiao-Jian; Yang, Guang-Hong

    2012-08-01

    This article considers the fault detection (FD) problem for a class of Itô-type stochastic time-delay systems subject to external disturbances and sensor faults. The main objective is to design a fault detection filter (FDF) such that it has prescribed levels of disturbance attenuation and fault sensitivity. Sufficient conditions for guaranteeing these levels are formulated in terms of linear matrix inequalities (LMIs), and the corresponding fault detection filter design is cast into a convex optimisation problem which can be efficiently handled by using standard numerical algorithms. In order to reduce the conservatism of filter design with mixed objectives, multi-Lyapunov functions approach is used via Projection Lemma. In addition, it is shown that our results not only include some previous conditions characterising H ∞ performance and H - performance defined for linear time-invariant (LTI) systems as special cases but also improve these conditions. Finally, two examples are employed to illustrate the effectiveness of the proposed design scheme.

  8. Fault Detection of Wind Turbines with Uncertain Parameters

    DEFF Research Database (Denmark)

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

    2012-01-01

    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...

  9. Fuzzy delay model based fault simulator for crosstalk delay fault test generation in asynchronous sequential circuits

    Indian Academy of Sciences (India)

    S Jayanthy; M C Bhuvaneswari

    2015-02-01

    In this paper, a fuzzy delay model based crosstalk delay fault simulator is proposed. As design trends move towards nanometer technologies, more number of new parameters affects the delay of the component. Fuzzy delay models are ideal for modelling the uncertainty found in the design and manufacturing steps. The fault simulator based on fuzzy delay detects unstable states, oscillations and non-confluence of settling states in asynchronous sequential circuits. The fuzzy delay model based fault simulator is used to validate the test patterns produced by Elitist Non-dominated sorting Genetic Algorithm (ENGA) based test generator, for detecting crosstalk delay faults in asynchronous sequential circuits. The multi-objective genetic algorithm, ENGA targets two objectives of maximizing fault coverage and minimizing number of transitions. Experimental results are tabulated for SIS benchmark circuits for three gate delay models, namely unit delay model, rise/fall delay model and fuzzy delay model. Experimental results indicate that test validation using fuzzy delay model is more accurate than unit delay model and rise/fall delay model.

  10. Fault detection and diagnosis of diesel engine valve trains

    Science.gov (United States)

    Flett, Justin; Bone, Gary M.

    2016-05-01

    This paper presents the development of a fault detection and diagnosis (FDD) system for use with a diesel internal combustion engine (ICE) valve train. A novel feature is generated for each of the valve closing and combustion impacts. Deformed valve spring faults and abnormal valve clearance faults were seeded on a diesel engine instrumented with one accelerometer. Five classification methods were implemented experimentally and compared. The FDD system using the Naïve-Bayes classification method produced the best overall performance, with a lowest detection accuracy (DA) of 99.95% and a lowest classification accuracy (CA) of 99.95% for the spring faults occurring on individual valves. The lowest DA and CA values for multiple faults occurring simultaneously were 99.95% and 92.45%, respectively. The DA and CA results demonstrate the accuracy of our FDD system for diesel ICE valve train fault scenarios not previously addressed in the literature.

  11. Export Methods in Fault Detection and Localization Mechanisms

    Directory of Open Access Journals (Sweden)

    Aymen Belghith

    2012-07-01

    Full Text Available Monitoring the quality of service in a multi-domain network allows providers to ensure the control of multi-domain service performance. A multi-domain service is a service that crosses multiple domains. In this paper, we propose several mechanisms for fault detection and fault localization. A fault is detected when an end-to-end contract is not respected. Faulty domains are domains that do not fulfill their Quality of Service (QoS requirements. Our three proposed fault detection and localization mechanisms (FDLM depend on the export method used. These export methods define how the measurement results are exported for analysis. We consider the periodic export, the triggered export, and a combined method. For each FDLM, we propose two sub-schemes that use different fault detection strategies. In this paper, we describe these mechanisms and evaluate their performance using Network Simulator (NS-2.

  12. An improved fault detection classification and location scheme based on wavelet transform and artificial neural network for six phase transmission line using single end data only.

    Science.gov (United States)

    Koley, Ebha; Verma, Khushaboo; Ghosh, Subhojit

    2015-01-01

    Restrictions on right of way and increasing power demand has boosted development of six phase transmission. It offers a viable alternative for transmitting more power, without major modification in existing structure of three phase double circuit transmission system. Inspite of the advantages, low acceptance of six phase system is attributed to the unavailability of a proper protection scheme. The complexity arising from large number of possible faults in six phase lines makes the protection quite challenging. The proposed work presents a hybrid wavelet transform and modular artificial neural network based fault detector, classifier and locator for six phase lines using single end data only. The standard deviation of the approximate coefficients of voltage and current signals obtained using discrete wavelet transform are applied as input to the modular artificial neural network for fault classification and location. The proposed scheme has been tested for all 120 types of shunt faults with variation in location, fault resistance, fault inception angles. The variation in power system parameters viz. short circuit capacity of the source and its X/R ratio, voltage, frequency and CT saturation has also been investigated. The result confirms the effectiveness and reliability of the proposed protection scheme which makes it ideal for real time implementation. PMID:26435897

  13. Soft Fault Diagnosis for Analog Circuits Based on Slope Fault Feature and BP Neural Networks

    Institute of Scientific and Technical Information of China (English)

    HU Mei; WANG Hong; HU Geng; YANG Shiyuan

    2007-01-01

    Fault diagnosis is very important for development and maintenance of safe and reliable electronic circuits and systems. This paper describes an approach of soft fault diagnosis for analog circuits based on slope fault feature and back propagation neural networks (BPNN). The reported approach uses the voltage relation function between two nodes as fault features; and for linear analog circuits, the voltage relation function is a linear function, thus the slope is invariant as fault feature. Therefore, a unified fault feature for both hard fault (open or short fault) and soft fault (parametric fault) is extracted. Unlike other NN-based diagnosis methods which utilize node voltages or frequency response as fault features, the reported BPNN is trained by the extracted feature vectors, the slope features are calculated by just simulating once for each component, and the trained BPNN can achieve all the soft faults diagnosis of the component. Experiments show that our approach is promising.

  14. 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. PMID:22163416

  15. 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.

  16. 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

  17. Concealed fault analysis based on the CT projection matrix

    Institute of Scientific and Technical Information of China (English)

    Yang Zhen; Yao Wenli; Ma Liuzhu; Wise Lucas

    2016-01-01

    This paper proposes the concept of projection curves based on the theory of CT image reconstruction to probe the internal structure of the working panel prior to formal mining of the working panel. As well as reducing costs, this method provides safe and efficient excavation of the working panel. According to the results of the numerical model and the actual working panel, the new method has been proven to be accurate in detecting the location of the fault that extends into the face. Concealed faults of the internal working panel, as well as the start and end points of the fault, can be detected by this method. Engineering practice has proven that the method is highly reliable, has a highly decisive impact on faults for coal mining, and can be used to guide the safe mining of the working panel.

  18. Nonlinear fault diagnosis method based on kernel principal component analysis

    Institute of Scientific and Technical Information of China (English)

    Yan Weiwu; Zhang Chunkai; Shao Huihe

    2005-01-01

    To ensure the system run under working order, detection and diagnosis of faults play an important role in industrial process. This paper proposed a nonlinear fault diagnosis method based on kernel principal component analysis (KPCA). In proposed method, using essential information of nonlinear system extracted by KPCA, we constructed KPCA model of nonlinear system under normal working condition. Then new data were projected onto the KPCA model. When new data are incompatible with the KPCA model, it can be concluded that the nonlinear system isout of normal working condition. Proposed method was applied to fault diagnosison rolling bearings. Simulation results show proposed method provides an effective method for fault detection and diagnosis of nonlinear system.

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

    International Nuclear Information System (INIS)

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

  20. Fuzzy fault diagnostic system based on fault tree analysis

    OpenAIRE

    Yang, Zong Xiao; Suzuki, Kazuhiko; Shimada, Yukiyasu; Sayama, Hayatoshi

    1995-01-01

    A method is presented for process fault diagnosis using information from fault tree analysis and uncertainty/imprecision of data. Fault tree analysis, which has been used as a method of system reliability/safety analysis, provides a procedure for identifying failures within a process. A fuzzy fault diagnostic system is constructed which uses the fuzzy fault tree analysis to represent a knowledge of the causal relationships in process operation and control system. The proposed method is applie...

  1. 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.

  2. Faults detection and reconstruction for quad-rotor aircraft based on double-observers parallel structure%基于双观测器并行结构的四旋翼无人机故障检测与重构

    Institute of Scientific and Technical Information of China (English)

    宫勋; 赵常均; 王丽; 白越

    2015-01-01

    The fault detection and reconstruction method based on actutor gain-fault of a quad-rotor aircraft is proposed. A sliding-mode fault observer is designed to detect the fault. In consideration of the actuation redundancy characteristic, a fault reconstruction algorithm is proposed based on paralleled double-observers and a super-twisting algorithm. The theoretical analysis on the proposed methods is given, and the numerical simulation verifies the effectiveness of the proposed methods.%以四旋翼飞行器执行单元增益型故障的检测与重构为研究内容,设计基于滑模观测器的故障检测算法。针对飞行器姿态控制系统的驱动单元冗余特性,提出一种并行双降维观测器与超螺旋算法相结合的故障重构算法。对所提出的算法进行了理论分析,并通过数值仿真验证了所提出算法的有效性。

  3. New method for online interturn faults detection in power transformer with using probabilistic neural network

    Directory of Open Access Journals (Sweden)

    S. hajiaghasi

    2014-07-01

    Full Text Available In recent years with notice increase reliability in power system and Intelligent Systems and also notice that transformers are one of the main part of the transmission and distribution systems, online monitoring of these equipment in power system are require. In this paper, a new method for online interturn fault detection base on leakage flux in power transformer are propose. When an interturn fault occur the symmetry of flux destruction and leakage flux increase or decrease and for various location and severity of fault leakage flux is different and it can be used for fault detection. In this paper for measure these flux we using search coils that mounted on HV winding. To fault detection and classify we using probabilistic neural network. and for decrease the information volume PCA is used. The simulation results are compare and verified with experimental result and show that this propose method is very good.

  4. Algorithm-Based Fault Tolerance for Numerical Subroutines

    Science.gov (United States)

    Tumon, Michael; Granat, Robert; Lou, John

    2007-01-01

    A software library implements a new methodology of detecting faults in numerical subroutines, thus enabling application programs that contain the subroutines to recover transparently from single-event upsets. The software library in question is fault-detecting middleware that is wrapped around the numericalsubroutines. Conventional serial versions (based on LAPACK and FFTW) and a parallel version (based on ScaLAPACK) exist. The source code of the application program that contains the numerical subroutines is not modified, and the middleware is transparent to the user. The methodology used is a type of algorithm- based fault tolerance (ABFT). In ABFT, a checksum is computed before a computation and compared with the checksum of the computational result; an error is declared if the difference between the checksums exceeds some threshold. Novel normalization methods are used in the checksum comparison to ensure correct fault detections independent of algorithm inputs. In tests of this software reported in the peer-reviewed literature, this library was shown to enable detection of 99.9 percent of significant faults while generating no false alarms.

  5. Fault detection method based on LECA for multimode process%基于LECA的多工况过程故障检测方法

    Institute of Scientific and Technical Information of China (English)

    钟娜; 邓晓刚; 徐莹

    2015-01-01

    针对工业过程监控中的多工况复杂分布数据,提出一种基于局部熵成分分析(LECA)的故障检测方法.为处理数据的多模态分布问题,LECA 首先采用 KNN-Parzen 窗方法估计变量的局部概率密度,进一步构造局部相对概率密度函数降低对窗参数选择的敏感性.为有效挖掘非高斯分布数据中的特征信息,利用信息熵理论计算过程数据的局部信息熵,并采用独立元分析(ICA)方法建立局部熵成分统计模型,实时检测过程故障.在数值例子和连续搅拌反应釜(CSTR)上的仿真结果表明,该方法在故障检测过程中能够获得较好的监控性能.%Aiming at the multimode process data which follow complex distribution in industrial process monitoring, this paper proposes a fault detection method based on local entropy component analysis (LECA) algorithm. In order to deal with the multimode characteristic of operating data,k nearest neighbor Parzen window (KNN-Parzen) method is used to estimate the local probability density of each sample. Then, a local relative density estimate function is constructed to decrease the sensitivity to window width parameter. To effectively extract the feature information hidden in the non-Gaussian data, the local entropies of process data are calculated by using information entropy theory. Independent component analysis (ICA) is applied to establish the local entropy component statistic model for fault detection. The simulation results of numerical example and continuous stirred tank reactor (CSTR) system indicate that LECA can show superior performance in process monitoring.

  6. Performance evaluation of fault detection methods for wastewater treatment processes.

    Science.gov (United States)

    Corominas, Lluís; Villez, Kris; Aguado, Daniel; Rieger, Leiv; Rosén, Christian; Vanrolleghem, Peter A

    2011-02-01

    Several methods to detect faults have been developed in various fields, mainly in chemical and process engineering. However, minimal practical guidelines exist for their selection and application. This work presents an index that allows for evaluating monitoring and diagnosis performance of fault detection methods, which takes into account several characteristics, such as false alarms, false acceptance, and undesirable switching from correct detection to non-detection during a fault event. The usefulness of the index to process engineering is demonstrated first by application to a simple example. Then, it is used to compare five univariate fault detection methods (Shewhart, EWMA, and residuals of EWMA) applied to the simulated results of the Benchmark Simulation Model No. 1 long-term (BSM1_LT). The BSM1_LT, provided by the IWA Task Group on Benchmarking of Control Strategies, is a simulation platform that allows for creating sensor and actuator faults and process disturbances in a wastewater treatment plant. The results from the method comparison using BSM1_LT show better performance to detect a sensor measurement shift for adaptive methods (residuals of EWMA) and when monitoring the actuator signals in a control loop (e.g., airflow). Overall, the proposed index is able to screen fault detection methods.

  7. A New Method for the Detections of Multiple Faults Using Binary Decision Diagrams

    Institute of Scientific and Technical Information of China (English)

    PAN Zhongliang; CHEN Ling; ZHANG Guangzhao

    2006-01-01

    With the complexity of integrated circuits is continually increasing, a local defect in circuits may cause multiple faults. The behavior of a digital circuit with a multiple fault may significantly differ from that of a single fault. A new method for the detection of multiple faults in digital circuits is presented in this paper, the method is based on binary decision diagram (BDD). First of all, the BDDs for the normal circuit and faulty circuit are built respectively. Secondly, a test BDD is obtained by the XOR operation of the BDDs corresponds to normal circuit and faulty circuit. In the test BDD, each input assignment that leads to the leaf node labeled 1 is a test vector of multiple faults. Therefore, the test set of multiple faults is generated by searching for the type of input assignments in the test BDD. Experimental results on some digital circuits show the feasibility of the approach presented in this paper.

  8. Filtering, control and fault detection with randomly occurring incomplete information

    CERN Document Server

    Dong, Hongli; Gao, Huijun

    2013-01-01

    This book investigates the filtering, control and fault detection problems for several classes of nonlinear systems with randomly occurring incomplete information. It proposes new concepts, including RVNs, ROMDs, ROMTCDs, and ROQEs. The incomplete information under consideration primarily includes missing measurements, time-delays, sensor and actuator saturations, quantization effects and time-varying nonlinearities. The first part of this book focuses on the filtering, control and fault detection problems for several classes of nonlinear stochastic discrete-time systems and

  9. On-line early fault detection and diagnosis of municipal solid waste incinerators.

    Science.gov (United States)

    Zhao, Jinsong; Huang, Jianchao; Sun, Wei

    2008-11-01

    A fault detection and diagnosis framework is proposed in this paper for early fault detection and diagnosis (FDD) of municipal solid waste incinerators (MSWIs) in order to improve the safety and continuity of production. In this framework, principal component analysis (PCA), one of the multivariate statistical technologies, is used for detecting abnormal events, while rule-based reasoning performs the fault diagnosis and consequence prediction, and also generates recommendations for fault mitigation once an abnormal event is detected. A software package, SWIFT, is developed based on the proposed framework, and has been applied in an actual industrial MSWI. The application shows that automated real-time abnormal situation management (ASM) of the MSWI can be achieved by using SWIFT, resulting in an industrially acceptable low rate of wrong diagnosis, which has resulted in improved process continuity and environmental performance of the MSWI. PMID:18255276

  10. Correlating hardware fault detection information from distributed control systems to isolate and diagnose a fault in pressurised water reactors

    OpenAIRE

    Cilliers, Anthonie Christoffel

    2013-01-01

    Early fault identification systems enable detecting and diagnosing early onset faults or fault causes which allow 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. We have shown (Cilliers and Mulder, 2012) that detecting faults early during transient operation in NPPs is possible when coupled with a reliable reference to compare plant measu...

  11. Reconfigurable Test Architecture for Online Concurrent Fault Detection, Diagnosis and Repair

    Directory of Open Access Journals (Sweden)

    Rajeevan Chandel

    2012-03-01

    Full Text Available A complete and versatile online test solution based on reconfigurable test architecture is presented in the present paper. Reconfigurable test architecture works alongside the controllers for online concurrent fault detection. The output vectors of the controllers are concurrently monitored and any fault present is detected in a few cycles from the sensitization of the fault. The architecture is then reprogrammed to a similar set of diagnostic hardware to locate a sub block which is the cause for the fault. The same architecture is then reprogrammed to replace the faulty block thereby completing repair. The test architecture is designed based on configurable logic blocks. The design has several advantages viz. (i it works well for critical VLSI controllers where shutting down or suspending the operation of a controller for testing is not possible and where the fault needs to be detected at the earliest, during the run time of the system, (ii after a fault is detected, diagnosis can be performed online, (iii once a faulty block is located, repair is also done online. Since fault detection, diagnosis and repair are completed online with one test hardware, the effective hardware overhead is negligible and the system can resume its function within a brief period. The applicability of the architecture is demonstrated for the control blocks in OC8051.

  12. 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

    problems and propose appropriate solutions (hence diagnosis) based on previously stored cases. The methodology is evaluated on a pilot-scale SBR performing nitrogen, phosphorus and COD removal and to help to diagnose abnormal situations in the process operation. Finally, it is believed that the methodology...

  13. 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.

  14. Bearings fault detection using inference tools

    OpenAIRE

    Prieto, Miguel Delgado; Roura, Jordi Cusidó i; Martínez, Jose Luis Romeral

    2011-01-01

    The most used electric machine in the industry is the Induction Motor (IM), due to its simplicity and reduced cost. The analysis of the origin of IMs failures exhibits that the bearings are the major source of fault, and even a common cause of degradation in other kinds of motors as Permanent Magnet Synchronous Machines.

  15. 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.

  16. Optimal Sensor Allocation for Fault Detection and Isolation

    Science.gov (United States)

    Azam, Mohammad; Pattipati, Krishna; Patterson-Hine, Ann

    2004-01-01

    Automatic fault diagnostic schemes rely on various types of sensors (e.g., temperature, pressure, vibration, etc) to measure the system parameters. Efficacy of a diagnostic scheme is largely dependent on the amount and quality of information available from these sensors. The reliability of sensors, as well as the weight, volume, power, and cost constraints, often makes it impractical to monitor a large number of system parameters. An optimized sensor allocation that maximizes the fault diagnosibility, subject to specified weight, volume, power, and cost constraints is required. Use of optimal sensor allocation strategies during the design phase can ensure better diagnostics at a reduced cost for a system incorporating a high degree of built-in testing. In this paper, we propose an approach that employs multiple fault diagnosis (MFD) and optimization techniques for optimal sensor placement for fault detection and isolation (FDI) in complex systems. Keywords: sensor allocation, multiple fault diagnosis, Lagrangian relaxation, approximate belief revision, multidimensional knapsack problem.

  17. 支持向量机的低压故障电弧识别方法%Detection of Low-voltage Arc Fault Based on Support Vector Machine

    Institute of Scientific and Technical Information of China (English)

    徐贞华

    2012-01-01

    故障电弧是引发电气火灾事故的主要原因之一.该文将支持向量机引入故障电弧研究领域,进行不同负荷情况下故障电弧识别检测.首先参照美国UL1699标准进行实验采集电流数据,然后利用支持向量机实现故障电弧训练、检测识别,并对训练、识别结果进行分析,实验证明本文的检测方法具备一定的泛化能力.%Arc fault is one of the prime reasons causing electrical fire accidents. In this paper,the support vector machine(SVM)is applied to the field of arc faults,for the prupose of detecting arc faults under different loads. Firstly,experiment data are collected based on UL1699. Arc faults are detected and identified by applying SVM. The analysis of the results shows that this detection method has some generalization.

  18. Nonlinear Statistical Process Monitoring and Fault Detection Using Kernel ICA

    Institute of Scientific and Technical Information of China (English)

    ZHANG Xi; YAN Wei-wu; ZHAO Xu; SHAO Hui-he

    2007-01-01

    A novel nonlinear process monitoring and fault detection method based on kernel independent component analysis (ICA) is proposed. The kernel ICA method is a two-phase algorithm: whitened kernel principal component (KPCA) plus ICA. KPCA spheres data and makes the data structure become as linearly separable as possible by virtue of an implicit nonlinear mapping determined by kernel. ICA seeks the projection directions in the KPCA whitened space, making the distribution of the projected data as non-gaussian as possible. The application to the fluid catalytic cracking unit (FCCU) simulated process indicates that the proposed process monitoring method based on kernel ICA can effectively capture the nonlinear relationship in process variables. Its performance significantly outperforms monitoring method based on ICA or KPCA.

  19. Application of fault factor method to fault detection and diagnosis for space shuttle main engine

    Science.gov (United States)

    Cha, Jihyoung; Ha, Chulsu; Ko, Sangho; Koo, Jaye

    2016-09-01

    This paper deals with an application of the multiple linear regression algorithm to fault detection and diagnosis for the space shuttle main engine (SSME) during a steady state. In order to develop the algorithm, the energy balance equations, which balances the relation among pressure, mass flow rate and power at various locations within the SSME, are obtained. Then using the measurement data of some important parameters of the engine, fault factors which reflects the deviation of each equation from the normal state are estimated. The probable location of each fault and the levels of severity can be obtained from the estimated fault factors. This process is numerically demonstrated for the SSME at 104% Rated Propulsion Level (RPL) by using the simulated measurement data from the mathematical models of the engine. The result of the current study is particularly important considering that the recently developed reusable Liquid Rocket Engines (LREs) have staged-combustion cycles similarly to the SSME.

  20. Multiscale Permutation Entropy Based Rolling Bearing Fault Diagnosis

    Directory of Open Access Journals (Sweden)

    Jinde Zheng

    2014-01-01

    Full Text Available A new rolling bearing fault diagnosis approach based on multiscale permutation entropy (MPE, Laplacian score (LS, and support vector machines (SVMs is proposed in this paper. Permutation entropy (PE was recently proposed and defined to measure the randomicity and detect dynamical changes of time series. However, for the complexity of mechanical systems, the randomicity and dynamic changes of the vibration signal will exist in different scales. Thus, the definition of MPE is introduced and employed to extract the nonlinear fault characteristics from the bearing vibration signal in different scales. Besides, the SVM is utilized to accomplish the fault feature classification to fulfill diagnostic procedure automatically. Meanwhile, in order to avoid a high dimension of features, the Laplacian score (LS is used to refine the feature vector by ranking the features according to their importance and correlations with the main fault information. Finally, the rolling bearing fault diagnosis method based on MPE, LS, and SVM is proposed and applied to the experimental data. The experimental data analysis results indicate that the proposed method could identify the fault categories effectively.

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

    KAUST Repository

    Diaz Ledezma, F.

    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.

  2. 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.

  3. 基于位移传感器LVDT的信号断线故障检测%Line-break fault detection based on LVDT

    Institute of Scientific and Technical Information of China (English)

    王丹麟; 马伟东; 杨昕霖

    2011-01-01

    针对线性可变差动变压器(LVDT)信号断线检测的必要性,以及目前断线检测方法的局限性,提出一种采用软硬件结合的方式实现对LVDT信号断线检测的方法.针对不同信号断线情况采用不同的检测方法:对于反馈线圈断线采用硬件超限检测方法,对于激励线圈断线采用软件判别检测方法.通过对实际电路测试,对目前断线检测方法的局限性进行了验证,并对软硬件结合方式实现LVDT的信号断线检测进行了验证.检测方法已应用到工程应用,可有效实现对LVDT信号断线的检测.%As it is necessary to detect the line-break fault for LVDT(Linear Variable Differential Transformer) and the current LVDT line-break detection method has limitations, a LVDT line-break detection combining hardware and software methods is presented for different line-break situations: hardware detection for feedback loop line-break and software detection for excitation loop line-break. The limitations of current detection methods and the effectiveness of the proposed method are verified by the tests for actual circuit. The proposed method has been applied to projects for LVDT line-break fault detection.

  4. Exhumation history of an active fault to constrain a fault-based seismic hazard scenario: the Pizzalto fault (central Apennines, Italy) example.

    Science.gov (United States)

    Tesson, Jim; Pace, Bruno; Benedetti, Lucilla; Visini, Francesco; Delli Rocioli, Mattia; Didier, Bourles; Karim, keddadouche; Gorges, Aumaitre

    2016-04-01

    A prerequisite to constrain fault-based and time-dependent earthquake rupture forecast models is to acquire data on the past large earthquake frequency on an individual seismogenic source and to compare all the recorded occurrences in the active fault-system. We investigated the Holocene seismic history of the Pizzalto normal fault, a 13 km long fault segment belonging to the Pizzalto-Rotella-Aremogna fault system in the Apennines (Italy). We collected 44 samples on the Holocene exhumed Pizzalto fault plane and analyzed their 36Cl and rare earth elements content. Conjointly used, the 36Cl and REE concentrations show that at least 6 events have exhumed 4.4 m of the fault scarp between 3 and 1 ka BP, the slip per event ranging from 0.3 to 1.2 m. No major events have been detected over the last 1 ka. The Rotella-Aremogna-Pizzalto fault system has a clustered earthquake behaviour with a mean recurrence time of 1.2 ka and a low to moderate probability (ranging from 4% to 26%) of earthquake occurrence over the next 50 years. We observed similarities between seismic histories of several faults belonging to two adjacent fault systems. This could again attest that non-random processes occurring in the release of the strain accumulated on faults, commonly referred to as fault interactions and leading to apparent synchronization. If these processes were determined as being the main parameter controlling the occurrence of earthquakes, it would be crucial to take them into account in seismic hazard models.

  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. Active Fault Detection and Isolation for Hybrid Systems

    DEFF Research Database (Denmark)

    Gholami, Mehdi; Schiøler, Henrik; Bak, Thomas;

    2009-01-01

    An algorithm for active fault detection and isolation is proposed. In order to observe the failure hidden due to the normal operation of the controllers or the systems, an optimization problem based on minimization of test signal is used. The optimization based method imposes the normal and faulty...... 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...

  7. Sensor Fault Detection and Diagnosis for autonomous vehicles

    Directory of Open Access Journals (Sweden)

    Realpe Miguel

    2015-01-01

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

  8. Study on Knowledge -based Intelligent Fault Diagnosis of Hydraulic System

    Directory of Open Access Journals (Sweden)

    Xuexia Liu

    2012-12-01

    Full Text Available A general framework of hydraulic fault diagnosis system was studied. It consisted of equipment knowledge bases, real-time databases, fusion reasoning module, knowledge acquisition module and so on. A tree-structure model of fault knowledge was established. Fault nodes knowledge was encapsulated by object-oriented technique. Complete knowledge bases were made including fault bases and diagnosis bases. It could describe the fault positions, the structure of fault, cause-symptom relationships, diagnosis principles and other knowledge. Taking the fault of left and right lifting oil cylinder out of sync for example, the diagnostic results show that the methods were effective.

  9. 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.

  10. Detection of fault structures with airborne LiDAR point-cloud data

    Science.gov (United States)

    Chen, Jie; Du, Lei

    2015-08-01

    The airborne LiDAR (Light Detection And Ranging) technology is a new type of aerial earth observation method which can be used to produce high-precision DEM (Digital Elevation Model) quickly and reflect ground surface information directly. Fault structure is one of the key forms of crustal movement, and its quantitative description is the key to the research of crustal movement. The airborne LiDAR point-cloud data is used to detect and extract fault structures automatically based on linear extension, elevation mutation and slope abnormal characteristics. Firstly, the LiDAR point-cloud data is processed to filter out buildings, vegetation and other non-surface information with the TIN (Triangulated Irregular Network) filtering method and Burman model calibration method. TIN and DEM are made from the processed data sequentially. Secondly, linear fault structures are extracted based on dual-threshold method. Finally, high-precision DOM (Digital Orthophoto Map) and other geological knowledge are used to check the accuracy of fault structure extraction. An experiment is carried out in Beiya Village of Yunnan Province, China. With LiDAR technology, results reveal that: the airborne LiDAR point-cloud data can be utilized to extract linear fault structures accurately and automatically, measure information such as height, width and slope of fault structures with high precision, and detect faults in areas with vegetation coverage effectively.

  11. Detection and Quantization of Bearing Fault in Direct Drive Wind Turbine via Comparative Analysis

    Directory of Open Access Journals (Sweden)

    Wei Teng

    2016-01-01

    Full Text Available Bearing fault is usually buried by intensive noise because of the low speed and heavy load in direct drive wind turbine (DDWT. Furthermore, varying wind speed and alternating loads make it difficult to quantize bearing fault feature that indicates the degree of deterioration. This paper presents the application of multiscale enveloping spectrogram (MuSEnS and cepstrum to detect and quantize bearing fault in DDWT. MuSEnS can manifest fault modulation information adaptively based on the capacity of complex wavelet transform, which enables the weak bearing fault in DDWT to be detected. Cepstrum can calculate the average interval of periodic components in frequency domain and is suitable for quantizing bearing fault feature under varying operation conditions due to the logarithm weight on the power spectrum. Through comparing a faulty DDWT with a normal one, the bearing fault feature is evidenced and the quantization index is calculated, which show a good application prospect for condition monitoring and fault diagnosis in real DDWT.

  12. 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...

  13. 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...

  14. A Unified Framework for Fault Detection and Diagnosis Using Particle Filter

    Directory of Open Access Journals (Sweden)

    Bo Zhao

    2014-10-01

    Full Text Available In this paper, a particle filter (PF based fault detection and diagnosis framework is proposed. A system with possible faults is modeled as a group of hidden Markov models representing the system in fault-free mode and different failure modes, and a first order Markov chain is modeling the system mode transitions. A modified particle filter algorithm is developed to estimate the system states and mode. By doing this, system faults are detected when estimating the system mode, and the size of the fault is diagnosed by estimating the system state. A new resampling method is also developed for running the modified PF efficiently. Two introductory examples and a case study are given in detail. The introduction examples demonstrate the manner to model a system with possible faults into hidden Markov model and Markov chain. The case study considers a numerical model with common measurement failure modes. It focuses on the verification of the proposed fault diagnosis and detection algorithm and shows the behavior of the particle filter.

  15. Application of Uncertainty Reasoning Theory to satellite Fault Detection and Diagnosis

    Institute of Scientific and Technical Information of China (English)

    YangTianshe; LiHuaizu; SunYanbong

    2004-01-01

    Reasoning theories are divided into certainty reasoning theories and uncertainty reasoning theories.Now,only certainty reason-ing theories use to deitcs are used to detect and diagnose satellite faults.However,in practice,it is difficult to detect and diagnose some faults of the satellite autiomatically only by use of ccrtainty.Fortunately.uncerlainty Reasoning theories are applied to detect and diagnose satellite faults.Uncertainty reasoning theories include several kinds of theories,such as inclusion degree theory,rough set theory,evidence reasoning theory,probabilisticresoning theory,fuzzy,fuzzy reasoningteory,and so on.Inclusion degree theory.rough set theory and evidence reasoning theory are three advanced ones,Based on these three theories respectively.the audhor introduces three new methods to detect and diagnose satellite faults in this paper.It is shown that the methods,suitable for detecting and diagnosing satellite faults,especially uncertainty faults,can remedy the defects of the current methods.

  16. Concurrent Detection and Classification of Faults in Matrix Converter using Trans-Conductance

    OpenAIRE

    Sarah Azimi; Mehdi Vejdaniamiri

    2014-01-01

    This paper presents a fault diagnostic algorithm for detecting and locating open-circuit and short-circiut faults in switching components of matrix converters (MCs) which can be effectively used to drive a permanent magnet synchronous motor for research in critical applications. The proposed method is based on monitoring the voltages and currents of the switches. These measurements are used to evaluate the forward trans-conductance of each transistor for different values of switch voltages. T...

  17. Model based fault diagnosis in a centrifugal pump application using structural analysis

    DEFF Research Database (Denmark)

    Kallesøe, C. S.; Izadi-Zamanabadi, Roozbeh; Rasmussen, Henrik;

    2004-01-01

    A model based approach for fault detection and isolation in a centrifugal pump is proposed in this paper. The fault detection algorithm is derived using a combination of structural analysis, Analytical Redundant Relations (ARR) and observer designs. Structural considerations on the system are used...

  18. Model Based Fault Diagnosis in a Centrifugal Pump Application using Structural Analysis

    DEFF Research Database (Denmark)

    Kallesøe, C. S.; Izadi-Zamanabadi, Roozbeh; Rasmussen, Henrik;

    2004-01-01

    A model based approach for fault detection and isolation in a centrifugal pump is proposed in this paper. The fault detection algorithm is derived using a combination of structural analysis, Analytical Redundant Relations (ARR) and observer designs. Structural considerations on the system are used...

  19. 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.

  20. Health Monitoring of Offshore Wind Turbines Online Fault Detection and Identification Module Test Case: Pitch Offset

    DEFF Research Database (Denmark)

    Perisic, Nevena; Pedersen, Bo Juul; Kirkegaard, Poul Henning

    LACobserver is a model based health monitoring (HM) system for wind turbines (WTGs) which provides an intuitive engineering link between load and strength parameters. The present work demonstrates a newly developed LACobserver Fault Detection and Identification (FDI) module for online detection...

  1. Study of fault injection system based on software

    Institute of Scientific and Technical Information of China (English)

    2007-01-01

    A software fault injection system SFIS is designed, which consists of the target system plus a fault injector, fault library, workload, data collector, and data analyzer. A serial communication mechanism is adopted to simulate the factual work environment. Then a fault model is built for single particle event, which can be denoted as FM = (FL, FT). FL stands for fault location, and FT stands for fault type. The fault model supports three temporal faults: transient, intermittent, and permanent. During the experiments implemented by SFIS,the software interruption method is adopted to inject transient faults, and step trace method is adopted to inject permanent faults into the target system. The experiment results indicate that for the injected transient code segment faults, 2.8% of them do not affect the program output, 80.1% of them are detected by the built-in error detection in the system, and 17.1% of them are not detected by fault detection mechanism. The experiment results verify the validity of the fault injection method.

  2. Detection and classification of winding faults in windmill generators using Wavelet Transform and ANN

    Energy Technology Data Exchange (ETDEWEB)

    Gketsis, Zacharias E.; Zervakis, Michalis E.; Stavrakakis, George [Department of Electronics and Computer Engineering, Technical University of Crete, Chania 73100 (Greece)

    2009-11-15

    This paper exploits the Wavelet Transform (WT) analysis along with Artificial Neural Networks (ANN) for the diagnosis of electrical machines winding faults. A novel application is presented exploring the problem of automatically identifying short circuits of windings, which often appear during machine manufacturing and operation. Such faults are usually resulting from electrodynamics forces generated during the flow of large short circuit currents, as well as forces occurring when the machines are transported. The early detection and classification of winding failures is of particular importance, as these kinds of defects can lead to winding damage due to overheating, imbalance, etc. Application results and investigations of windmill generator winding turn-to-turn faults between adjacent winding wires are presented. The ANN approach is proven effective in detecting and classifying faults based on WT features extracted from high frequency measurements of the admittance, current, or voltage responses. (author)

  3. 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

    account 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......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...... 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...

  4. Functional Fault Modeling of a Cryogenic System for Real-Time Fault Detection and Isolation

    Science.gov (United States)

    Ferrell, Bob; Lewis, Mark; Perotti, Jose; Oostdyk, Rebecca; Brown, Barbara

    2010-01-01

    The purpose of this paper is to present the model development process used to create a Functional Fault Model (FFM) of a liquid hydrogen (L H2) system that will be used for realtime fault isolation in a Fault Detection, Isolation and Recover (FDIR) system. The paper explains th e steps in the model development process and the data products required at each step, including examples of how the steps were performed fo r the LH2 system. It also shows the relationship between the FDIR req uirements and steps in the model development process. The paper concl udes with a description of a demonstration of the LH2 model developed using the process and future steps for integrating the model in a live operational environment.

  5. Fault detection and optimization for networked control systems with uncertain time-varying delay

    Institute of Scientific and Technical Information of China (English)

    Qing Wang; Zhaolei Wang; Chaoyang Dong; Erzhuo Niu

    2015-01-01

    The observer-based robust fault detection filter design and optimization for networked control systems (NCSs) with uncer-tain time-varying delays are addressed. The NCSs with uncertain time-varying delays are modeled as parameter-uncertain systems by the matrix theory. Based on the model, an observer-based residual generator is constructed and the sufficient condition for the existence of the desired fault detection filter is derived in terms of the linear matrix inequality. Furthermore, a time domain opti-mization approach is proposed to improve the performance of the fault detection system. To prevent the false alarms, a new thresh-old function is established, and the solution of the optimization problem is given by using the singular value decomposition (SVD) of the matrix. A numerical example is provided to il ustrate the effectiveness of the proposed approach.

  6. 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....... By analyzing and reconstructing the fault signals, it is easy to detect the fault characteristic frequency and see the characteristic frequencies of angle faults depend on the shaft rotating frequency, which is known as the 1P frequency and 3P frequency distinctly....

  7. Modeling, estimation, fault detection and fault diagnosis of spacecraft air contaminants

    Science.gov (United States)

    Narayan, Anand P.

    1998-07-01

    The objective of this dissertation is to develop a framework for the modeling, estimation, fault detection and diagnosis of air contaminants aboard spacecraft. Safe air is a vital resource aboard spacecraft for crewed missions, and especially so in long range missions, where the luxury of returning to earth for a clean-up does not exist. This research uses modern control theory in conjunction with advanced fluid mechanics to achieve the objective of developing an implementable comprehensive monitoring systems, suitable for use on space missions. First, a three-dimensional transport model is developed in order to model the dispersion of air contaminants. The flow field, which is an important input to the transport model, is obtained by solving the Navier Stokes equations for the cabin geometry and the appropriate boundary conditions, using a finite element method. Steady flow fields are computed for various conditions for both laminar and turbulent cases. Contamination dispersion studies are undertaken both for routine substances introduced through the inlet ducts and for emissions of toxics inside the cabin volume. The dispersion studies indicate that lumped models and even a two-dimensional model are sometimes inadequate to assure that the Spacecraft Maximum Allowable Concentrations (SMACs) are not exceeded locally. Since the research was targeted at real-time application aboard Spacecraft, a state estimation routine is implemented using Implicit Kalman Filtering. The routine makes use of the model predictions and measurements from the sensor system in order to arrive at an optimal estimate of the state of the system for each time step. Fault detection is accomplished through the use of analytical redundancy, where error residuals from the Kalman filter are monitored in order to detect any faults in the system, and to distinguish between sensor and process faults. Finally, a fault diagnosis system is developed, which is a combination of sensitivity analysis and an

  8. 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.

  9. Detection of Inter-turn Faults in Five-Phase Permanent Magnet Synchronous Motors

    Directory of Open Access Journals (Sweden)

    SAAVEDRA, H.

    2014-11-01

    Full Text Available Five-phase permanent magnet synchronous motors (PMSMs have inherent fault-tolerant capabilities. This paper analyzes the detection of inter-turn short circuit faults in five-phase PMSMs in their early stage, i.e. with only one turn in short circuit by means of the analysis of the stator currents and the zero-sequence voltage component (ZSVC spectra. For this purpose, a parametric model of five-phase PMSMs which accounts for the effects of inter-turn short circuits is developed to determine the most suitable harmonic frequencies to be analyzed to detect such faults. The amplitudes of these fault harmonic are analyzed in detail by means of finite-elements method (FEM simulations, which corroborate the predictions of the parametric model. A low-speed five-phase PMSM for in-wheel applications is studied and modeled. This paper shows that the ZSVC-based method provides better sensitivity to diagnose inter-turn faults in the analyzed low-speed application. Results presented under a wide speed range and different load levels show that it is feasible to diagnose such faults in their early stage, thus allowing applying a post-fault strategy to minimize their effects while ensuring a safe operation.

  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. Robust Fault Detection Using Robust Z1 Estimation and Fuzzy Logic

    Science.gov (United States)

    Curry, Tramone; Collins, Emmanuel G., Jr.; Selekwa, Majura; Guo, Ten-Huei (Technical Monitor)

    2001-01-01

    This research considers the application of robust Z(sub 1), estimation in conjunction with fuzzy logic to robust fault detection for an aircraft fight control system. It begins with the development of robust Z(sub 1) estimators based on multiplier theory and then develops a fixed threshold approach to fault detection (FD). It then considers the use of fuzzy logic for robust residual evaluation and FD. Due to modeling errors and unmeasurable disturbances, it is difficult to distinguish between the effects of an actual fault and those caused by uncertainty and disturbance. Hence, it is the aim of a robust FD system to be sensitive to faults while remaining insensitive to uncertainty and disturbances. While fixed thresholds only allow a decision on whether a fault has or has not occurred, it is more valuable to have the residual evaluation lead to a conclusion related to the degree of, or probability of, a fault. Fuzzy logic is a viable means of determining the degree of a fault and allows the introduction of human observations that may not be incorporated in the rigorous threshold theory. Hence, fuzzy logic can provide a more reliable and informative fault detection process. Using an aircraft flight control system, the results of FD using robust Z(sub 1) estimation with a fixed threshold are demonstrated. FD that combines robust Z(sub 1) estimation and fuzzy logic is also demonstrated. It is seen that combining the robust estimator with fuzzy logic proves to be advantageous in increasing the sensitivity to smaller faults while remaining insensitive to uncertainty and disturbances.

  12. Induction motor inter turn fault detection using infrared thermographic analysis

    Science.gov (United States)

    Singh, Gurmeet; Anil Kumar, T. Ch.; Naikan, V. N. A.

    2016-07-01

    Induction motors are the most commonly used prime movers in industries. These are subjected to various environmental, thermal and load stresses that ultimately reduces the motor efficiency and later leads to failure. Inter turn fault is the second most commonly observed faults in the motors and is considered the most severe. It can lead to the failure of complete phase and can even cause accidents, if left undetected or untreated. This paper proposes an online and non invasive technique that uses infrared thermography, in order to detect the presence of inter turn fault in induction motor drive. Two methods have been proposed that detect the fault and estimate its severity. One method uses transient thermal monitoring during the start of motor and other applies pseudo coloring technique on infrared image of the motor, after it reaches a thermal steady state. The designed template for pseudo-coloring is in acquiescence with the InterNational Electrical Testing Association (NETA) thermographic standard. An index is proposed to assess the severity of the fault present in the motor.

  13. Rotor Faults Detection in Induction Motor by Wavelet Analysis

    Directory of Open Access Journals (Sweden)

    Neelam Mehala

    2009-12-01

    Full Text Available Motor current signature analysis has been successfully used for fault diagnosis in induction motors. However, this method does not always achieve good results when the speed or the load torque is not constant, because this cause variation on the motor slip and fast Fourier transform problems appear due to non-stationary signal. This paper experimentally describes the effects of rotor broken bar fault in the stator current of induction motor operating under non-constant load conditions. To achieve this, broken rotor bar fault is eplicated in a laboratory and its effect on the motor current has been studied. To diagnose the broken rotor bar fault, a new approach based on wavelet transform is applied by using ‘Labview 8.2 software’ of National Instrument (NI. The diagnosis procedure was performed by using the virtual instruments. The theoretical basis of proposed method is proved by laboratory tests.

  14. Fault detection based on two-stage learning and semi-supervised SVM%基于两阶段学习的半监督SVM故障检测方法

    Institute of Scientific and Technical Information of China (English)

    陶新民; 曹盼东; 宋少宇; 付丹丹

    2012-01-01

    提出一种基于两阶段学习的半监督SVM故障检测方法.该方法首先使用标识传递算法给未标识样本赋予初始伪标识,并通过k近邻图对比样本点标识值,将可能是噪声的样本点识别并剔除;然后将去噪处理后的样本集输入到SVM中,使得SVM在训练时能兼顾整个样本集的信息,从而提高SVM的故障检测性能.实验中将该方法同支持向量机(SVM)、模糊支持向量机(FSVM)、直推式支持向量机(TSVM)及拉普拉斯支持向量机算法(LapSVM)进行比较,结果表明该方法在不同数目标识样本集合的情况下,检测精度较其他算法有较大幅度提高,同时该方法还比较了不含测试样本和含测试样本训练条件下的故障检测性能,结果表明结合测试样本可进一步提高算法的故障检测性能.%A novel semi-supervised support vector machine fault detection model based on two-stage learning was presented here. First of all, propagation algorithm was used to provide pseudo labels for unlabeled samples. Secondly, k-near-neighbor graph was utilized to distinguish and delete the noisy samples. Then, the denoised samples were input into a support vector machine (SVM) so that the global information of the whole sample set could be considered by the SVM to enhance its fault detection ability. The comparisons among support vector machine, fuzzy support vector machine, transductive support vector machine and Laplacian support vector machine fault detection algorithms were performed. The results showed that the proposed approach has a more substantial detection accuracy than other algorithms with different labeled sample sets. The behaviors of the proposed fault detection method with test samples and without those were compared. The results illustrated that the proposed method with test samples can further raise its ability to detect faults.

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

    Science.gov (United States)

    Lu, Feng; Huang, Jinquan; Xing, Yaodong

    2012-01-01

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

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

    Science.gov (United States)

    Lu, Feng; Huang, Jinquan; Xing, Yaodong

    2012-01-01

    Combining a simplified on-board turbo-shaft model with sensor fault diagnostic logic, a model-based sensor fault diagnosis method is proposed. The existing fault diagnosis method for turbo-shaft engine key sensors is mainly based on a double redundancies technique, and this can't be satisfied in some occasions as lack of judgment. The simplified on-board model provides the analytical third channel against which the dual channel measurements are compared, while the hardware redundancy will increase the structure complexity and weight. The simplified turbo-shaft model contains the gas generator model and the power turbine model with loads, this is built up via dynamic parameters method. Sensor fault detection, diagnosis (FDD) logic is designed, and two types of sensor failures, such as the step faults and the drift faults, are simulated. When the discrepancy among the triplex channels exceeds a tolerance level, the fault diagnosis logic determines the cause of the difference. Through this approach, the sensor fault diagnosis system achieves the objectives of anomaly detection, sensor fault diagnosis and redundancy recovery. Finally, experiments on this method are carried out on a turbo-shaft engine, and two types of faults under different channel combinations are presented. The experimental results show that the proposed method for sensor fault diagnostics is efficient. PMID:23112645

  17. Architecture for Intrusion Detection System with Fault Tolerance Using Mobile Agent

    Directory of Open Access Journals (Sweden)

    Chintan Bhatt

    2011-10-01

    Full Text Available This paper is a survey of the work, done for making an IDS fault tolerant.Architecture of IDS that usesmobile Agent provides higher scalability. Mobile Agent uses Platform for detecting Intrusions using filterAgent, co-relater agent, Interpreter agent and rule database. When server (IDS Monitor goes down,other hosts based on priority takes Ownership. This architecture uses decentralized collection andanalysis for identifying Intrusion. Rule sets are fed based on user-behaviour or applicationbehaviour.This paper suggests that intrusion detection system (IDS must be fault tolerant; otherwise, theintruder may first subvert the IDS then attack the target system at will.

  18. Research on leak fault intelligent detection method for fluid pipeline based on fuzzy classification%基于模糊分类的流体管道泄漏故障智能检测方法研究

    Institute of Scientific and Technical Information of China (English)

    刘金海; 冯健

    2011-01-01

    本文针对基于负压波法管道泄漏实时检测系统误报高和灵敏度低的问题提出一种流体管道泄漏故障智能检测方法,该方法首先给出管道运行参数的确定模型,然后结合模糊算子给出流体管道状态模糊模型,进而利用该模型实现管道故障分类.以这种智能检测方法为核心设计流体管道故泄漏故障智能诊断系统(leak intelligent diagnosis system for fluid pipeline,LIDSFP),通过对某成品油管道实例仿真和在流体管道测试系统上的试验研究,给出了LIDSFP性能指标,进一步分析表明该系统可以有效完成流体管道的泄漏故障诊断.%A leak fault intelligent detection method for fluid pipeline based on fuzzy classifier is proposed, which can decrease false alarms and improve leak detection sensitivity. To complete real-time and exact fault diagnosis of fluid pipeline, a fuzzy classifier for operation states is designed according to the framework of fuzzy expert system. A leak fault intelligent diagnosis system for fluid pipeline ( LIDSFP) was designed with this intelligent method; simulation was carried out to detect faults from the historic operation data of certain petrolatum product fluid pipeline in China.Test study was also carried out on the proposed system and the performance specification of the system is given. Simulation and test results show that the proposed diagnosis system has good performance.

  19. Soft computing applications in high impedance fault detection in distribution systems

    Energy Technology Data Exchange (ETDEWEB)

    Sedighi, A.-R.; Haghifam, M.-R. [Department of Electrical Engineering, Tarbiat Modarres University, P.O. Box 14115-111, Tehran (Iran); Malik, O.P. [Department of Electrical and Computer Engineering, University of Calgary, Calgary, Alberta (Canada T2N 1N4)

    2005-09-15

    Two methods, one based on genetic algorithm (GA) and one based on neural networks (NN), are proposed for high impedance fault (HIF) detection in distribution systems. These methods are used to discriminate HIFs from isolator leakage current (ILC) and transients such as capacitor switching, load switching (high/low voltage), ground fault, inrush current and no load line switching. Wavelet transform is used for the decomposition of signals and feature extraction in both methods. In one method, GA is used for feature vector reduction and Bayes for classification. In the other method, principal component analysis (PCA) is applied for feature vector reduction and NN for classification. HIF and ILC data was acquired by experimental tests and the data for other faults was obtained by simulating a real network using EMTP. Results show that either of the proposed procedures can be used to identify HIF from other events efficiently. (author) [Bayes classifier; High impedance fault; Genetic algorithm; Neural network; Principal component analysis; Wavelet transform].

  20. Research on intelligent fault diagnosis based on time series analysis algorithm

    Institute of Scientific and Technical Information of China (English)

    CHEN Gang; LIU Yang; ZHOU Wen-an; SONG Jun-de

    2008-01-01

    Aiming to realize fast and accurate fault diagnosisin complex network environment, this article proposes a set ofanomaly detection algorithm and intelligent fault diagnosismodel. Firstly, a novel anomaly detection algorithm based ontime series analysis is put forward to improve the generalizedlikelihood ratio (GLR) test, and thus, detection accuracy isenhanced and the algorithm complexity is reduced. Secondly,the intelligent fault diagnosis model is established byintroducing neural network technology, and thereby, theanomaly information of each node in end-to-end network isintegrated and processed in parallel to intelligently diagnosethe fault cause. Finally, server backup solution in enterpriseinformation network is taken as the simulation scenario. Theresults demonstrate that the proposed method can not onlydetect fault occurrence in time, but can also implement onlinediagnosis for fault cause, and thus, real-time and intelligent faultmanagement process is achieved.

  1. 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.

  2. 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.

  3. 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.

  4. GLRT Based Anomaly Detection for Sensor Network Monitoring

    KAUST Repository

    Harrou, Fouzi

    2015-12-07

    Proper operation of antenna arrays requires continuously monitoring their performances. When a fault occurs in an antenna array, the radiation pattern changes and can significantly deviate from the desired design performance specifications. In this paper, the problem of fault detection in linear antenna arrays is addressed within a statistical framework. Specifically, a statistical fault detection method based on the generalized likelihood ratio (GLR) principle is utilized for detecting potential faults in linear antenna arrays. The proposed method relies on detecting deviations in the radiation pattern of the monitored array with respect to a reference (fault-free) one. To assess the abilities of the GLR based fault detection method, three case studies involving different types of faults have been performed. The simulation results clearly illustrate the effectiveness of the GLR-based fault detection method in monitoring the performance of linear antenna arrays.

  5. Fault diagnosis algorithm based on switching function for boost converters

    Science.gov (United States)

    Cho, H.-K.; Kwak, S.-S.; Lee, S.-H.

    2015-07-01

    A fault diagnosis algorithm, which is necessary for constructing a reliable power conversion system, should detect fault occurrences as soon as possible to protect the entire system from fatal damages resulting from system malfunction. In this paper, a fault diagnosis algorithm is proposed to detect open- and short-circuit faults that occur in a boost converter switch. The inductor voltage is abnormally kept at a positive DC value during a short-circuit fault in the switch or at a negative DC value during an open-circuit fault condition until the inductor current becomes zero. By employing these abnormal properties during faulty conditions, the inductor voltage is compared with the switching function to detect each fault type by generating fault alarms when a fault occurs. As a result, from the fault alarm, a decision is made in response to the fault occurrence and the fault type in less than two switching time periods using the proposed algorithm constructed in analogue circuits. In addition, the proposed algorithm has good resistivity to discontinuous current-mode operation. As a result, this algorithm features the advantages of low cost and simplicity because of its simple analogue circuit configuration.

  6. Multiscale Permutation Entropy Based Rolling Bearing Fault Diagnosis

    OpenAIRE

    Jinde Zheng; Junsheng Cheng; Yu Yang

    2014-01-01

    A new rolling bearing fault diagnosis approach based on multiscale permutation entropy (MPE), Laplacian score (LS), and support vector machines (SVMs) is proposed in this paper. Permutation entropy (PE) was recently proposed and defined to measure the randomicity and detect dynamical changes of time series. However, for the complexity of mechanical systems, the randomicity and dynamic changes of the vibration signal will exist in different scales. Thus, the definition of MPE is introduced and...

  7. NEW METHOD FOR WEAK FAULT FEATURE EXTRACTION BASED ON SECOND GENERATION WAVELET TRANSFORM AND ITS APPLICATION

    Institute of Scientific and Technical Information of China (English)

    Duan Chendong; He Zhengjia; Jiang Hongkai

    2004-01-01

    A new time-domain analysis method that uses second generation wavelet transform (SGWT) for weak fault feature extraction is proposed. To extract incipient fault feature, a biorthogonal wavelet with the characteristics of impact is constructed by using SGWT. Processing detail signal of SGWT with a sliding window devised on the basis of rotating operation cycle, and extracting modulus maximum from each window, fault features in time-domain are highlighted. To make further analysis on the reason of the fault, wavelet package transform based on SGWT is used to process vibration data again. Calculating the energy of each frequency-band, the energy distribution features of the signal are attained. Then taking account of the fault features and the energy distribution, the reason of the fault is worked out. An early impact-rub fault caused by axis misalignment and rotor imbalance is successfully detected by using this method in an oil refinery.

  8. Effective confidence interval estimation of fault-detection process of software reliability growth models

    Science.gov (United States)

    Fang, Chih-Chiang; Yeh, Chun-Wu

    2016-09-01

    The quantitative evaluation of software reliability growth model is frequently accompanied by its confidence interval of fault detection. It provides helpful information to software developers and testers when undertaking software development and software quality control. However, the explanation of the variance estimation of software fault detection is not transparent in previous studies, and it influences the deduction of confidence interval about the mean value function that the current study addresses. Software engineers in such a case cannot evaluate the potential hazard based on the stochasticity of mean value function, and this might reduce the practicability of the estimation. Hence, stochastic differential equations are utilised for confidence interval estimation of the software fault-detection process. The proposed model is estimated and validated using real data-sets to show its flexibility.

  9. Sensor fault detection and isolation in doubly-fed induction generators accounting for parameter variations

    Energy Technology Data Exchange (ETDEWEB)

    Galvez-Carrillo, Manuel; Kinnaert, Michel [Dept. of Control Engineering and System Analysis, Universite Libre de Bruxelles (ULB), 50 Av. F.D. Roosevelt, CP 165/55, B-1050 Brussels (Belgium)

    2011-05-15

    A fault detection and isolation (FDI) system for monitoring rotor current sensors in a doubly-fed induction generator (DFIG) for wind turbine applications is presented. The FDI system is designed so that the effect of parameter variations (resistances and inductances) is minimized. The residual generation is based on the generalized observer scheme (GOS) including parameter estimation. A decision system made of a combination of vector CUSUM (Cumulative sum) algorithms is used to process the residual vector and to achieve detection and isolation of incipient (small magnitude) faults. The approach is validated using signals obtained from a simulated vector-controlled DFIG. (author)

  10. Application of Wavelets Transform to Fault Detection in Rotorcraft UAV Sensor Failure

    Institute of Scientific and Technical Information of China (English)

    Jun-tong Qi; Jian-da Han

    2007-01-01

    This paper describes a novel wavelet-based approach to the detection of abrupt fault of Rotorcraft Unmanned Aerial Vehicle (RUAV) sensor system. By use of wavelet transforms that accurately localize the characteristics of a signal both in the time and frequency domains, the occurring instants of abnormal status of a sensor in the output signal can be identified by the multi-scale representation of the signal. Once the instants are detected, the distribution differences of the signal energy on all decomposed wavelet scales of the signal before and after the instants are used to claim and classify the sensor faults.

  11. The Marshall Space Flight Center Fault Detection Diagnosis and Recovery Laboratory

    Science.gov (United States)

    Burchett, Bradley T.; Gamble, Jonathan; Rabban, Michael

    2008-01-01

    The Fault Detection Diagnosis and Recovery Lab (FDDR) has been developed to support development of,fault detection algorithms for the flight computer aboard the Ares I and follow-on vehicles. It consists of several workstations using Ethernet and TCP/IP to simulate communications between vehicle sensors, flight computers, and ground based support computers. Isolation of tasks between workstations was set up intentionally to limit information flow and provide a realistic simulation of communication channels within the vehicle and between the vehicle and ground station.

  12. MICROTHREAD BASED (MTB) COARSE GRAINED FAULT TOLERANCE SUPERSCALAR PROCESSOR ARCHITECTURE

    Institute of Scientific and Technical Information of China (English)

    2006-01-01

    Fault tolerance in microprocessor systems has become a popular topic of architecture research.Much work has been done at different levels to accomplish reliability against soft errors, and some fault tolerance architectures have been proposed. But little attention is paid to the thread level superscalar fault tolerance.This letter introduces microthread concept into superscalar processor fault tolerance domain, and puts forward a novel fault tolerance architecture, namely, MicroThread Based (MTB) coarse grained transient fault tolerance superscalar processor architecture, then discusses some detailed implementations.

  13. Model-Based Fault Tolerant Control for Hybrid Dynamic Systems with Sensor Faults%一类带有传染器故障的混合系统的容错控制

    Institute of Scientific and Technical Information of China (English)

    杨浩; 冒泽慧; 姜斌

    2006-01-01

    A model-based fault tolerant control approach for hybrid linear dynamic systems is proposed in this paper. The proposed method, taking advantage of reliable control, can maintain the performance of the faulty system during the time delay of fault detection and diagnosis (FDD) and fault accommodation (FA), which can be regarded as the first line of defence against sensor faults.Simulation results of a three-tank system with sensor fault are given to show the efficiency of the method.

  14. Robust Fault Detection of Linear Uncertain Time-Delay Systems Using Unknown Input Observers

    Directory of Open Access Journals (Sweden)

    Saeed Ahmadizadeh

    2013-01-01

    Full Text Available This paper deals with the problem of fault detection for linear uncertain time-delay systems. The proposed method for Luenberger observers is developed for unknown input observers (UIOs, and a novel procedure for the design of residual based on UIOs is presented. The design procedure is carried out based on the model matching approach which minimizes the difference between generated residuals by the optimal observer and those by the designed observer in the presence of uncertainties. The optimal observer is designed for the ideal system and works so that the fault effect is maximized while the exogenous disturbances and noise effects are minimized. This observer can give disturbance decoupling in the presence of noise and uncertainties for linear uncertain time-delay systems. The developed method is applied to a numerical example, and the simulation results show that the proposed approach is able to detect faults reliably in the presence of modeling errors, disturbances, and noise.

  15. The Method of Earth-Fault Detection in Distribution Network Based on Transient Signal%基于暂态信号的配电网接地故障检测方法

    Institute of Scientific and Technical Information of China (English)

    钟奕; 李彩林; 黄知超; 刘木

    2011-01-01

    针对配电网接地故障暂态信号的产生过程及其传播特点,提出了在线路区段边界处并联谐振滤波电路使检测装置两侧特征频带暂态电流能量产生差异,通过比较特征频带内暂态电流能量的幅值实现接地故障区段定位方法,成功设计谐振滤波装置和接地故障检测装置.采用FPGA+DSP的并行处理结构,并结合GSM无线通信网络实现整个故障检测装置的功能,有效地提高了整个故障检测装置的实时性和可靠性.验证试验表明,该装置不仅能有效地实现接地故障选线功能,而且能够快速可靠地选出接地故障所发生区域.%Arming at the generating process and propagation characteristics of earth-fault transient signal, an earth fault location method for distribution network based on transient signal was presented. In the method, a resonant filter circuit was paralleled in line segment border, so that it could cause transient current energy differences in feature frequency band on both sides of detection device. The location method achieved earth-fault area location through comparing transient current energy in feature frequency band. A resonant filter device and an earth-fault location device were designed. The detection device was designed with GSM wireless network and parallel processing structure of FPGA and DSP, so that the real-time and reliability of the instrument were improved effectively. The test result showed that the instrument not only could select the faulty line effectively but also detect the earth-fault area rapidly and reliably in distribution network.

  16. 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-Kalman F...

  17. Research of singular value decomposition based on slip matrix for rolling bearing fault diagnosis

    Science.gov (United States)

    Cong, Feiyun; Zhong, Wei; Tong, Shuiguang; Tang, Ning; Chen, Jin

    2015-05-01

    Rolling element bearings are at the heart of most rotating machines and they bear the function of connectivity between the rotor and stator. It is important to distinguish the incipient fault before the bearing step into serious failure. The Slip Matrix (SM) construction method based on Singular Value Decomposition (SVD) is proposed in this paper. The SM based fault feature extraction and impulses intelligent detection methods are introduced as the key steps for rolling bearing fault diagnosis. The numerical simulation of rolling bearing fault signal is adopted which shows that the proposed method is good at fault impulses detection in strong background noise environment. The rolling element bearing accelerated life test is performed for the acquisition of experimental data of rolling bearing fault. With the rolling bearing running from normal state to failure, the initial fault signal part can be picked out from the whole life vibration data of the rolling bearing. The vibration signal is close to the nature fault signal which is acquired from a rolling bearing applied in industrial field. The analysis result shows that the proposed method has an excellent performance in rolling bearing fault detection.

  18. Optimal Threshold Functions for Fault Detection and Isolation

    DEFF Research Database (Denmark)

    Stoustrup, Jakob; Niemann, H.; Harbo, Anders La-Cour

    2003-01-01

    Fault diagnosis systems usually comprises two parts: a filtering part and a decision part, the latter typically based on threshold functions. In this paper, systematic ways to choose the threshold values are proposed. Two different test functions for the filtered signals are discussed and a method...

  19. Fault Location Based on Synchronized Measurements: A Comprehensive Survey

    Directory of Open Access Journals (Sweden)

    A. H. Al-Mohammed

    2014-01-01

    Full Text Available This paper presents a comprehensive survey on transmission and distribution fault location algorithms that utilize synchronized measurements. Algorithms based on two-end synchronized measurements and fault location algorithms on three-terminal and multiterminal lines are reviewed. Series capacitors equipped with metal oxide varistors (MOVs, when set on a transmission line, create certain problems for line fault locators and, therefore, fault location on series-compensated lines is discussed. The paper reports the work carried out on adaptive fault location algorithms aiming at achieving better fault location accuracy. Work associated with fault location on power system networks, although limited, is also summarized. Additionally, the nonstandard high-frequency-related fault location techniques based on wavelet transform are discussed. Finally, the paper highlights the area for future research.

  20. 基于SR-UKF的移动机器人主动故障检测和容错控制%SR-UKF based active fault detection and tolerant control of mobile robots

    Institute of Scientific and Technical Information of China (English)

    周波; 戴先中

    2011-01-01

    提出了一种基于SR-UKF的主动状态建模方法用于移动机器人的在线故障检测和容错跟踪控制.通过对履带式机器人常见滑动故障的运动学分析,建立了带未知滑动故障参数的机器人运动学模型,并采用SR-UKF非线性滤波方法来联合估计机器人的位姿和滑动参数,在对机器人进行实时定位的同时实现了对快速变化(或突变)的滑动故障的在线跟踪和检测.在此基础上,将估计得到的自适应参数模型与基于Lyapunov分析的反馈控制律设计方法结合,获得了一致渐近稳定的轨迹跟踪控制结果,实现了针对在线故障自适应模型的容错控制重构.针对典型的阶跃式滑动故障参数变化的轨迹跟踪仿真实验表明了该方法的有效性.%A square-rooted unscented Kalman filter (SR-UKF) based active state model is proposed for online fault detection and tolerant tracking control of mobile robots. The common slipping fault of a tracked vehicle is considered and the corresponding kinematic model with unknown slipping parameters is created. The slipping parameters as well as the pose of the robot are estimated simultaneously by the joint estimation framework to realize the real-time localization of robots and online identification and detection of the fast changing (or even abrupt changing) slipping fault. The obtained adaptive fault model is then incorporated to the tolerant control configuration in which a Lyapunov based feedback law is designed to achieve uniformly asymptotic stable trajectory tracking control. Simulation experiments for trajectory tracking with typical step changing slipping faults show the efficiency of the proposed method.

  1. Multi-sensor Distributed Fault Detection Method Based on Subjective Bayesian Reasoning%基于主观贝叶斯推理的多传感器分布式故障检测融合方法

    Institute of Scientific and Technical Information of China (English)

    徐小力; 刘秀丽; 蒋章雷; 任彬

    2015-01-01

    针对复杂数控加工中心故障预测中各传感器检测信息呈现不确定性的问题,提出基于不确定性推理的多传感器分布式检测融合算法。该算法通过利用主观贝叶斯推理,获取局部检测装置的判决规则,并选取合适的局部判决规则送到融合规则中心,将来自不同传感器的观测数据进行综合分析,最后产生全局判决。以复杂立式加工中心为对象建立测试平台,利用多传感器样本获取方法进行机床不同运行状态及运行环境下的故障样本获取试验。试验表明在含有大量不确定性信息的故障诊断系统中,基于主观贝叶斯推理的分布式检测融合算法具有故障信息识别率高、诊断速度快的优点,其诊断错误率明显低于单个传感器的诊断错误率,且诊断错误率要低于串行分布式检测融算法。%Each sensor detects information presents problems under uncertainty in computerized numerical control(CNC)machining center fault prediction. Aiming at this problem, multi-sensor distributed fault detection method based on uncertainty reasoning is proposed. The algorithm by using subjective Bayesian reasoning, acquire the local detection device of decision rules, and select the local decision rules suitable to the fusion center, finally a global decision is produced. The complex vertical machining center as an example, the distributed multiple sensor fault detection platform is built. Fault sample is acquired using multi-sensor sample on different machine running state and running environment. Experiments show that in the fault diagnosis system contains a lot of information uncertainty, distributed detection fusion algorithm based on subjective Bayesian inference has the advantages of high recognition rate of fault information, diagnosis speed. Diagnosis error rate of multi-sensor distributed detection fusion algorithm is significantly lower than that of single sensor and the serial

  2. 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).

  3. Robust fault detection for discrete-time Markovian jump systems with mode-dependent time-delays

    Institute of Scientific and Technical Information of China (English)

    Hongru WANG; Changhong WANG; Shaoshuai MOU; Huijun GAO

    2007-01-01

    This paper investigates a fault detection problem for a class of discrete-time Markovian jump systems with norm-bounded uncertainties and mode-dependent time-delays. Attention is focused on constructing the residual generator based on the filter of which its parameters matrices are dependent on the system mode, that is, the fault detection filter is a Markovian jump system as well. The design of fault detection filter is reduced to H-infinity filtering problem by using H-infinity control theory, which can guarantee the difference between the residual and the fault (or, more generally weighted fault) as small as possible in the context of enhancing the robustness of residual to modeling errors, control inputs and unknown inputs. Sufficient condition for the existence of the above filters is established by means of linear matrix inequalities, which can be readily solved by using standard numerical software. A numerical example is given to illustrate the feasibility of the proposed method.

  4. Rolling bearing fault detection using an adaptive lifting multiwavelet packet with a {1\\frac{1}{2}} dimension spectrum

    Science.gov (United States)

    Jiang, Hongkai; Xia, Yong; Wang, Xiaodong

    2013-12-01

    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\\frac{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\\frac{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.

  5. Benchmarking an expert fault detection and diagnostic system on the Three Mile Island accident event sequence

    OpenAIRE

    Cilleirs, A.C.

    2013-01-01

    Early fault identification systems enable detecting and diagnosing early onset faults or fault causes which allow 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. We have shown (Cilliers and Mulder, 2012) that detecting faults early during transient operation in NPPs is possible when coupled with a reliable reference to compare...

  6. An approach for mechanical fault classification based on generalized discriminant analysis

    Institute of Scientific and Technical Information of China (English)

    LI Wei-hua; SHI Tie-lin; YANG Shu-zi

    2006-01-01

    To deal with pattern classification of complicated mechanical faults,an approach to multi-faults classification based on generalized discriminant analysis is presented.Compared with linear discriminant analysis (LDA),generalized discriminant analysis (GDA),one of nonlinear discriminant analysis methods,is more suitable for classifying the linear non-separable problem.The connection and difference between KPCA (Kernel Principal Component Analysis) and GDA is discussed.KPCA is good at detection of machine abnormality while GDA performs well in multi-faults classification based on the collection of historical faults symptoms.When the proposed method is applied to air compressor condition classification and gear fault classification,an excellent performance in complicated multi-faults classification is presented.

  7. Rotor broken bar fault diagnosis for induction motors based on double PQ transformation

    Institute of Scientific and Technical Information of China (English)

    2007-01-01

    A new rotor broken bar fault diagnosis method for induction motors based on the double PQ transformation is presented. By distinguishing the different patterns of the PQ components in the PQ plane, the rotor broken bar fault can be detected.The magnitude of power component directly resulted from rotor fault is used as the fault indicator and the distance between the point of no-load condition and the center of the ellipse as its normalization value. Based on these, the fault severity factor which is completely independent of the inertia and load level is defined. Moreover, a method to reliably discriminate between rotor faults and periodic load fluctuation is presented. Experimental results from a 4 kW induction motor demonstrated the validity of the proposed method.

  8. A novel data channel fault detection mechanism in optical burst switching networks

    Institute of Scientific and Technical Information of China (English)

    2007-01-01

    Fault detection in optical burst switching (OBS) networks will be a challenging task in the future. A novel mechanism based on probe burst (PB) and a new key concept is proposed to detect faults of OBS networks by sampling the health of data channels, which solve the difficulty of optical monitoring schemes while keeps the transparency of data network to Internet protocol (IP) packets. It takes full advantage of the characteristics of OBS, including architecture and signalling scheme, and introduces the excellent performances of single-hop-test used in electrical communication networks into OBS environment while avoids the shortcoming that any optical burst must undergo an optical-electric-optical (OEO) conversion.Well designed PB can provide exact criterion for judging whether protection/restoration should be excuted according to hard or soft fault identification.

  9. Faults

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — Through the study of faults and their effects, much can be learned about the size and recurrence intervals of earthquakes. Faults also teach us about crustal...

  10. Fault Diagnosis Strategies for SOFC-Based Power Generation Plants.

    Science.gov (United States)

    Costamagna, Paola; De Giorgi, Andrea; Gotelli, Alberto; Magistri, Loredana; Moser, Gabriele; Sciaccaluga, Emanuele; Trucco, Andrea

    2016-08-22

    The success of distributed power generation by plants based on solid oxide fuel cells (SOFCs) is hindered by reliability problems that can be mitigated through an effective fault detection and isolation (FDI) system. However, the numerous operating conditions under which such plants can operate and the random size of the possible faults make identifying damaged plant components starting from the physical variables measured in the plant very difficult. In this context, we assess two classical FDI strategies (model-based with fault signature matrix and data-driven with statistical classification) and the combination of them. For this assessment, a quantitative model of the SOFC-based plant, which is able to simulate regular and faulty conditions, is used. Moreover, a hybrid approach based on the random forest (RF) classification method is introduced to address the discrimination of regular and faulty situations due to its practical advantages. Working with a common dataset, the FDI performances obtained using the aforementioned strategies, with different sets of monitored variables, are observed and compared. We conclude that the hybrid FDI strategy, realized by combining a model-based scheme with a statistical classifier, outperforms the other strategies. In addition, the inclusion of two physical variables that should be measured inside the SOFCs can significantly improve the FDI performance, despite the actual difficulty in performing such measurements.

  11. Fault Diagnosis Strategies for SOFC-Based Power Generation Plants

    Directory of Open Access Journals (Sweden)

    Paola Costamagna

    2016-08-01

    Full Text Available The success of distributed power generation by plants based on solid oxide fuel cells (SOFCs is hindered by reliability problems that can be mitigated through an effective fault detection and isolation (FDI system. However, the numerous operating conditions under which such plants can operate and the random size of the possible faults make identifying damaged plant components starting from the physical variables measured in the plant very difficult. In this context, we assess two classical FDI strategies (model-based with fault signature matrix and data-driven with statistical classification and the combination of them. For this assessment, a quantitative model of the SOFC-based plant, which is able to simulate regular and faulty conditions, is used. Moreover, a hybrid approach based on the random forest (RF classification method is introduced to address the discrimination of regular and faulty situations due to its practical advantages. Working with a common dataset, the FDI performances obtained using the aforementioned strategies, with different sets of monitored variables, are observed and compared. We conclude that the hybrid FDI strategy, realized by combining a model-based scheme with a statistical classifier, outperforms the other strategies. In addition, the inclusion of two physical variables that should be measured inside the SOFCs can significantly improve the FDI performance, despite the actual difficulty in performing such measurements.

  12. Fault Diagnosis Strategies for SOFC-Based Power Generation Plants

    Science.gov (United States)

    Costamagna, Paola; De Giorgi, Andrea; Gotelli, Alberto; Magistri, Loredana; Moser, Gabriele; Sciaccaluga, Emanuele; Trucco, Andrea

    2016-01-01

    The success of distributed power generation by plants based on solid oxide fuel cells (SOFCs) is hindered by reliability problems that can be mitigated through an effective fault detection and isolation (FDI) system. However, the numerous operating conditions under which such plants can operate and the random size of the possible faults make identifying damaged plant components starting from the physical variables measured in the plant very difficult. In this context, we assess two classical FDI strategies (model-based with fault signature matrix and data-driven with statistical classification) and the combination of them. For this assessment, a quantitative model of the SOFC-based plant, which is able to simulate regular and faulty conditions, is used. Moreover, a hybrid approach based on the random forest (RF) classification method is introduced to address the discrimination of regular and faulty situations due to its practical advantages. Working with a common dataset, the FDI performances obtained using the aforementioned strategies, with different sets of monitored variables, are observed and compared. We conclude that the hybrid FDI strategy, realized by combining a model-based scheme with a statistical classifier, outperforms the other strategies. In addition, the inclusion of two physical variables that should be measured inside the SOFCs can significantly improve the FDI performance, despite the actual difficulty in performing such measurements. PMID:27556472

  13. 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.

  14. Research On a Marine Motor Fault Detection System Based on LabVIEW%基于LabVIEW的船用电机故障检测系统的研究

    Institute of Scientific and Technical Information of China (English)

    吴苏

    2014-01-01

    文章针对现在船用电机故障的主要问题,介绍了基于小波分析原理的消噪和特征提取方法,结合LabVIEW平台实现了硬件和软件资源的共享。其中,系统硬件由加速度传感器、信号调理电路、数据采集卡和PC机组成;系统软件利用LabVIEW平台编程实现数据采集、保存、小波除噪处理及小波变换,检测出故障特征信息。实验表明,本系统大大提高了故障检测的实时性与准确性。%Aiming at the main fault problem of marine motor, this thesis introduces the method of de-noise and feature extraction based on wavelet analysis, and combines with the LabVIEW platform to realize the sharing of hardware and software resources. The hardware system is composed of the acceleration sensors, signal condition circuit, DAQ (Data Acquisition) card, and PC. The software system is based on the LabVIEW to realize the data acquisition, saving, wavelet de-noise and wavelet transform, and detect the fault feature information. The experiment results show that this design greatly improves the real-time performance and the accuracy of the fault detection.

  15. 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

    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......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...

  16. A METHOD TO IMPROVE RELIABILITY OF GEARBOX FAULT DETECTION WITH ARTIFICIAL NEURAL NETWORKS

    Directory of Open Access Journals (Sweden)

    P.V. Srihari

    2010-12-01

    Full Text Available Fault diagnosis of gearboxes plays an important role in increasing the availability of machinery in condition monitoring. An effort has been made in this work to develop an artificial neural networks (ANN based fault detection system to increase reliability. Two prominent fault conditions in gears, worn-out and broken teeth, are simulated and five feature parameters are extracted based on vibration signals which are used as input features to the ANN based fault detection system developed in MATLAB, a three layered feed forward network using a back propagation algorithm. This ANN system has been trained with 30 sets of data and tested with 10 sets of data. The learning rate and number of hidden layer neurons are varied individually and the optimal training parameters are found based on the number of epochs. Among the five different learning rates used the 0.15 is deduced to be optimal one and at that learning rate the number of hidden layer neurons of 9 was the optimal one out of the three values considered. Then keeping the training parameters fixed, the number of hidden layers is varied by comparing the performance of the networks and results show the two and three hidden layers have the best detection accuracy.

  17. 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

  18. Study on Fault Diagnosis of Rolling Bearing Based on Time-Frequency Generalized Dimension

    Directory of Open Access Journals (Sweden)

    Yu Yuan

    2015-01-01

    Full Text Available The condition monitoring technology and fault diagnosis technology of mechanical equipment played an important role in the modern engineering. Rolling bearing is the most common component of mechanical equipment which sustains and transfers the load. Therefore, fault diagnosis of rolling bearings has great significance. Fractal theory provides an effective method to describe the complexity and irregularity of the vibration signals of rolling bearings. In this paper a novel multifractal fault diagnosis approach based on time-frequency domain signals was proposed. The method and numerical algorithm of Multi-fractal analysis in time-frequency domain were provided. According to grid type J and order parameter q in algorithm, the value range of J and the cut-off condition of q were optimized based on the effect on the dimension calculation. Simulation experiments demonstrated that the effective signal identification could be complete by multifractal method in time-frequency domain, which is related to the factors such as signal energy and distribution. And the further fault diagnosis experiments of bearings showed that the multifractal method in time-frequency domain can complete the fault diagnosis, such as the fault judgment and fault types. And the fault detection can be done in the early stage of fault. Therefore, the multifractal method in time-frequency domain used in fault diagnosis of bearing is a practicable method.

  19. STEM - software test and evaluation methods: fault detection using static analysis techniques

    International Nuclear Information System (INIS)

    STEM is a software reliability project with the objective of evaluating a number of fault detection and fault estimation methods which can be applied to high integrity software. This Report gives some interim results of applying both manual and computer-based static analysis techniques, in particular SPADE, to an early CERL version of the PODS software containing known faults. The main results of this study are that: The scope for thorough verification is determined by the quality of the design documentation; documentation defects become especially apparent when verification is attempted. For well-defined software, the thoroughness of SPADE-assisted verification for detecting a large class of faults was successfully demonstrated. For imprecisely-defined software (not recommended for high-integrity systems) the use of tools such as SPADE is difficult and inappropriate. Analysis and verification tools are helpful, through their reliability and thoroughness. However, they are designed to assist, not replace, a human in validating software. Manual inspection can still reveal errors (such as errors in specification and errors of transcription of systems constants) which current tools cannot detect. There is a need for tools to automatically detect typographical errors in system constants, for example by reporting outliers to patterns. To obtain the maximum benefit from advanced tools, they should be applied during software development (when verification problems can be detected and corrected) rather than retrospectively. (author)

  20. 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

    2015-01-01

    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. PMID:25642825

  1. 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.

  2. UiLog:Improving Log-Based Fault Diagnosis by Log Analysis

    Institute of Scientific and Technical Information of China (English)

    De-Qing Zou; Hao Qin; Hai Jin

    2016-01-01

    In modern computer systems, system event logs have always been the primary source for checking system status. As computer systems become more and more complex, the interaction between software and hardware increases frequently. The components will generate enormous log information, including running reports and fault information. The sheer quantity of data is a great challenge for analysis relying on the manual method. In this paper, we implement a management and analysis system of log information, which can assist system administrators to understand the real-time status of the entire system, classify logs into different fault types, and determine the root cause of the faults. In addition, we improve the existing fault correlation analysis method based on the results of system log classification. We apply the system in a cloud computing environment for evaluation. The results show that our system can classify fault logs automatically and effectively. With the proposed system, administrators can easily detect the root cause of faults.

  3. Active Fault Isolation in MIMO Systems

    DEFF Research Database (Denmark)

    Niemann, Hans Henrik; Poulsen, Niels Kjølstad

    2014-01-01

    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......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...

  4. 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.

  5. Bearing Fault Diagnosis Based on Laplace Wavelet Transform

    Directory of Open Access Journals (Sweden)

    Yingjie Yin

    2012-12-01

    Full Text Available The roller bearing characteristic frequencies contain very little energy, and are usually overwhelmed by noise and higher levels of structural vibrations. Therefore, envelope spectrum analysis is widely used to detection bearing localized fault. In order to overcome the shortcomings in the traditional envelope analysis in which manually specifying a resonant frequency band is required, a new approach based on the fusion of the Laplace wavelet transform and envelope spectrum is proposed for detection and diagnosis defects in roller element bearings. The basic principle is introduced in detail. Laplace wavelet transform is self-adaptive to non-stationary and non-linear signal. The methodology developed in this paper decomposes the original times series data in intrinsic oscillation modes, using the Laplace wavelet transform. Then the envelope spectrum is applied to the selected daughter wavelet that stands for the bearing faults. The experimental results show that Laplace wavelet can extract the impulse response from strong noise signals and can effectively diagnose the faults of bearing.

  6. Study on Fault Detection and PCB Picture-Location Method of Logic Circuit

    OpenAIRE

    Mingping Xia

    2013-01-01

    The main content of logic circuit fault detection includes describing circuit to be diagnosed, determining fault and circuit initial information, generating circuit location test set. In this study, LASAR is used to carry out the logic circuit simulation so as to create such documents as fault dictionary, node truth value table, etc. for the preparation of fault detection. Due to the limitation of circuit observability and testing vectors, the diagnosis program can not accurately locate the f...

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

    OpenAIRE

    Cilliers, Anthonie Christoffel; Mulder, Eben Johan

    2012-01-01

    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. Dete...

  8. Fault tolerant control of electric pitch control system based on single current detection%基于单电流检测的电动变桨系统容错控制

    Institute of Scientific and Technical Information of China (English)

    李宏伟; 付勃; 董海鹰; 杨立霞; 王睿敏

    2016-01-01

    针对电动变桨系统中常见的电流传感器故障,提出一种基于单电流检测的电动变桨系统变论域模糊容错控制方法。当变桨系统发生单个或两个电流传感器故障时,该方法利用直流母线电流传感器对所缺失的电流信息进行重构,保证三相电流能在任意两个相邻采样周期内得到及时更新,确保闭环系统稳定,并通过自适应阈值故障判断法完成故障相电流传感器的切换及容错。针对调制法引起的重构信号误差及电动变桨系统的主要控制目标,将变论域模糊控制方法应用于速度环,以改善系统抗负载扰动能力,提高容错系统鲁棒性。结果表明,该容错控制方法使得变桨系统在传感器故障情况下,牺牲部分系统性能后依然具有较理想的控制特性,并且该方法的正确性也得到了验证。%In view of the current sensors failure in electric pitch system,a variable universe fuzzy fault tolerant control method of electric pitch control system based on single current detection is proposed.When there is single or two-current sensor fault occurs,based on the proposed method the missing current information can be reconstructed by using direct current (DC)bus current sensor and the three-phase current can be updated in time within any two adjacent sampling periods,so as to ensure sta-bility of the closed-loop system.And then the switchover and fault tolerant control of fault current sensor would be accom-plished by fault diagnosis method based on adaptive threshold judgment.For the reconstructed signal error caused by the modu-lation method and the main control target of electric pitch system,a variable universe fuzzy control method is used in the speed loop,which can improve the anti-disturbance ability to load variation,and the robustness of fault tolerance system.The results show that the fault tolerant control method makes the variable pitch control system still has ideal

  9. Construction of customized redundant multiwavelet via increasing multiplicity for fault detection of rotating machinery

    Science.gov (United States)

    Chen, Jinglong; Zuo, Ming J.; Zi, Yanyang; He, Zhengjia

    2014-01-01

    Fault detection from the vibration measurement data of rotating machinery is significant for avoiding serious accidents. However, non-stationary vibration signal with a large amount of noise makes this task challenging. Multiwavelet not only owns the advantage on multi-resolution analysis but also can offer multiple wavelet basis functions. So it has the possibility of detecting various fault features preferably. However, the fixed basis functions which are not related to the given signal may lower the accuracy of fault detection. Moreover, another major intrinsic deficiency of multiwavelet lies in its critically sampled filter-bank, which causes shift-variance and is harmful to extract the feature of periodical impulses. To overcome these deficiencies, a new method called customized redundant multiwavelet (CRM) is constructed via increasing multiplicity (IM). IM is a simple method to design a series of changeable multiwavelet which are available for the subsequent optimization process. By the rule of the envelope spectrum entropy minimum principle, optimal multiwavelet is searched for. Based on the customized multiwavelet filters, the filters of CRM can be calculated by inserting zeros. The proposed method is applied to analyze the simulation, gearbox and rolling element bearing vibration signals. Compared with some other conventional methods, the results demonstrate that the proposed method possesses robust performance in detecting fault features of rotating machinery.

  10. Takagi Sugeno fuzzy expert model based soft fault diagnosis for two tank interacting system

    Directory of Open Access Journals (Sweden)

    Manikandan Pandiyan

    2014-09-01

    Full Text Available The inherent characteristics of fuzzy logic theory make it suitable for fault detection and diagnosis (FDI. Fault detection can benefit from nonlinear fuzzy modeling and fault diagnosis can profit from a transparent reasoning system, which can embed operator experience, but also learn from experimental and/or simulation data. Thus, fuzzy logic-based diagnostic is advantageous since it allows the incorporation of a-priori knowledge and lets the user understand the inference of the system. In this paper, the successful use of a fuzzy FDI based system, based on dynamic fuzzy models for fault detection and diagnosis of an industrial two tank system is presented. The plant data is used for the design and validation of the fuzzy FDI system. The validation results show the effectiveness of this approach.

  11. DETECTION OF FAULT LOCATION ON THE POWER LINES 6–35 kV WITH UNILATERAL FEED

    Directory of Open Access Journals (Sweden)

    F. A. Romaniuk

    2014-01-01

    Full Text Available The paper describes new algorithm of detecting the fault location for the power lines 6–35 kV with unilateral feed. The results of operational control of the short-circuit current for the faulted phases are the only initial data needed for the fault location algorithm. Analysis of the impact of the type of short circuit, transition resistance at the damaged point, errors of current transformers, load of the line, power and resistance of supply system, calculation errors of short-circuit currents at the beginning and at the end of the line on the performance of this algorithm is also performed. Estimated parameters of the algorithm of detecting the fault location based on identified influencing factors were established by method of computational experiment. Analysis of the simulation results performed shows that the variation of the relative error in the fault location determination for different types of faults is about the same. Moreover levels of these relative errors from the effects of all influencing factors can be less than just from one of them. This is due to the mutual compensation of the various factors’ influence on values of relative errors. This fact must be taken into consideration when performing the corresponding estimates for the worst case scenario.This paper presents the dynamic characteristics of this algorithm for detecting the fault location that allows estimating the time of detecting the fault location in different modes. Their analysis shows that there is almost no difference in quantitative and qualitative dependencies for different loads and types of faults. As the evaluation of results performed it should be noted that by means of the control only one parameter in short current mode, i.e. the short-circuit current, it is possibly with acceptable accuracy to detect the fault location. 

  12. 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.

  13. Fault detection and isolation of sensors in aeration control systems.

    Science.gov (United States)

    Carlsson, Bengt; Zambrano, Jesús

    2016-01-01

    In this paper, we consider the problem of fault detection (FD) and isolation in the aeration system of an activated sludge process. For this study, the dissolved oxygen in each aerated zone is assumed to be controlled automatically. As the basis for an FD method we use the ratio of air flow rates into different zones. The method is evaluated in two scenarios: using the Benchmark Simulation Model no. 1 (BSM1) by Monte Carlo simulations and using data from a wastewater treatment plant. The FD method shows good results for a correct and early FD and isolation.

  14. Distributed Fault-Tolerant Event Region Detection of Wireless Sensor Networks

    OpenAIRE

    Dyi-Rong Duh; Ssu-Pei Li; Victor W. Cheng

    2013-01-01

    This work provides a distributed fault-tolerant event region detection algorithm for wireless sensor networks. The proposed algorithm can identify faulty and fault-free sensors and ignore the abnormal readings to avoid false alarm. Moreover, every event region can also be detected and identified. Simulation results show that fault detection accuracy (FDA) is greater than 92%, false alarm rate (FAR) is near 0%, and event detection accuracy (EDA) is greater than 99% under uniform distribution. ...

  15. Robust Nonlinear Analytic Redundancy for Fault Detection and Isolation in Mobile Robot

    Institute of Scientific and Technical Information of China (English)

    Bibhrajit Halder; Nilanjan Sarkar

    2007-01-01

    A robust nonlinear analytical redundancy (RNLAR) technique is presented to detect and isolate actuator and sensor faults in a mobile robot. Both model-plant-mismatch (MPM) and process disturbance are considered during fault detection. The RNLAR is used to design primary residual vectors (PRV), which are highly sensitive to the faults and less sensitive to MPM and process disturbance, for sensor and actuator fault detection. The PRVs are then transformed into a set of structured residual vectors (SRV) for fault isolation. Experimental results on a Pioneer 3-DX mobile robot are presented to justify the effectiveness of the RNLAR scheme.

  16. A new and accurate fault location algorithm for combined transmission lines using Adaptive Network-Based Fuzzy Inference System

    Energy Technology Data Exchange (ETDEWEB)

    Sadeh, Javad; Afradi, Hamid [Electrical Engineering Department, Faculty of Engineering, Ferdowsi University of Mashhad, P.O. Box: 91775-1111, Mashhad (Iran)

    2009-11-15

    This paper presents a new and accurate algorithm for locating faults in a combined overhead transmission line with underground power cable using Adaptive Network-Based Fuzzy Inference System (ANFIS). The proposed method uses 10 ANFIS networks and consists of 3 stages, including fault type classification, faulty section detection and exact fault location. In the first part, an ANFIS is used to determine the fault type, applying four inputs, i.e., fundamental component of three phase currents and zero sequence current. Another ANFIS network is used to detect the faulty section, whether the fault is on the overhead line or on the underground cable. Other eight ANFIS networks are utilized to pinpoint the faults (two for each fault type). Four inputs, i.e., the dc component of the current, fundamental frequency of the voltage and current and the angle between them, are used to train the neuro-fuzzy inference systems in order to accurately locate the faults on each part of the combined line. The proposed method is evaluated under different fault conditions such as different fault locations, different fault inception angles and different fault resistances. Simulation results confirm that the proposed method can be used as an efficient means for accurate fault location on the combined transmission lines. (author)

  17. Research on Fault Diagnostic System in CVT Based on UDS

    Directory of Open Access Journals (Sweden)

    Jiande Wang

    2015-01-01

    Full Text Available A communication model of diagnostic network and implementation of unified diagnostic services (UDS based on controller area network (CAN bus are presented in this paper, and fault diagnostic function of transmission control unit (TCU, USB- (universal serial bus- CAN hardware and software modules, and fault diagnostic software based on personal computer (PC are designed. Model diagnostic method is applied on ratio control, and fault diagnostic system is tested in vehicle.

  18. Dynamic Output Feedback Based Active Decentralized Fault-Tolerant Control for Reconfigurable Manipulator with Concurrent Failures

    Directory of Open Access Journals (Sweden)

    Yuanchun Li

    2015-01-01

    Full Text Available The goal of this paper is to describe an active decentralized fault-tolerant control (ADFTC strategy based on dynamic output feedback for reconfigurable manipulators with concurrent actuator and sensor failures. Consider each joint module of the reconfigurable manipulator as a subsystem, and treat the fault as the unknown input of the subsystem. Firstly, by virtue of linear matrix inequality (LMI technique, the decentralized proportional-integral observer (DPIO is designed to estimate and compensate the sensor fault online; hereafter, the compensated system model could be derived. Then, the actuator fault is estimated similarly by another DPIO using LMI as well, and the sufficient condition of the existence of H∞ fault-tolerant controller in the dynamic output feedback is presented for the compensated system model. Furthermore, the dynamic output feedback controller is presented based on the estimation of actuator fault to realize active fault-tolerant control. Finally, two 3-DOF reconfigurable manipulators with different configurations are employed to verify the effectiveness of the proposed scheme in simulation. The main advantages of the proposed scheme lie in that it can handle the concurrent faults act on the actuator and sensor on the same joint module, as well as there is no requirement of fault detection and isolation process; moreover, it is more feasible to the modularity of the reconfigurable manipulator.

  19. Model-based fault diagnosis in PEM fuel cell systems

    Energy Technology Data Exchange (ETDEWEB)

    Escobet, T.; de Lira, S.; Puig, V.; Quevedo, J. [Automatic Control Department (ESAII), Universitat Politecnica de Catalunya (UPC), Rambla Sant Nebridi 10, 08222 Terrassa (Spain); Feroldi, D.; Riera, J.; Serra, M. [Institut de Robotica i Informatica Industrial (IRI), Consejo Superior de Investigaciones Cientificas (CSIC), Universitat Politecnica de Catalunya (UPC) Parc Tecnologic de Barcelona, Edifici U, Carrer Llorens i Artigas, 4-6, Planta 2, 08028 Barcelona (Spain)

    2009-07-01

    In this work, a model-based fault diagnosis methodology for PEM fuel cell systems is presented. The methodology is based on computing residuals, indicators that are obtained comparing measured inputs and outputs with analytical relationships, which are obtained by system modelling. The innovation of this methodology is based on the characterization of the relative residual fault sensitivity. To illustrate the results, a non-linear fuel cell simulator proposed in the literature is used, with modifications, to include a set of fault scenarios proposed in this work. Finally, it is presented the diagnosis results corresponding to these fault scenarios. It is remarkable that with this methodology it is possible to diagnose and isolate all the faults in the proposed set in contrast with other well known methodologies which use the binary signature matrix of analytical residuals and faults. (author)

  20. Reconstruction based approach to sensor fault diagnosis using auto-associative neural networks

    Institute of Scientific and Technical Information of China (English)

    Mousavi Hamidreza; Shahbazian Mehdi; Jazayeri-Rad Hooshang; Nekounam Aliakbar

    2014-01-01

    Fault diagnostics is an important research area including different techniques. Principal component analysis (PCA) is a linear technique which has been widely used. For nonlinear processes, however, the nonlinear principal component analysis (NLPCA) should be applied. In this work, NLPCA based on auto-associative neural network (AANN) was applied to model a chemical process using historical data. First, the residuals generated by the AANN were used for fault detection and then a reconstruction based approach called enhanced AANN (E-AANN) was presented to isolate and reconstruct the faulty sensor simultaneously. The proposed method was implemented on a continuous stirred tank heater (CSTH) and used to detect and isolate two types of faults (drift and offset) for a sensor. The results show that the proposed method can detect, isolate and reconstruct the occurred fault properly.

  1. 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.

  2. An adaptive confidence limit for periodic non-steady conditions fault detection

    Science.gov (United States)

    Wang, Tianzhen; Wu, Hao; Ni, Mengqi; Zhang, Milu; Dong, Jingjing; Benbouzid, Mohamed El Hachemi; Hu, Xiong

    2016-05-01

    System monitoring has become a major concern in batch process due to the fact that failure rate in non-steady conditions is much higher than in steady ones. A series of approaches based on PCA have already solved problems such as data dimensionality reduction, multivariable decorrelation, and processing non-changing signal. However, if the data follows non-Gaussian distribution or the variables contain some signal changes, the above approaches are not applicable. To deal with these concerns and to enhance performance in multiperiod data processing, this paper proposes a fault detection method using adaptive confidence limit (ACL) in periodic non-steady conditions. The proposed ACL method achieves four main enhancements: Longitudinal-Standardization could convert non-Gaussian sampling data to Gaussian ones; the multiperiod PCA algorithm could reduce dimensionality, remove correlation, and improve the monitoring accuracy; the adaptive confidence limit could detect faults under non-steady conditions; the fault sections determination procedure could select the appropriate parameter of the adaptive confidence limit. The achieved result analysis clearly shows that the proposed ACL method is superior to other fault detection approaches under periodic non-steady conditions.

  3. Concurrent Detection and Classification of Faults in Matrix Converter using Trans-Conductance

    Directory of Open Access Journals (Sweden)

    Sarah Azimi

    2014-07-01

    Full Text Available This paper presents a fault diagnostic algorithm for detecting and locating open-circuit and short-circiut faults in switching components of matrix converters (MCs which can be effectively used to drive a permanent magnet synchronous motor for research in critical applications. The proposed method is based on monitoring the voltages and currents of the switches. These measurements are used to evaluate the forward trans-conductance of each transistor for different values of switch voltages. These trans-conductance values are then compared to the nominal values. Under healthy conditions, the values obtained for the fault signal is less than the tolerable value. Under the open/short-circuit conditions, the fault signal exceeds the threshold, hence enables the matrix converter drive to detect and exactly identify the location of the faulty IGBT. The main advantages of this diagnostic method include fast detection and locating of the faulty IGBT, easiness of implementation and independency of the modulation strategy of the converter.

  4. Short-time matrix series based singular value decomposition for rolling bearing fault diagnosis

    Science.gov (United States)

    Cong, Feiyun; Chen, Jin; Dong, Guangming; Zhao, Fagang

    2013-01-01

    Rolling element bearing faults are among the main causes of rotating machines breakdown. It is important to distinguish the incipient fault before the bearings step into serious failure. Based on the traditional singular value decomposition (SVD) theory, short-time matrix series (STMS) and singular value ratio (SVR) are introduced to the vibration signal processing. The proposed signal processing method is called S-SVDR (STMS based SVD method using SVR) and it has been proved to have a good local identification capability in the rolling bearing fault diagnosis. The detailed description of applying S-SVDR methods to rolling bearing fault diagnosis is given through the artificial fault signal processing in experiment 1. In experiment 2, rolling element bearing accelerated life test is performed in Hangzhou Bearing Test & Research Center (HBRC). The experimental result shows that the incipient fault can be well detected through S-SVDR processing method. However, the envelope analysis of original signal cannot detect the fault due to the existence of signal interference. A conclusion can be made that the proposed S-SVDR method has a good effect on de-noising and eliminating the signal interference of rolling bearing for the fault diagnosis.

  5. Detecting Faults in Southern California using Computer-Vision Techniques and Uninhabited Aerial Vehicle Synthetic Aperture Radar (UAVSAR) Interferometry

    Science.gov (United States)

    Barba, M.; Rains, C.; von Dassow, W.; Parker, J. W.; Glasscoe, M. T.

    2013-12-01

    Knowing the location and behavior of active faults is essential for earthquake hazard assessment and disaster response. In Interferometric Synthetic Aperture Radar (InSAR) images, faults are revealed as linear discontinuities. Currently, interferograms are manually inspected to locate faults. During the summer of 2013, the NASA-JPL DEVELOP California Disasters team contributed to the development of a method to expedite fault detection in California using remote-sensing technology. The team utilized InSAR images created from polarimetric L-band data from NASA's Uninhabited Aerial Vehicle Synthetic Aperture Radar (UAVSAR) project. A computer-vision technique known as 'edge-detection' was used to automate the fault-identification process. We tested and refined an edge-detection algorithm under development through NASA's Earthquake Data Enhanced Cyber-Infrastructure for Disaster Evaluation and Response (E-DECIDER) project. To optimize the algorithm we used both UAVSAR interferograms and synthetic interferograms generated through Disloc, a web-based modeling program available through NASA's QuakeSim project. The edge-detection algorithm detected seismic, aseismic, and co-seismic slip along faults that were identified and compared with databases of known fault systems. Our optimization process was the first step toward integration of the edge-detection code into E-DECIDER to provide decision support for earthquake preparation and disaster management. E-DECIDER partners that will use the edge-detection code include the California Earthquake Clearinghouse and the US Department of Homeland Security through delivery of products using the Unified Incident Command and Decision Support (UICDS) service. Through these partnerships, researchers, earthquake disaster response teams, and policy-makers will be able to use this new methodology to examine the details of ground and fault motions for moderate to large earthquakes. Following an earthquake, the newly discovered faults can

  6. Analog circuit test stimuli optimal method based on fault detectable analysis and genetic algorithm%基于故障可诊性与遗传算法的模拟电路测试激励优化方法

    Institute of Scientific and Technical Information of China (English)

    姜媛媛; 王友仁; 崔江; 罗慧; 赵鹏

    2011-01-01

    The selection of test stimuli has an influence on circuit fault diagnosis precision.To overcome the disadvantage of prior methods, this paper proposed a new test stimuli optimal design method based on fault detectable analysis and genetic algorithm.First, executing 10 octave AC Sweep analysis on the circuit under test (CUT) so that the frequency range of sine wave could be set, and then calculated the inner and inter class distance and sensitivity factor value based on the discrete frequency-rms voltage response data which acquired under different fault type circuit simulation, finally finished the test stimuli optimal design through the genetic algorithm with sensitivity factor as its objective function.Used a double-bandpass filter circuit to validate the proposed method.The experimental results show that the optimal test stimuli can effectively reduce the ambiguity of different fault feature distribution and reach a satisfied diagnosis result.%测试激励的选择影响电路故障诊断的精度,为克服现有方法不足,提出了一种基于电路可诊性和遗传算法的模拟电路测试激励优化方法.对待测电路进行十倍频程AC Sweep分析来确定正弦测试信号的频率优化范围,基于电路幅频响应中离散频点相应的电压有效值来计算各故障特征数据类内类间离散度,统计多频测试信号敏感度因子大小,并以此为目标函数利用遗传算法实现测试信号中的频率参数优化.以双带通滤波电路为例进行了仿真验证,实验结果表明该方法优化出的测试激励信号能有效降低电路故障响应特征分布模糊性,提高了故障诊断率.

  7. Test results judgment method based on BIT faults

    Institute of Scientific and Technical Information of China (English)

    Wang Gang; Qiu Jing; Liu Guanjun; Lyu Kehong

    2015-01-01

    Built-in-test (BIT) is responsible for equipment fault detection, so the test data correct-ness directly influences diagnosis results. Equipment suffers all kinds of environment stresses, such as temperature, vibration, and electromagnetic stress. As embedded testing facility, BIT also suffers from these stresses and the interferences/faults are caused, so that the test course is influenced, resulting in incredible results. Therefore it is necessary to monitor test data and judge test failures. Stress monitor and BIT self-diagnosis would redound to BIT reliability, but the existing anti-jamming researches are mainly safeguard design and signal process. This paper focuses on test results monitor and BIT equipment (BITE) failure judge, and a series of improved approaches is proposed. Firstly the stress influences on components are illustrated and the effects on the diagnosis results are summarized. Secondly a composite BIT program is proposed with information integra-tion, and a stress monitor program is given. Thirdly, based on the detailed analysis of system faults and forms of BIT results, the test sequence control method is proposed. It assists BITE failure judge and reduces error probability. Finally the validation cases prove that these approaches enhance credibility.

  8. 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.

  9. Fault Detection and Isolation for a Supermarket Refrigeration System - Part One

    DEFF Research Database (Denmark)

    Yang, Zhenyu; Rasmussen, Karsten B.; Kieu, Anh T.;

    2011-01-01

    Fault Detection and Isolation (FDI) using the Kalman Filter (KF) technique for a supermarket refrigeration system is explored. Four types of sensor fault scenarios, namely drift, offset, freeze and hard-over, are considered for two temperature sensors, and one type of parametric fault scenario...

  10. 基于MUSIC与SAA的笼型异步电动机转子断条故障检测%A MUSIC-SAA-Based Detection Method For Broken Rotor Bar Fault in Induction Motors

    Institute of Scientific and Technical Information of China (English)

    孙丽玲; 许伯强; 李志远

    2012-01-01

    This paper presents a detection method for rotor fault in induction motors,which is based on multiple signal classification(MUSIC) and simulated annealing algorithm(SAA).Firstly,the performance of MUSIC is tested with the simulated stator current signal of an induction motor with broken rotor bar fault.The results show that MUSIC is capable of identifying clearly the frequencies of the broken rotor bar fault feature components and the others in the simulated signal even with short-time sample,although it can not handle with the amplitudes and initial phases of those components.Secondly,SAA is introduced to determine the amplitudes and initial phases of the frequency components in the simulated signal and the results are really satisfactory.Thus paves the way to detect broken rotor bar fault in induction motors by combining MUSIC and SAA.Finally,the related experiment on a 3kW,Y100L—2-typed induction motor is conducted,and the results demonstrate that the MUSIC-SAA-based method to detect broken rotor bar fault in induction motors is effective even with short-time sample,as makes it a promising choice for induction motors operating with fluctuant load or under severe interference.%提出了一种基于多重信号分类(Multiple Signal Classification,MUSIC)与模拟退火算法(Simulated Annealing Algorithm,SAA)的异步电动机转子断条故障检测新方法。首先以转子断条故障仿真信号检验MUSIC性能,结果表明:MUSIC对于短时信号具备高频率分辨力,可以准确计算转子断条故障特征分量以及其他分量之频率;但对诸频率分量幅值、初相角,MUSIC无能为力。为此,引入SAA确定诸频率分量幅值、初相角,效果理想。进而,对一台Y100L—2型3kW笼型异步电动机完成了转子断条故障检测实验。实验结果表明:基于MUSIC与SAA的异步电动机转子断条故障检测方法是切实可行的,并且因仅需处理短时信号而适用于负荷波动、噪声等干扰严重情况。

  11. A BRB Based Fault Prediction Method of Complex Electromechanical Systems

    Directory of Open Access Journals (Sweden)

    Bangcheng Zhang

    2015-01-01

    Full Text Available Fault prediction is an effective and important approach to improve the reliability and reduce the risk of accidents for complex electromechanical systems. In order to use the quantitative information and qualitative knowledge efficiently to predict the fault, a new model is proposed on the basis of belief rule base (BRB. Moreover, an evidential reasoning (ER based optimal algorithm is developed to train the fault prediction model. The screw failure in computer numerical control (CNC milling machine servo system is taken as an example and the fault prediction results show that the proposed method can predict the behavior of the system accurately with combining qualitative knowledge and some quantitative information.

  12. 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

  13. Improving Fault Detection in Modified Code——A Study from the Telecommunication Industry

    Institute of Scientific and Technical Information of China (English)

    Piotr Tomaszewski; Lars Lundberg; H(a)kan Grahn

    2007-01-01

    Many software systems are developed in a number of consecutive releases. In each release not only new codeis added but also existing code is often modified. In this study we show that the modified code can be an important sourceof faults. Faults are widely recognized as one of the major cost drivers in software projects. Therefore, we look for methodsthat improve the fault detection in the modified code. We propose and evaluate a number of prediction models that increasethe efficiency of fault detection. To build and evaluate our models we use data collected from two large telecommunicationsystems produced by Ericsson. We evaluate the performance of our models by applying them both to a different release ofthe system than the one they are built on and to a different system. The performance of our models is compared to theperformance of the theoretical best model, a simple model based on size, as well as to analyzing the code in a random order(not using any model). We find that the use of our models provides a significant improvement over not using any model atall and over using a simple model based on the class size. The gain offered by our models corresponds to 38~57% of thetheoretical maximum gain.

  14. Minimum System Sensitivity Study of Linear Discrete Time Systems for Fault Detection

    Directory of Open Access Journals (Sweden)

    Xiaobo Li

    2013-01-01

    Full Text Available Fault detection is a critical step in the fault diagnosis of modern complex systems. An important notion in fault detection is the smallest gain of system sensitivity, denoted as ℋ− index, which measures the worst fault sensitivity. This paper is concerned with characterizing ℋ− index for linear discrete time systems. First, a necessary and sufficient condition on the lower bound of ℋ− index in finite time horizon for linear discrete time-varying systems is developed. It is characterized in terms of the existence of solution to a backward difference Riccati equation with an inequality constraint. The result is further extended to systems with unknown initial condition based on a modified ℋ− index. In addition, for linear time-invariant systems in infinite time horizon, based on the definition of the ℋ− index in frequency domain, a condition in terms of algebraic Riccati equation is developed. In comparison with the well-known bounded real lemma, it is found that ℋ− index is not completely dual to ℋ∞ norm. Finally, several numerical examples are given to illustrate the main results.

  15. Smac–Fdi: A Single Model Active Fault Detection and Isolation System for Unmanned Aircraft

    Directory of Open Access Journals (Sweden)

    Ducard Guillaume J.J.

    2015-03-01

    Full Text Available This article presents a single model active fault detection and isolation system (SMAC-FDI which is designed to efficiently detect and isolate a faulty actuator in a system, such as a small (unmanned aircraft. This FDI system is based on a single and simple aerodynamic model of an aircraft in order to generate some residuals, as soon as an actuator fault occurs. These residuals are used to trigger an active strategy based on artificial exciting signals that searches within the residuals for the signature of an actuator fault. Fault isolation is carried out through an innovative mechanism that does not use the previous residuals but the actuator control signals directly. In addition, the paper presents a complete parameter-tuning strategy for this FDI system. The novel concepts are backed-up by simulations of a small unmanned aircraft experiencing successive actuator failures. The robustness of the SMAC-FDI method is tested in the presence of model uncertainties, realistic sensor noise and wind gusts. Finally, the paper concludes with a discussion on the computational efficiency of the method and its ability to run on small microcontrollers.

  16. 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)

  17. On Real-Time Fault Detection in Wind Turbines: Sensor Selection Algorithm and Detection Time Reduction Analysis

    Directory of Open Access Journals (Sweden)

    Francesc Pozo

    2016-07-01

    Full Text Available In this paper, we address the problem of real-time fault detection in wind turbines. Starting from a data-driven fault detection method, the contribution of this paper is twofold. First, a sensor selection algorithm is proposed with the goal to reduce the computational effort of the fault detection method. Second, an analysis is performed to reduce the data acquisition time needed by the fault detection method, that is, with the goal of reducing the fault detection time. The proposed methods are tested in a benchmark wind turbine where different actuator and sensor failures are simulated. The results demonstrate the performance and effectiveness of the proposed algorithms that dramatically reduce the number of sensors and the fault detection time.

  18. Sensor and Actuator Fault Detection and Isolation in Nonlinear System using Multi Model Adaptive Linear Kalman Filter

    Directory of Open Access Journals (Sweden)

    M. Manimozhi

    2014-05-01

    Full Text Available Fault Detection and Isolation (FDI using Linear Kalman Filter (LKF is not sufficient for effective monitoring of nonlinear processes. Most of the chemical plants are nonlinear in nature while operating the plant in a wide range of process variables. In this study we present an approach for designing of Multi Model Adaptive Linear Kalman Filter (MMALKF for Fault Detection and Isolation (FDI of a nonlinear system. The uses a bank of adaptive Kalman filter, with each model based on different fault hypothesis. In this study the effectiveness of the MMALKF has been demonstrated on a spherical tank system. The proposed method is detecting and isolating the sensor and actuator soft faults which occur sequentially or simultaneously.

  19. Detection of Crosstalk Faults in Field Programmable Gate Arrays (FPGA)

    Science.gov (United States)

    Das, N.; Roy, P.; Rahaman, H.

    2015-09-01

    In this work, a Built-in-Self-Test (BIST) technique has been proposed to detect crosstalk faults in FPGA and run time congestion and to provide the crosstalk aware router for FPGA. The proposed BIST circuits require less overhead as compared to earlier techniques. The proposed detector can detect any logic hazard or delay due to crosstalk. A technique has also been proposed to avoid the crosstalk by routing the path in such a way that no interference occurs between the interconnects. The proposed router has achieved better utilization of routing resource to determine the net as compared to the earlier works. The proposed scheme is simulated in MATLAB and verified using Xilinx ISE tools and Modelsim 6.0. The router is implemented by using class provided by JBits for Xilinx, Vertex-II FPGA. It has been found that the results are quite encouraging.

  20. Development and evaluation of virtual refrigerant mass flow sensors for fault detection and diagnostics

    Energy Technology Data Exchange (ETDEWEB)

    Kim, Woohyun; Braun, J.

    2016-03-05

    Refrigerant mass flow rate is an important measurement for monitoring equipment performance and enabling fault detection and diagnostics. However, a traditional mass flow meter is expensive to purchase and install. A virtual refrigerant mass flow sensor (VRMF) uses a mathematical model to estimate flow rate using low-cost measurements and can potentially be implemented at low cost. This study evaluates three VRMFs for estimating refrigerant mass flow rate. The first model uses a compressor map that relates refrigerant flow rate to measurements of inlet and outlet pressure, and inlet temperature measurements. The second model uses an energy-balance method on the compressor that uses a compressor map for power consumption, which is relatively independent of compressor faults that influence mass flow rate. The third model is developed using an empirical correlation for an electronic expansion valve (EEV) based on an orifice equation. The three VRMFs are shown to work well in estimating refrigerant mass flow rate for various systems under fault-free conditions with less than 5% RMS error. Each of the three mass flow rate estimates can be utilized to diagnose and track the following faults: 1) loss of compressor performance, 2) fouled condenser or evaporator filter, 3) faulty expansion device, respectively. For example, a compressor refrigerant flow map model only provides an accurate estimation when the compressor operates normally. When a compressor is not delivering the expected flow due to a leaky suction or discharge valve or other internal fault, the energy-balance or EEV model can provide accurate flow estimates. In this paper, the flow differences provide an indication of loss of compressor performance and can be used for fault detection and diagnostics.

  1. RESEARCH ON EXPERT SYSTEM OF FAULT DETECTION AND DIAGNOSING FOR PNEUMATIC SYSTEM OF AUTOMATIC PRODUCTION LINE

    Institute of Scientific and Technical Information of China (English)

    2002-01-01

    Fault detection and diagnosis for pneumatic system of automatic production line are studied. An expert system using fuzzy-neural network and pneumatic circuit fault diagnosis instrument are designed. The mathematical model of various pneumatic faults and experimental device are built. In the end, some experiments are done, which shows that the expert system using fuzzy-neural network can diagnose fast and truly fault of pneumatic circuit.

  2. A Delay System Approach to Fault Detection Filter of Networked Control Systems

    Institute of Scientific and Technical Information of China (English)

    MA Li-wei; TIAN Zuo-hua; SHI Song-jiao; WENG Zheng-xin

    2009-01-01

    In this paper, the fault detection filter (FDF) design problem for networked control systems (NCSs) with both network-induced delay and data dropout is studied. Based on a new NCSs model proposed recently, an observer-based filter is introduced to be the residual generator and formulated as an H∞-optimization problem for systems with two successive delay components. By applying Lyapunov-Krasovskii approach, a new sufficient condition on stability and H∞ performance is derived for systems with two successive delay components in the state. A solution of the optimization problem is then presented in terms of linear matrix inequality (LMI) formulation, dependently of time delay. In order to detect the fault, the residual evaluation problem is also considered. An illustrative design example is employed to demonstrate the validity of the proposed approach.

  3. An adaptive SK technique and its application for fault detection of rolling element bearings

    Science.gov (United States)

    Wang, Yanxue; Liang, Ming

    2011-07-01

    In this paper, we propose an adaptive spectral kurtosis (SK) technique for the fault detection of rolling element bearings. The primary contribution is adaptive determination of the bandwidth and center frequency. This is implemented with successive attempts to right-expand a given window along the frequency axis by merging it with its subsequent neighboring windows. Influence of the parameters such as the initial window function, bandwidth and window overlap on the merged windows as well as how to choose those parameters in practical applications are explored. Based on simulated experiments, it can be found that the proposed technique can further enhance the SK-based method as compared to the kurtogram approach. The effectiveness of the proposed method in fault detection of the rolling element bearings is validated using experimental signals.

  4. 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.

  5. Gearbox Fault Detection through PSO Exact Wavelet Analysis and SVM Classifier

    OpenAIRE

    Zamanian, Amir Hosein; Ohadi, Abdolreza

    2016-01-01

    Time-frequency methods for vibration-based gearbox faults detection have been considered the most efficient method. Among these methods, continuous wavelet transform (CWT) as one of the best time-frequency method has been used for both stationary and transitory signals. Some deficiencies of CWT are problem of overlapping and distortion ofsignals. In this condition, a large amount of redundant information exists so that it may cause false alarm or misinterpretation of the operator. In this pap...

  6. Research on Transformer Fault Based on Probabilistic Neural Network

    Directory of Open Access Journals (Sweden)

    Li Yingshun

    2015-01-01

    Full Text Available With the development of computer science and technology, and increasingly intelligent industrial production, the application of big data in industry also advances rapidly, and the development of artificial intelligence in the aspect of fault diagnosis is particularly prominent. On the basis of MATLAB platform, this paper constructs a fault diagnosis expert system of artificial intelligence machine based on the probabilistic neural network, and it also carries out a simulation of production process by the use of bionic algorithm. This paper makes a diagnosis of transformer fault by the use of an expert system developed by this paper, and verifies that the probabilistic neural network has a good convergence, fault-tolerant ability and big data handling capability in the fault diagnosis. It is suitable for industrial production, which can provide a reliable mathematical model for the construction of fault diagnosis expert system in the industrial production.

  7. A Carrier Signal Approach for Intermittent Fault Detection and Health Monitoring for Electronics Interconnections System

    Directory of Open Access Journals (Sweden)

    Syed Wakil Ahmad

    2015-12-01

    Full Text Available Intermittent faults are completely missed out by traditional monitoring and detection techniques due to non-stationary nature of signals. These are the incipient events of a precursor of permanent faults to come. Intermittent faults in electrical interconnection are short duration transients which could be detected by some specific techniques but these do not provide enough information to understand the root cause of it. Due to random and non-predictable nature, the intermittent faults are the most frustrating, elusive, and expensive faults to detect in interconnection system. The novel approach of the author injects a fixed frequency sinusoidal signal into electronics interconnection system that modulates intermittent fault if persist. Intermittent faults and other channel effects are computed from received signal by demodulation and spectrum analysis. This paper describes technology for intermittent fault detection, and classification of intermittent fault, and channel characterization. The paper also reports the functionally tests of computational system of the proposed methods. This algorithm has been tested using experimental setup. It generate an intermittent signal by external vibration stress on connector and intermittency is detected by acquiring and processing propagating signal. The results demonstrate to detect and classify intermittent interconnection and noise variations due to intermittency. Monitoring the channel in-situ with low amplitude, and narrow band signal over electronics interconnection between a transmitter and a receiver provides the most effective tool for continuously watching the wire system for the random, unpredictable intermittent faults, the precursor of failure.

  8. Remote Fault Information Acquisition and Diagnosis System of the Combine Harvester Based on LabVIEW

    Science.gov (United States)

    Chen, Jin; Wu, Pei; Xu, Kai

    Most combine harvesters have not be equipped with online fault diagnosis system. A fault information acquisition and diagnosis system of the Combine Harvester based on LabVIEW is designed, researched and developed. Using ARM development board, by collecting many sensors' signals, this system can achieve real-time measurement, collection, displaying and analysis of different parts of combine harvesters. It can also realize detection online of forward velocity, roller speed, engine temperature, etc. Meanwhile the system can judge the fault location. A new database function is added so that we can search the remedial measures to solve the faults and also we can add new faults to the database. So it is easy to take precautions against before the combine harvester breaking down then take measures to service the harvester.

  9. 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...

  10. SIMD-Swift: Improving Performance of Swift Fault Detection

    OpenAIRE

    Oleksenko, Oleksii

    2016-01-01

    The general tendency in modern hardware is an increase in fault rates, which is caused by the decreased operation voltages and feature sizes. Previously, the issue of hardware faults was mainly approached only in high-availability enterprise servers and in safety-critical applications, such as transport or aerospace domains. These fields generally have very tight requirements, but also higher budgets. However, as fault rates are increasing, fault tolerance solutions are starting to be also re...

  11. Fault detection and diagnosis of the deaerator level control system in nuclear power plants

    Energy Technology Data Exchange (ETDEWEB)

    Kim, Kyung Youn; Lee, Yoon Joon [Cheju National Univ., Cheju (Korea, Republic of)

    2004-02-01

    The deaerator of a power plant is one of feedwater heaters in the secondary system, and it is located above the feedwater pumps. The feedwater pumps take the water from the deaerator storage tank, and the Net Positive Suction Head(NPSH) should always be ensured. To secure the sufficient NPSH, the deaerator tank is equipped with the level control system of which level sensors are critical items. And it is necessary to ascertain the sensor state on-line. For this, a model-based Fault Detection and Diagnosis(FDD) is introduced in this study. The dynamic control model is formulated from the relation of input-output flow rates and liquid-level of the deaerator storage tank. Then an adaptive state estimator is designed for the fault detection and diagnosis of sensors. The performance and effectiveness of the proposed FDD scheme are evaluated by applying the operation data of Yonggwang Units 3 and 4.

  12. 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.

  13. Active Fault Diagnosis for Hybrid Systems Based on Sensitivity Analysis and EKF

    DEFF Research Database (Denmark)

    Gholami, Mehdi; Schiøler, Henrik; Bak, Thomas

    2011-01-01

    An active fault diagnosis approach for different kinds of faults is proposed. The input of the approach is designed off-line based on sensitivity analysis such that the maximum sensitivity for each individual system parameter is obtained. Using maximum sensitivity, results in a better precision...... in the estimation of the corresponding parameter. The fault detection and isolation is done by comparing the nominal parameters with those estimated by Extended Kalman Filter (EKF). In study, Gaussian noise is used as the input disturbance as well as the measurement noise for simulation. The method is implemented...

  14. Real-time fault detection for a waste-water treatment plant

    International Nuclear Information System (INIS)

    Automatic fault detection is becoming increasingly important in wastewater treatment plant operation, given the stringent treatment standards and the need to protect the investment costs from the potential damage of an unchecked fault propagating through the plant. This paper describes the development of a real-time Fault Detection and Isolation (FDI) system based on an adaptive Principal Component Analysis (PCA) algorithm, used to compare the current plant operation with a good behaviour model based on a preliminary set of data. The algorithm was developed in the Lab View 8.20 (National Instruments, Austin, TX, USA) platform for real-time operation in the compact Field Point, a Programmable Automation Controller by National Instruments supervising the plant operation. The FDI was tested with a large set of operational plant data with 1 hour sampling time from August 2007 through May 2008. Two time horizons were used in the analysis: a short term monthly horizon proved very reliable in isolating sensor failures and short duration disturbances such as spikes, whereas the long term horizon provided accurate detection of long-term drifts. The system robustness is enhanced by the use of multiple statistics, not only control charts but also contribution plots, which proved instrumental in discriminating among the various causes of malfunctioning.

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

    Science.gov (United States)

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

    2016-01-01

    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. PMID:27548183

  16. 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.

  17. 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

    2016-01-01

    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. PMID:27548183

  18. Estimating the detectability of faults in 3D-seismic data - A valuable input to Induced Seismic Hazard Assessment (ISHA)

    Science.gov (United States)

    Goertz, A.; Kraft, T.; Wiemer, S.; Spada, M.

    2012-12-01

    In the past several years, some geotechnical operations that inject fluid into the deep subsurface, such as oil and gas development, waste disposal, and geothermal energy development, have been found or suspected to cause small to moderate sized earthquakes. In several cases the largest events occurred on previously unmapped faults, within or in close vicinity to the operated reservoirs. The obvious conclusion drawn from this finding, also expressed in most recently published best practice guidelines and recommendations, is to avoid injecting into faults. Yet, how certain can we be that all faults relevant to induced seismic hazard have been identified, even around well studied sites? Here we present a probabilistic approach to assess the capability of detecting faults by means of 3D seismic imaging. First, we populate a model reservoir with seed faults of random orientation and slip direction. Drawing random samples from a Gutenberg-Richter distribution, each seed fault is assigned a magnitude and corresponding size using standard scaling relations based on a circular rupture model. We then compute the minimum resolution of a 3D seismic survey for given acquisition parameters and frequency bandwidth. Assuming a random distribution of medium properties and distribution of image frequencies, we obtain a probability that a fault of a given size is detected, or respectively overlooked, by the 3D seismic. Weighting the initial Gutenberg-Richter fault size distribution with the probability of imaging a fault, we obtain a modified fault size distribution in the imaged volume from which we can constrain the maximum magnitude to be considered in the seismic hazard assessment of the operation. We can further quantify the value of information associated with the seismic image by comparing the expected insured value loss between the image-weighted and the unweighted hazard estimates.

  19. Design of a bilinear fault detection observer for singular bilinear systems

    Institute of Scientific and Technical Information of China (English)

    Zhanshan WANG; Huaguang ZHANG

    2007-01-01

    A bilinear fault detection observer is proposed for a class of continuous time singular bilinear systems subject to unknown input disturbance and fault.By singular value decomposition on the original system,a bilinear fault detection observer is proposed for the decomposed system via an algebraic Riccati equation,and the domain of attraction of the state estimation error is estimated.A design procedure is presented to determine the fault detection threshold.A model of flexible joint robot is used to demonstrate the effectiveness of the proposed method.

  20. Fault Tolerant Control for Civil Structures Based on LMI Approach

    Directory of Open Access Journals (Sweden)

    Chunxu Qu

    2013-01-01

    Full Text Available The control system may lose the performance to suppress the structural vibration due to the faults in sensors or actuators. This paper designs the filter to perform the fault detection and isolation (FDI and then reforms the control strategy to achieve the fault tolerant control (FTC. The dynamic equation of the structure with active mass damper (AMD is first formulated. Then, an estimated system is built to transform the FDI filter design problem to the static gain optimization problem. The gain is designed to minimize the gap between the estimated system and the practical system, which can be calculated by linear matrix inequality (LMI approach. The FDI filter is finally used to isolate the sensor faults and reform the FTC strategy. The efficiency of FDI and FTC is validated by the numerical simulation of a three-story structure with AMD system with the consideration of sensor faults. The results show that the proposed FDI filter can detect the sensor faults and FTC controller can effectively tolerate the faults and suppress the structural vibration.

  1. Actuator Fault Detection for Sampled-Data Systems in H∞ Setting

    Institute of Scientific and Technical Information of China (English)

    YANG Xiao-jun; WENG Zheng-xin; TIAN Zuo-hua

    2005-01-01

    Actuator fault detection for sampled-data systems was investigated from the viewpoint of jump systems.With the aid of a prior frequency information on fault, such a problem is converted to an augmented H∞ filtering problem. A simple state-space approach is then proposed todeal with sampled-data actuator fault detection problem. Compared with the existed approaches, the proposed approach allows parameters of the sampled-data system being time-varying with consideration of measurement noise.

  2. 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.

  3. Phase-compensation-based dynamic time warping for fault diagnosis using the motor current signal

    Science.gov (United States)

    Zhen, D.; Zhao, H. L.; Gu, F.; Ball, A. D.

    2012-05-01

    Dynamic time warping (DTW) is a time-domain-based method and widely used in various similar recognition and data mining applications. This paper presents a phase-compensation-based DTW to process the motor current signals for detecting and quantifying various faults in a two-stage reciprocating compressor under different operating conditions. DTW is an effective method to align two signals for dissimilarity analysis. However, it has drawbacks such as singularities and high computational demands that limit its application in processing motor current signals for obtaining modulation characteristics accurately in diagnosing compressor faults. Therefore, a phase compensation approach is developed to reduce the singularity effect and a sliding window is designed to improve computing efficiency. Based on the proposed method, the motor current signals measured from the compressor induced with different common faults are analysed for fault diagnosis. Results show that residual signal analysis using the phase-compensation-based DTW allows the fault-related sideband features to be resolved more accurately for obtaining reliable fault detection and diagnosis. It provides an effective and easy approach to the analysis of motor current signals for better diagnosis in the time domain in comparison with conventional Fourier-transform-based methods.

  4. Phase-compensation-based dynamic time warping for fault diagnosis using the motor current signal

    International Nuclear Information System (INIS)

    Dynamic time warping (DTW) is a time-domain-based method and widely used in various similar recognition and data mining applications. This paper presents a phase-compensation-based DTW to process the motor current signals for detecting and quantifying various faults in a two-stage reciprocating compressor under different operating conditions. DTW is an effective method to align two signals for dissimilarity analysis. However, it has drawbacks such as singularities and high computational demands that limit its application in processing motor current signals for obtaining modulation characteristics accurately in diagnosing compressor faults. Therefore, a phase compensation approach is developed to reduce the singularity effect and a sliding window is designed to improve computing efficiency. Based on the proposed method, the motor current signals measured from the compressor induced with different common faults are analysed for fault diagnosis. Results show that residual signal analysis using the phase-compensation-based DTW allows the fault-related sideband features to be resolved more accurately for obtaining reliable fault detection and diagnosis. It provides an effective and easy approach to the analysis of motor current signals for better diagnosis in the time domain in comparison with conventional Fourier-transform-based methods. (paper)

  5. A fault-based model for crustal deformation, fault slip-rates and off-fault strain rate in California

    Science.gov (United States)

    Zeng, Yuehua; Shen, Zheng-Kang

    2016-01-01

    We invert Global Positioning System (GPS) velocity data to estimate fault slip rates in California using a fault‐based crustal deformation model with geologic constraints. The model assumes buried elastic dislocations across the region using Uniform California Earthquake Rupture Forecast Version 3 (UCERF3) fault geometries. New GPS velocity and geologic slip‐rate data were compiled by the UCERF3 deformation working group. The result of least‐squares inversion shows that the San Andreas fault slips at 19–22  mm/yr along Santa Cruz to the North Coast, 25–28  mm/yr along the central California creeping segment to the Carrizo Plain, 20–22  mm/yr along the Mojave, and 20–24  mm/yr along the Coachella to the Imperial Valley. Modeled slip rates are 7–16  mm/yr lower than the preferred geologic rates from the central California creeping section to the San Bernardino North section. For the Bartlett Springs section, fault slip rates of 7–9  mm/yr fall within the geologic bounds but are twice the preferred geologic rates. For the central and eastern Garlock, inverted slip rates of 7.5 and 4.9  mm/yr, respectively, match closely with the geologic rates. For the western Garlock, however, our result suggests a low slip rate of 1.7  mm/yr. Along the eastern California shear zone and southern Walker Lane, our model shows a cumulative slip rate of 6.2–6.9  mm/yr across its east–west transects, which is ∼1  mm/yr increase of the geologic estimates. For the off‐coast faults of central California, from Hosgri to San Gregorio, fault slips are modeled at 1–5  mm/yr, similar to the lower geologic bounds. For the off‐fault deformation, the total moment rate amounts to 0.88×1019  N·m/yr, with fast straining regions found around the Mendocino triple junction, Transverse Ranges and Garlock fault zones, Landers and Brawley seismic zones, and farther south. The overall California moment rate is 2.76×1019

  6. Equipment fault diagnosis system of sequencing batch reactors using rule-based fuzzy inference and on-line sensing data.

    Science.gov (United States)

    Kim, Y J; Bae, H; Poo, K M; Ko, J H; Kim, B G; Park, T J; Kim, C W

    2006-01-01

    The importance of a detection technique to prevent process deterioration is increasing. For the fast detection of this disturbance, a diagnostic algorithm was developed to determine types of equipment faults by using on-line ORP and DO profile in sequencing batch reactors (SBRs). To develop the rule base for fault diagnosis, the sensor profiles were obtained from a pilot-scale SBR when blower, influent pump and mixer were broken. The rules were generated based on the calculated error between an abnormal profile and a normal profile, e(ORP)(t) and e(DO)(t). To provide intermediate diagnostic results between "normal" and "fault", a fuzzy inference algorithm was incorporated to the rules. Fuzzified rules could present the diagnosis result "need to be checked". The diagnosis showed good performance in detecting and diagnosing various faults. The developed algorithm showed its applicability to detect faults and make possible fast action to correct them. PMID:16722090

  7. Fault diagnosis of motor drives using stator current signal analysis based on dynamic time warping

    Science.gov (United States)

    Zhen, D.; Wang, T.; Gu, F.; Ball, A. D.

    2013-01-01

    Electrical motor stator current signals have been widely used to monitor the condition of induction machines and their downstream mechanical equipment. The key technique used for current signal analysis is based on Fourier transform (FT) to extract weak fault sideband components from signals predominated with supply frequency component and its higher order harmonics. However, the FT based method has limitations such as spectral leakage and aliasing, leading to significant errors in estimating the sideband components. Therefore, this paper presents the use of dynamic time warping (DTW) to process the motor current signals for detecting and quantifying common faults in a downstream two-stage reciprocating compressor. DTW is a time domain based method and its algorithm is simple and easy to be embedded into real-time devices. In this study DTW is used to suppress the supply frequency component and highlight the sideband components based on the introduction of a reference signal which has the same frequency component as that of the supply power. Moreover, a sliding window is designed to process the raw signal using DTW frame by frame for effective calculation. Based on the proposed method, the stator current signals measured from the compressor induced with different common faults and under different loads are analysed for fault diagnosis. Results show that DTW based on residual signal analysis through the introduction of a reference signal allows the supply components to be suppressed well so that the fault related sideband components are highlighted for obtaining accurate fault detection and diagnosis results. In particular, the root mean square (RMS) values of the residual signal can indicate the differences between the healthy case and different faults under varying discharge pressures. It provides an effective and easy approach to the analysis of motor current signals for better fault diagnosis of the downstream mechanical equipment of motor drives in the time

  8. Interseismic Crustal Deformation in and around the Atotsugawa Fault System, Central Japan, Detected by InSAR and GNSS

    Science.gov (United States)

    Takada, Y.; Sagiya, T.; Nishimura, T.

    2015-12-01

    Interseismic crustal deformation of active faults provides crucial information to understand the stress accumulation process on the fault planes. Recently, the interseismic surface movements are detected with very high spatial resolution using combination of InSAR and GNSS survey. Most of the successful reports, however, addressed the fault creep in less vegetated area which enables C-band SAR interferometry. In this study, we report the interseismic crustal deformation in and around the Atotsugawa fault system, a strike-slip active fault in central Japan. This area is covered with dense vegetation in summer and with heavy snow in winter. We created a series of InSAR images acquired by ALOS/PALSAR and applied SBAS based time-series analysis (Berardino et al., 2002) to extract small deformation. Next, we corrected the long wave-length phase trend by GNSS network maintained by Japanese University Group (e.g, Ohzono et al., 2011) and GSI, Japan. The mean velocity field thus obtained shows a strain concentration zone along the Ushikubi fault, a major strand of the Atotsugawa fault system. The Ushikubi fault is seismically less active than the Atotsugawa fault, but it shows good correlation with a zone of large spatial gradient of Bouguer gravity anomaly. We further discuss on the deformation style at the junction between the Atotsugawa fault and the Hida mountain range (Tateyama volcano). Acknowledgement: The PALSAR level 1.0 data were provided by JAXA via the PALSAR Interferometry Consortium to Study our Evolving Land surface (PIXEL) based on a cooperative research contract between JAXA and the ERI, the University of Tokyo. The PALSAR product is owned by JAXA and METI.

  9. Spectral kurtosis for fault detection, diagnosis and prognostics of rotating machines: A review with applications

    Science.gov (United States)

    Wang, Yanxue; Xiang, Jiawei; Markert, Richard; Liang, Ming

    2016-01-01

    Condition-based maintenance via vibration signal processing plays an important role to reduce unscheduled machine downtime and avoid catastrophic accidents in industrial enterprises. Many machine faults, such as local defects in rotating machines, manifest themselves in the acquired vibration signals as a series of impulsive events. The spectral kurtosis (SK) technique extends the concept of kurtosis to that of a function of frequency that indicates how the impulsiveness of a signal. This work intends to review and summarize the recent research developments on the SK theories, for instance, short-time Fourier transform-based SK, kurtogram, adaptive SK and protrugram, as well as the corresponding applications in fault detection and diagnosis of the rotating machines. The potential prospects of prognostics using SK technique are also designated. Some examples have been presented to illustrate their performances. The expectation is that further research and applications of the SK technique will flourish in the future, especially in the fields of the prognostics.

  10. Usage of Inrush Current Surge for Early Detection of Inter-Winding Faults

    OpenAIRE

    Dolgicers, A; Kozadajevs, J; Zālītis, I

    2014-01-01

    This paper presents the development of micro- processor based device for improving the sensitivity of differential protection of power transformers. Power transformers failure may lead to high scale system’s operations disruption and heavy economic losses, that’s why the lever of requirements for power transformer protections is so high. Differential protection is usually used as a main protection against internal faults such as inter-windings faults, inter-coil faults or coil-core faults. Fo...

  11. A neural network approach to fault detection in spacecraft attitude determination and control systems

    Science.gov (United States)

    Schreiner, John N.

    This thesis proposes a method of performing fault detection and isolation in spacecraft attitude determination and control systems. The proposed method works by deploying a trained neural network to analyze a set of residuals that are defined such that they encompass the attitude control, guidance, and attitude determination subsystems. Eight neural networks were trained using either the resilient backpropagation, Levenberg-Marquardt, or Levenberg-Marquardt with Bayesian regularization training algorithms. The results of each of the neural networks were analyzed to determine the accuracy of the networks with respect to isolating the faulty component or faulty subsystem within the ADCS. The performance of the proposed neural network-based fault detection and isolation method was compared and contrasted with other ADCS FDI methods. The results obtained via simulation showed that the best neural networks employing this method successfully detected the presence of a fault 79% of the time. The faulty subsystem was successfully isolated 75% of the time and the faulty components within the faulty subsystem were isolated 37% of the time.

  12. Adaptive Observer-Based Fault Estimate for Nonlinear Systems

    Institute of Scientific and Technical Information of China (English)

    ZONG Qun; LIU Wenjing; LIU Li

    2006-01-01

    An approach for adaptive observer-based fault estimate for nonlinear system is proposed.H-infinity theory is applied to analyzing the design method and stable conditions of the adaptive observer,from which both system state and fault can be estimated.It is proved that the fault estimate error is related to the given H-infinity track performance indexes,as well as to the changing rate of the fault and the Lipschitz constant of the nonlinear item.The design steps of the adaptive observer are proposed.The simulation results show that the observer has good performance for fault estimate even when the system includes nonlinear terms,which confirms the effectiveness of the method.

  13. Fault Localization Analysis Based on Deep Neural Network

    Directory of Open Access Journals (Sweden)

    Wei Zheng

    2016-01-01

    Full Text Available With software’s increasing scale and complexity, software failure is inevitable. To date, although many kinds of software fault localization methods have been proposed and have had respective achievements, they also have limitations. In particular, for fault localization techniques based on machine learning, the models available in literatures are all shallow architecture algorithms. Having shortcomings like the restricted ability to express complex functions under limited amount of sample data and restricted generalization ability for intricate problems, the faults cannot be analyzed accurately via those methods. To that end, we propose a fault localization method based on deep neural network (DNN. This approach is capable of achieving the complex function approximation and attaining distributed representation for input data by learning a deep nonlinear network structure. It also shows a strong capability of learning representation from a small sized training dataset. Our DNN-based model is trained utilizing the coverage data and the results of test cases as input and we further locate the faults by testing the trained model using the virtual test suite. This paper conducts experiments on the Siemens suite and Space program. The results demonstrate that our DNN-based fault localization technique outperforms other fault localization methods like BPNN, Tarantula, and so forth.

  14. Fault Detection on the Software Implementation of CLEFIA Lightweight Cipher

    OpenAIRE

    Wei Li; Dawu Gu; Xiaoling Xia; Ya Liu; Zhiqiang Liu

    2012-01-01

    CLEFIA is an efficient lightweight cipher that delivers advanced copyright protection and authentication in computer networks. It is also applied in the secure protocol for transmission including SSL and TLS. Since it was proposed in 2007, some work about its security against differential fault analysis has been devoted to reducing the number of faults and to improving the time complexity of this attack. This attack is very efficient when a single fault is injected into the last several round...

  15. Application of the fault diagnosis strategy based on hierarchical information fusion in motors fault diagnosis

    Institute of Scientific and Technical Information of China (English)

    2006-01-01

    This paper has analyzed merits and demerits of both neural network technique and of the information fusion methods based on the D-S (dempster-shafer evidence) Theory as well as their complementarity, proposed the hierarchical information fusion fault diagnosis strategy by combining the neural network technique and the fused decision diagnosis based on D-S Theory, and established a corresponding functional model. Thus, we can not only solve a series of problems caused by rapid growth in size and complexity of neural network structure with diagnosis parameters increasing, but also can provide effective method for basic probability assignment in D-S Theory. The application of the strategy to diagnosing faults of motor bearings has proved that this method is of fairly high accuracy and reliability in fault diagnosis.

  16. Robust fault diagnosis for non-Gaussian stochastic systems based on the rational square-root approximation model

    Institute of Scientific and Technical Information of China (English)

    YAO LiNa; WANG Hong

    2008-01-01

    The task of robust fault detection and diagnosis of stochastic distribution control (SDC) systems with uncertainties is to use the measured input and the system output PDFs to still obtain possible faults information of the system. Using the ra-tional square-root B-spline model to represent the dynamics between the output PDF and the input, in this paper, a robust nonlinear adaptive observer-based fault diagnosis algorithm is presented to diagnose the fault in the dynamic part of such systems with model uncertainties. When certain conditions are satisfied, the weight vector of the rational square-root B-spline model proves to be bounded. Conver-gency analysis is performed for the error dynamic system raised from robust fault detection and fault diagnosis phase. Computer simulations are given to demon-strate the effectiveness of the proposed algorithm.

  17. A Novel Algorithm for Fault Classification on Transmission Lines using a Combined Adaptive Network-based Fuzzy Inference System

    Energy Technology Data Exchange (ETDEWEB)

    Yeo, S.M.; Kim, C.H. [Sungkyunkwan University (Korea); Chai, Y.M. [Chungju National University (Korea); Choi, J.D. [Daelim College (Korea)

    2001-07-01

    Accurate detection and classification of faults on transmission lines is vitally important. High impedance faults (HIF) in particular pose difficulties for the commonly employed conventional overcurrent and distance relays, and if not detected, can cause damage to expensive equipment, threaten life and cause fire hazards. Although HIFs are far less common than LIFs, it is imperative that any protection device should be able to satisfactorily deal with both HIFs and LIFs. This paper proposes an algorithm for fault detection and classification for both LIFs and HIFs using Adaptive Network-based Fuzzy Inference System(ANFIS). The performance of the proposed algorithm is tested on a typical 154[kV] Korean transmission line system under various fault conditions. Test results show that the ANFIS can detect and classify faults including (LIFs and HIFs) accurately within half a cycle. (author). 11 refs., 7 figs., 3 tabs.

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

    Directory of Open Access Journals (Sweden)

    Qiaoning Yang

    2015-10-01

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

  19. Railway faults spreading model based on dynamics of complex network

    Science.gov (United States)

    Zhou, Jin; Xu, Weixiang; Guo, Xin; Ma, Xin

    2015-12-01

    In this paper, we propose a railway faults spreading model which improved the SIR model and made it suitable for analyzing the dynamic process of faults spreading. To apply our model into a real network, the accident causation network of "7.23" China Yongwen high-speed railway accident is employed. This network is improved into a directed network, which more clearly reflects the causation relationships among the accident factors and provides help for our studies. Simulation results quantitatively show that the influence of failures can be diminished via choosing the appropriate initial recovery factors, reducing the time of the failure detected, decreasing the transmission rate of faults and increasing the propagating rate of corrected information. The model is useful to simulate the railway faults spreading and quantitatively analyze the influence of failures.

  20. Multifractal entropy based adaptive multiwavelet construction and its application for mechanical compound-fault diagnosis

    Science.gov (United States)

    He, Shuilong; Chen, Jinglong; Zhou, Zitong; Zi, Yanyang; Wang, Yanxue; Wang, Xiaodong

    2016-08-01

    Compound-fault diagnosis of mechanical equipment is still challenging at present because of its complexity, multiplicity and non-stationarity. In this work, an adaptive redundant multiwavelet packet (ARMP) method is proposed for the compound-fault diagnosis. Multiwavelet transform has two or more base functions and many excellent properties, making it suitable for detecting all the features of compound-fault simultaneously. However, on the other hand, the fixed basis function used in multiwavelet transform may decrease the accuracy of fault extraction; what's more, the multi-resolution analysis of multiwavelet transform in low frequency band may also leave out the useful features. Thus, the minimum sum of normalized multifractal entropy is adopted as the optimization criteria for the proposed ARMP method, while the relative energy ratio of the characteristic frequency is utilized as an effective way in automatically selecting the sensitive frequency bands. Then, The ARMP technique combined with Hilbert transform demodulation analysis is then applied to detect the compound-fault of bevel gearbox and planetary gearbox. The results verify that the proposed method can effectively identify and detect the compound-fault of mechanical equipment.

  1. 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.

  2. A Novel Framework for Real-Time Fault Diagnosis Based on Dynamic Fault Tree Analysis

    Directory of Open Access Journals (Sweden)

    Rongxing Duan

    2013-02-01

    Full Text Available To meet the real-time diagnosis requirements of the complex system, this study proposes a novel framework for real-time fault diagnosis using dynamic fault tree analysis. It pays special attention to meeting two challenges: model development and real-time reasoning. In terms of the challenge of model development, we use a dynamic fault tree model to capture the dynamic behavior of system failure mechanisms and calculate some reliability results by mapping a dynamic fault tree into an equivalent Bayesian Network (BN in order to avoid the infamous state space explosion problem. In terms of the real-time reasoning challenge, we adopt a logic compilation based inference algorithm, which compiles the BN into an arithmetic circuit and retrieves answers to probabilistic queries by evaluating and differentiating the arithmetic circuit. Furthermore, we incorporate sensors data into fault diagnosis, cope with the sensors reliability and propose the schemes on how to update the Diagnostic Importance Factor (DIF and the minimal cut sets. Finally, a case study is given to validate the efficiency of this method.

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

    International Nuclear Information System (INIS)

    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

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

    OpenAIRE

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

    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 comm...

  5. Research on Fault Evaluation of Armament Equipment Based on ADAMS

    Institute of Scientific and Technical Information of China (English)

    2001-01-01

    The levels of simulation are introduced, and the importance of virtual prototyping of armament equipment is discussed and steps of virtual prototyping are outlined. The faults that affect firing performance are discussea, ADAMS is first to be introduced to armament equipment,and a virtual prototyping model of artillery is established with the help of Fortran language based on analysis of topology of artillery and forces applied on it. The plan of fault evaluation is brought forward, the modules are analyzed, and the concept of fault evaluation function is introduced Finally, the perspective of virtual technology is presented.

  6. 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.

  7. PEM fuel cell fault detection and identification using differential method: simulation and experimental validation

    Science.gov (United States)

    Frappé, E.; de Bernardinis, A.; Bethoux, O.; Candusso, D.; Harel, F.; Marchand, C.; Coquery, G.

    2011-05-01

    PEM fuel cell performance and lifetime strongly depend on the polymer membrane and MEA hydration. As the internal moisture is very sensitive to the operating conditions (temperature, stoichiometry, load current, water management…), keeping the optimal working point is complex and requires real-time monitoring. This article focuses on PEM fuel cell stack health diagnosis and more precisely on stack fault detection monitoring. This paper intends to define new, simple and effective methods to get relevant information on usual faults or malfunctions occurring in the fuel cell stack. For this purpose, the authors present a fault detection method using simple and non-intrusive on-line technique based on the space signature of the cell voltages. The authors have the objective to minimize the number of embedded sensors and instrumentation in order to get a precise, reliable and economic solution in a mass market application. A very low number of sensors are indeed needed for this monitoring and the associated algorithm can be implemented on-line. This technique is validated on a 20-cell PEMFC stack. It demonstrates that the developed method is particularly efficient in flooding case. As a matter of fact, it uses directly the stack as a sensor which enables to get a quick feedback on its state of health.

  8. Bond graph to digraph conversion: A sensor placement optimization for fault detection and isolation by a structural approach

    Indian Academy of Sciences (India)

    Alem Saïd; Benazzouz Djamel

    2014-10-01

    In this paper, we consider the optimal sensors placement problem for faults detection and isolation using a novel structural and qualitative approach. This approach is based on the conversion of Bond Graph to Digraph representation of a structural system. When the fault detection and isolation of an existing system’s sensors are impossible or uncertain, a reconfiguration sensor placement of this system should be reconsidered. This paper proposes how this reconfiguration takes place by recovering all missing or redundant parts of the system. This novel approach is illustrated over a thermo-fluid application.

  9. Fault detection for chemical process based on locally linear embedding%基于局部线性嵌入算法的化工过程故障检测

    Institute of Scientific and Technical Information of China (English)

    马玉鑫; 王梦灵; 侍洪波

    2012-01-01

    随着工业过程日趋复杂,系统安全及产品质量的在线监控也变得日益重要.针对化工过程的非线性特点,提出了一种新的基于局部线性嵌入(locally linear embedding,LLE)流形学习算法和支持向量数据描述(support vector data description,SVDD)的故障检测方法.首先,使用LLE提取高维数据的低维子流形,进行维数约减,以保存更多原有系统的非线性特性,通过局部线性回归得到高维数据空间到低维特征空间的映射矩阵,保证了算法的实时性;然后,为了避免数据噪声的累加对传统统计量的影响,引入SVDD直接根据特征空间建立SVDD模型,构造统计量并确定其控制限;最后,通过数字仿真及Tennessee Eastman (TE)过程仿真研究验证了本文方法的有效性.%As industrial processes become more complex, on-line monitoring of the processes are gaining importance for plant safety, maintenance, and product quality. To handle the nonlinear problem for process monitoring, a novel fault detection method was proposed by combining locally linear embedding (LLE) with support vector data description (SVDD). Firstly, LLE manifold learning algorithm was performed for nonlinear dimensionality reduction and thus the main feature of the collected data was extracted. Then, the mapping matrix from data space to feature space was calculated by using local linear regression which guaranteed the real-time property of the algorithm. Next, in order to avoid the influence of data noise on the traditional statistics, a fault detection model was established based on SVDD in the feature space, while a corresponding statistic and its control limit were determined at the same time. Finally, the feasibility and efficiency of the proposed method were shown through a numerical simulation and the Tennessee Eastman (TE) benchmark process.

  10. A novel fault detection method for batch process based on digraph model%一种新的基于图模型的间歇过程故障监测方法

    Institute of Scientific and Technical Information of China (English)

    张玉良; 张贝克; 马昕; 曹柳林

    2014-01-01

    So far, the multivariate statistical methods have been widely used to perform fault detection for batch process, and they have shown some good performances .However, the interpretability of statistical model is not good, and it is difficult to directly use the safety experience of operators .In order to cope with these shortcomings , a fault detection method based on digraph model was proposed .By using the proposed qualitative modeling method , the process mechanism and safety experience of operators can be expressed easily .On-line data of process variables and forward reasoning algorithm were used to deduce the state of production process .The safety knowledge was used to find abnormal production state in time .When deduced conclusion was not consistent with measured process variable value or process variable value exceeds threshold , an explanation for this situation would be provided .By means of a batch reaction case , the advantages of proposed method on model interpretability and using safety expe-rience were verified .%目前多元统计方法被广泛用于间歇过程故障监测并已经取得了比较好的效果。但是,统计模型的可解释性能比较差,很难直接利用操作人员积累的安全经验。为应对这些不足,提出了一种基于图模型的间歇过程故障监测方法。利用提出的定性建模方法,过程机理及操作人员的安全经验能够方便地表达。利用在线的过程变量数据和正向推理算法推断生产应处于的状态,利用安全知识及时地发现生产异常。当推断的结论与在线测得的结果矛盾或过程变量超过设定的安全限时,给出解释性输出。通过一个间歇反应案例,验证了提出的方法在模型的可解释性和利用安全经验方面的优势。

  11. Fault tolerant control with torque limitation based on fault mode for ten-phase permanent magnet synchronous motor

    Institute of Scientific and Technical Information of China (English)

    Guo Hong; Xu Jinquan

    2015-01-01

    This paper proposes a novel fault tolerant control with torque limitation based on the fault mode for the ten-phase permanent magnet synchronous motor (PMSM) under various open-circuit and short-circuit fault conditions, which includes the optimal torque control and the torque limitation control based on the fault mode. The optimal torque control is adopted to guarantee the ripple-free electromagnetic torque operation for the ten-phase motor system under the post-fault condition. Furthermore, we systematically analyze the load capacity of the ten-phase motor system under different fault modes. And a torque limitation control approach based on the fault mode is proposed, which was not available earlier. This approach is able to ensure the safety operation of the faulted motor system in long operating time without causing the overheat fault. The simulation result confirms that the proposed fault tolerant control for the ten-phase motor system is able to guarantee the ripple-free electromagnetic torque and the safety operation in long operating time under the normal and fault conditions.

  12. Fault tolerant control with torque limitation based on fault mode for ten-phase permanent magnet synchronous motor

    Directory of Open Access Journals (Sweden)

    Guo Hong

    2015-10-01

    Full Text Available This paper proposes a novel fault tolerant control with torque limitation based on the fault mode for the ten-phase permanent magnet synchronous motor (PMSM under various open-circuit and short-circuit fault conditions, which includes the optimal torque control and the torque limitation control based on the fault mode. The optimal torque control is adopted to guarantee the ripple-free electromagnetic torque operation for the ten-phase motor system under the post-fault condition. Furthermore, we systematically analyze the load capacity of the ten-phase motor system under different fault modes. And a torque limitation control approach based on the fault mode is proposed, which was not available earlier. This approach is able to ensure the safety operation of the faulted motor system in long operating time without causing the overheat fault. The simulation result confirms that the proposed fault tolerant control for the ten-phase motor system is able to guarantee the ripple-free electromagnetic torque and the safety operation in long operating time under the normal and fault conditions.

  13. 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.

  14. Aircraft Engine Sensor Fault Diagnostics Based on Estimation of Engine's Health Degradation

    Institute of Scientific and Technical Information of China (English)

    Xue Wei; Guo Yingqing

    2009-01-01

    bank, the real faults that have occurred can be detected and isolated. The on-line fault detection algorithm has the ability of maintaining the effectiveness over the engine's lifetime and is verified by simulation using a nonlinear engine model.

  15. Research on Transformer Fault Based on Probabilistic Neural Network

    OpenAIRE

    Li Yingshun; Li Jingjing; Han Junfeng

    2015-01-01

    With the development of computer science and technology, and increasingly intelligent industrial production, the application of big data in industry also advances rapidly, and the development of artificial intelligence in the aspect of fault diagnosis is particularly prominent. On the basis of MATLAB platform, this paper constructs a fault diagnosis expert system of artificial intelligence machine based on the probabilistic neural network, and it also carries out a simulation of production pr...

  16. Fault location method for transmission line based on traveling waves

    Institute of Scientific and Technical Information of China (English)

    ZHENG Na; ZHAO Yulin

    2007-01-01

    The single phase grounding fault location is the focus which researchers pay attention to and study in power system. The accurate fault location can lighten the patrolling burden, and enhance the reliability of the power network. It adopts A/D which has high speed, and uses TMS320VC5402 DSP chip as the system core. This paper presented theory of operation based on traveling waves and achieved software and hardware in detail.

  17. Fault Detection of Reciprocating Compressors using a Model from Principles Component Analysis of Vibrations

    Science.gov (United States)

    Ahmed, M.; Gu, F.; Ball, A. D.

    2012-05-01

    Traditional vibration monitoring techniques have found it difficult to determine a set of effective diagnostic features due to the high complexity of the vibration signals originating from the many different impact sources and wide ranges of practical operating conditions. In this paper Principal Component Analysis (PCA) is used for selecting vibration feature and detecting different faults in a reciprocating compressor. Vibration datasets were collected from the compressor under baseline condition and five common faults: valve leakage, inter-cooler leakage, suction valve leakage, loose drive belt combined with intercooler leakage and belt loose drive belt combined with suction valve leakage. A model using five PCs has been developed using the baseline data sets and the presence of faults can be detected by comparing the T2 and Q values from the features of fault vibration signals with corresponding thresholds developed from baseline data. However, the Q -statistic procedure produces a better detection as it can separate the five faults completely.

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

    NARCIS (Netherlands)

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

    2016-01-01

    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 prop

  19. 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. Th...

  20. 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...

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

    Directory of Open Access Journals (Sweden)

    Mark Frogley

    2013-01-01

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

  2. 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

  3. Application of Schlumberger transverse profiling method to detecting a strike fault

    Institute of Scientific and Technical Information of China (English)

    2006-01-01

    Because it is difficult to detect a strike fault, its physical properties are discussed in this paper. Using physical simulation, numerical modeling and the in situ data, the differences between the apparent resistivity of low resistivity model obtained by transverse profiling method (TPM) whose electrode array is vertical to the profile and those by longitudinal profiling method (LPM) whose electrode array is parallel to the profile are analyzed, respectively. Our results show that the former has much marked amplitudes of anomaly. Therefore, TPM can be used to detect a strike fault more effectively and locate it more precisely, and is expected to be a new approach for detecting a sliding fault.

  4. DETECTION OF INCIPIENT LOCALIZED GEAR FAULTS IN GEARBOX BY COMPLEX CONTINUOUS WAVELET TRANSFORM

    Institute of Scientific and Technical Information of China (English)

    Han Zhennan; Xiong Shibo; Li Jinbao

    2003-01-01

    As far as the vibration signal processing is concerned, composition of vibration signal resulting from incipient localized faults in gearbox is too weak to be detected by traditional detecting technology available now. The method, which includes two steps: vibration signal from gearbox is first processed by synchronous average sampling technique and then it is analyzed by complex continuous wavelet transform to diagnose gear fault, is introduced. Two different kinds of faults in the gearbox, i.e.shaft eccentricity and initial crack in tooth fillet, are detected and distinguished from each other successfully.

  5. Clustering of the Self-Organizing Map based Approach in Induction Machine Rotor Faults Diagnostics

    Directory of Open Access Journals (Sweden)

    Ahmed TOUMI

    2009-12-01

    Full Text Available Self-Organizing Maps (SOM is an excellent method of analyzingmultidimensional data. The SOM based classification is attractive, due to itsunsupervised learning and topology preserving properties. In this paper, theperformance of the self-organizing methods is investigated in induction motorrotor fault detection and severity evaluation. The SOM is based on motor currentsignature analysis (MCSA. The agglomerative hierarchical algorithms using theWard’s method is applied to automatically dividing the map into interestinginterpretable groups of map units that correspond to clusters in the input data. Theresults obtained with this approach make it possible to detect a rotor bar fault justdirectly from the visualization results. The system is also able to estimate theextent of rotor faults.

  6. Data mining based sensor fault diagnosis and validation for building air conditioning system

    International Nuclear Information System (INIS)

    A strategy based on the data mining (DM) method is developed to detect and diagnose sensor faults based on the past running performance data in heating, ventilating and air conditioning (HVAC) systems, combining a rough set approach and an artificial neural network (ANN). The reduced information is used to develop classification rules and train the neural network to infer appropriate parameters. The differences between measured thermodynamic states and predicted states obtained from models for normal performance (residuals) are used as performance indices for sensor fault detection and diagnosis. Real test results from a real HVAC system show that only the temperature and humidity measurements of many air handling units (AHU) can work very well as the measurements to distinguish simultaneous temperature sensor faults of the supply chilled water (SCW) and return chilled water (RCW)

  7. Fault diagnosis for power system transmission line based on PCA and SVMs

    Energy Technology Data Exchange (ETDEWEB)

    Guo, Yuanjun; Li, Kang; Liu, Xueqin [Queen' s Univ., Belfast (United Kingdom). School of Electronics, Electrical Engineering and Computer Science

    2013-07-01

    This paper presents the application of a fault detection method based on the principal component analysis (PCA) and support vector machine (SVM) for the detection and classification of faults in power system transmission lines. Consider that the data may be huge with a number of strongly correlated variables, method which incorporates both the principal component analysis (PCA) and support vector machine (SVM) is proposed. This algorithm has two stages. The first stage involves the use of the PCA to reduce the dimensionality as well as to find violating point of the signals according to the confidential limit. The features of each fault extracted from the data are used in the second stage to construct SVM networks. The second stage is to use pattern recognition method to distinguish the phase of the faulty situation. The proposed scheme is able to solve the problems encountered in traditional magnitude and frequency based methods. The benefits of this improvement are demonstrated.

  8. Fault diagnosis for the heat exchanger of the aircraft environmental control system based on the strong tracking filter.

    Science.gov (United States)

    Ma, Jian; Lu, Chen; Liu, Hongmei

    2015-01-01

    The aircraft environmental control system (ECS) is a critical aircraft system, which provides the appropriate environmental conditions to ensure the safe transport of air passengers and equipment. The functionality and reliability of ECS have received increasing attention in recent years. The heat exchanger is a particularly significant component of the ECS, because its failure decreases the system's efficiency, which can lead to catastrophic consequences. Fault diagnosis of the heat exchanger is necessary to prevent risks. However, two problems hinder the implementation of the heat exchanger fault diagnosis in practice. First, the actual measured parameter of the heat exchanger cannot effectively reflect the fault occurrence, whereas the heat exchanger faults are usually depicted by utilizing the corresponding fault-related state parameters that cannot be measured directly. Second, both the traditional Extended Kalman Filter (EKF) and the EKF-based Double Model Filter have certain disadvantages, such as sensitivity to modeling errors and difficulties in selection of initialization values. To solve the aforementioned problems, this paper presents a fault-related parameter adaptive estimation method based on strong tracking filter (STF) and Modified Bayes classification algorithm for fault detection and failure mode classification of the heat exchanger, respectively. Heat exchanger fault simulation is conducted to generate fault data, through which the proposed methods are validated. The results demonstrate that the proposed methods are capable of providing accurate, stable, and rapid fault diagnosis of the heat exchanger. PMID:25823010

  9. Hypothesis Testing and Decision Theoretic Approach for Fault Detection in Wireless Sensor Networks

    CERN Document Server

    Nandi, Mrinal; Roy, Bimal; Sarkar, Santanu

    2012-01-01

    Sensor networks aim at monitoring their surroundings for event detection and object tracking. But due to failure or death of sensors, false signal can be transmitted. In this paper, we consider the problem of fault detection in wireless sensor network (WSN), in particular, addressing both the noise-related measurement error and sensor fault simultaneously in fault detection. We assume that the sensors are placed at the center of a square (or hexagonal) cell in region of interest (ROI) and, if the event occurs, it occurs at a particular cell of the ROI. We propose fault detection schemes that take into account error probabilities into the optimal event detection process. We develop the schemes under the consideration of Neyman-Pearson test and Bayes test.

  10. A New Method for Node Fault Detection in Wireless Sensor Networks

    Directory of Open Access Journals (Sweden)

    Peng Jiang

    2009-02-01

    Full Text Available Wireless sensor networks (WSNs are an important tool for monitoring distributed remote environments. As one of the key technologies involved in WSNs, node fault detection is indispensable in most WSN applications. It is well known that the distributed fault detection (DFD scheme checks out the failed nodes by exchanging data and mutually testing among neighbor nodes in this network., but the fault detection accuracy of a DFD scheme would decrease rapidly when the number of neighbor nodes to be diagnosed is small and the node’s failure ratio is high. In this paper, an improved DFD scheme is proposed by defining new detection criteria. Simulation results demonstrate that the improved DFD scheme performs well in the above situation and can increase the fault detection accuracy greatly.

  11. Compound fault diagnosis of rotating machinery based on OVMD and a 1.5-dimension envelope spectrum

    Science.gov (United States)

    Yan, Xiaoan; Jia, Minping; Xiang, Ling

    2016-07-01

    Owing to the character of diversity and complexity, the compound fault diagnosis of rotating machinery under non-stationary operation has turned into a challenging task. In this paper, a novel method based on the optimal variational mode decomposition (OVMD) and 1.5-dimension envelope spectrum is proposed for detecting the compound faults of rotating machinery. In this method, compound fault signals are first decomposed by using OVMD containing optimal decomposition parameters, and several intrinsic mode components are obtained. Then, an adaptive selection method based on the weight factor (WF) is presented to choose two intrinsic mode components that contain the principal fault characteristic information. Finally, the 1.5-dimension envelope spectrum of the selected intrinsic mode components is utilized to extract the compound fault characteristic information of vibration signals. The performance of the proposed method is demonstrated by using the simulation signal and the experimental vibration signals collected from a rolling bearing and a gearbox with compound faults. The analysis results suggest that the proposed method is not only capable of detecting compound faults of a bearing and a gearbox, but can separate the characteristic signatures of compound faults. The research offers a new means for the compound fault diagnosis of rotating machinery.

  12. Fault Detection and Recovery in Wireless Sensor Network Using Clustering

    Directory of Open Access Journals (Sweden)

    Abolfazl Akbari

    2011-02-01

    Full Text Available Some WSN by a lot of immobile node and with the limited energy and without furthercharge of energy. Whereas extension of many sensor nodes and their operation. Hence it isnormal.unactive nodes miss their communication in network, hence split the network. For avoidance splitof network, we proposed a fault recovery corrupted node and Self Healing is necessary. In this Thesis, wedesign techniques to maintain the cluster structure in the event of failures caused by energy-drainednodes. Initially, node with the maximum residual energy in a cluster becomes cluster heed and node withthe second maximum residual energy becomes secondary cluster heed. Later on, selection of cluster heedand secondary cluster heed will be based on available residual energy. We use Matlab software assimulation platform quantities. like, energy consumption at cluster and number of clusters is computed inevaluation of proposed algorithm. Eventually we evaluated and compare this proposed method againstprevious method and we demonstrate our model is better optimization than other method such asVenkataraman, in energy consumption rate.

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

    DEFF Research Database (Denmark)

    Bergantino, Nicola; Caponetti, Fabio; Longhi, Sauro

    2009-01-01

    Efficient and reliable monitoring systems are mandatory to assure the required security standards in industrial complexes. This paper describes the recent developments of FaultBuster, a purely data-driven diagnostic system. It is designed so to be easily scalable to different monitor tasks...

  14. Soil-gas helium and surface-waves detection of fault zones in granitic bedrock

    Indian Academy of Sciences (India)

    G K Reddy; T Seshunarayana; Rajeev Menon; P Senthil Kumar

    2010-10-01

    Fracture and fault networks are conduits that facilitate groundwater movement in hard-rock terrains.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 resolution soil-gas samples were collected at every one meter at a depth of <1.5m along 100 m long profiles (3 in number).In addition,deep shear-wave images were also obtained using the multichannel analysis of surface waves.The study clearly indicates several soil-gas helium anomalies (above 200 ppb)along the pro files,where the shear-wave velocity images also show many near-surface vertical low velocity zones.We thus interpret that the soil-gas helium anomalous zones and the vertical low-velocity zones are probable traces of fault/fracture zones that could be efficient natural recharge zones and potential groundwater conduits.The result obtained from this study demonstrates the efficacy of an integrated approach of soil-gas helium and the seismic methods for mapping groundwater resource zones in granite/gneiss provinces.

  15. 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

  16. Distributed model-based nonlinear sensor fault diagnosis in wireless sensor networks

    Science.gov (United States)

    Lo, Chun; Lynch, Jerome P.; Liu, Mingyan

    2016-01-01

    Wireless sensors operating in harsh environments have the potential to be error-prone. This paper presents a distributive model-based diagnosis algorithm that identifies nonlinear sensor faults. The diagnosis algorithm has advantages over existing fault diagnosis methods such as centralized model-based and distributive model-free methods. An algorithm is presented for detecting common non-linearity faults without using reference sensors. The study introduces a model-based fault diagnosis framework that is implemented within a pair of wireless sensors. The detection of sensor nonlinearities is shown to be equivalent to solving the largest empty rectangle (LER) problem, given a set of features extracted from an analysis of sensor outputs. A low-complexity algorithm that gives an approximate solution to the LER problem is proposed for embedment in resource constrained wireless sensors. By solving the LER problem, sensors corrupted by non-linearity faults can be isolated and identified. Extensive analysis evaluates the performance of the proposed algorithm through simulation.

  17. Model-Based Fault Diagnosis Techniques Design Schemes, Algorithms and Tools

    CERN Document Server

    Ding, Steven X

    2013-01-01

    Guaranteeing a high system performance over a wide operating range is an important issue surrounding the design of automatic control systems with successively increasing complexity. As a key technology in the search for a solution, advanced fault detection and identification (FDI) is receiving considerable attention. This book introduces basic model-based FDI schemes, advanced analysis and design algorithms, and mathematical and control-theoretic tools. This second edition of Model-Based Fault Diagnosis Techniques contains: ·         new material on fault isolation and identification, and fault detection in feedback control loops; ·         extended and revised treatment of systematic threshold determination for systems with both deterministic unknown inputs and stochastic noises; addition of the continuously-stirred tank heater as a representative process-industrial benchmark; and ·         enhanced discussion of residual evaluation in stochastic processes. Model-based Fault Diagno...

  18. Framework for the Design and Implementation of Fault Detection and Isolation Project

    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 Project

    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 Modeling and Testing for Analog Circuits in Complex Space Based on Supply Current and Output Voltage

    Directory of Open Access Journals (Sweden)

    Hongzhi Hu

    2015-01-01

    Full Text Available This paper deals with the modeling of fault for analog circuits. A two-dimensional (2D fault model is first proposed based on collaborative analysis of supply current and output voltage. This model is a family of circle loci on the complex plane, and it simplifies greatly the algorithms for test point selection and potential fault simulations, which are primary difficulties in fault diagnosis of analog circuits. Furthermore, in order to reduce the difficulty of fault location, an improved fault model in three-dimensional (3D complex space is proposed, which achieves a far better fault detection ratio (FDR against measurement error and parametric tolerance. To address the problem of fault masking in both 2D and 3D fault models, this paper proposes an effective design for testability (DFT method. By adding redundant bypassing-components in the circuit under test (CUT, this method achieves excellent fault isolation ratio (FIR in ambiguity group isolation. The efficacy of the proposed model and testing method is validated through experimental results provided in this paper.

  1. Rule-based fault-tolerant flight control

    Science.gov (United States)

    Handelman, Dave

    1988-01-01

    Fault tolerance has always been a desirable characteristic of aircraft. The ability to withstand unexpected changes in aircraft configuration has a direct impact on the ability to complete a mission effectively and safely. The possible synergistic effects of combining techniques of modern control theory, statistical hypothesis testing, and artificial intelligence in the attempt to provide failure accommodation for aircraft are investigated. This effort has resulted in the definition of a theory for rule based control and a system for development of such a rule based controller. Although presented here in response to the goal of aircraft fault tolerance, the rule based control technique is applicable to a wide range of complex control problems.

  2. 基于动态PCA的核动力装置传感器故障检测%Sensor Fault Detection for Nuclear Power Plant Based on Dynamic Principal Component Analysis

    Institute of Scientific and Technical Information of China (English)

    宋梅村; 蔡琦

    2012-01-01

    As to the maladjustment of model of traditional principal component analysis in changing condition process, different principal component models have been built by dynamic principal component analysis according to condition type, through stability factor analysis to eliminate the changing process data and condition classification of different steady conditions with the fuzzy-clustering method. This method is applied to sensor fault detection for nuclear power plant . The result shows that it is fit for sensor fault detection in changing condition process,it reduces the chances of detection mistakes and it improves the detection sensitivity.%针对变工况过程中传统主元分析方法的模型不适应问题,通过稳定性因子分析,剔除过渡过程数据,并用模糊聚类方法将不同稳态工况进行分类,利用动态主元模型方法根据工况类型建立不同的主元模型,并将该方法用于核动力装置传感器的故障检测,结果表明该方法能够适应变工况情况下的传感器故障检测,减少了故障的误检,并提高了检测灵敏度.

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

    OpenAIRE

    Zhentao Wang; Arne Wahrburg; Stephan Rinderknecht

    2012-01-01

    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 ro...

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

    International Nuclear Information System (INIS)

    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

  5. 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.

  6. A Model of Intelligent Fault Diagnosis of Power Equipment Based on CBR

    Directory of Open Access Journals (Sweden)

    Gang Ma

    2015-01-01

    Full Text Available Nowadays the demand of power supply reliability has been strongly increased as the development within power industry grows rapidly. Nevertheless such large demand requires substantial power grid to sustain. Therefore power equipment’s running and testing data which contains vast information underpins online monitoring and fault diagnosis to finally achieve state maintenance. In this paper, an intelligent fault diagnosis model for power equipment based on case-based reasoning (IFDCBR will be proposed. The model intends to discover the potential rules of equipment fault by data mining. The intelligent model constructs a condition case base of equipment by analyzing the following four categories of data: online recording data, history data, basic test data, and environmental data. SVM regression analysis was also applied in mining the case base so as to further establish the equipment condition fingerprint. The running data of equipment can be diagnosed by such condition fingerprint to detect whether there is a fault or not. Finally, this paper verifies the intelligent model and three-ratio method based on a set of practical data. The resulting research demonstrates that this intelligent model is more effective and accurate in fault diagnosis.

  7. Current Sensor Fault Diagnosis Based on a Sliding Mode Observer for PMSM Driven Systems

    Directory of Open Access Journals (Sweden)

    Gang Huang

    2015-05-01

    Full Text Available This paper proposes a current sensor fault detection method based on a sliding mode observer for the torque closed-loop control system of interior permanent magnet synchronous motors. First, a sliding mode observer based on the extended flux linkage is built to simplify the motor model, which effectively eliminates the phenomenon of salient poles and the dependence on the direct axis inductance parameter, and can also be used for real-time calculation of feedback torque. Then a sliding mode current observer is constructed in αβ coordinates to generate the fault residuals of the phase current sensors. The method can accurately identify abrupt gain faults and slow-variation offset faults in real time in faulty sensors, and the generated residuals of the designed fault detection system are not affected by the unknown input, the structure of the observer, and the theoretical derivation and the stability proof process are concise and simple. The RT-LAB real-time simulation is used to build a simulation model of the hardware in the loop. The simulation and experimental results demonstrate the feasibility and effectiveness of the proposed method.

  8. Complex faulting in the Quetta Syntaxis: fault source modeling of the October 28, 2008 earthquake sequence in Baluchistan, Pakistan, based on ALOS/PALSAR InSAR data

    Science.gov (United States)

    Usman, Muhammad; Furuya, Masato

    2015-09-01

    The Quetta Syntaxis in western Baluchistan, Pakistan, is the result of an oroclinal bend of the western mountain belt and serves as a junction for different faults. As this area also lies close to the left-lateral strike-slip Chaman fault, which marks the boundary between the Indian and Eurasian plates, the resulting seismological behavior of this regime is very complex. In the region of the Quetta Syntaxis, close to the fold and thrust belt of the Sulaiman and Kirthar Ranges, an earthquake with a magnitude of 6.4 (Mw) occurred on October 28, 2008, which was followed by a doublet on the very next day. Six more shocks associated with these major events then occurred (one foreshock and five aftershocks), with moment magnitudes greater than 4. Numerous researchers have tried to explain the source of this sequence based on seismological, GPS, and Environmental Satellite (ENVISAT)/Advanced Synthetic Aperture Radar (ASAR) data. Here, we used Advanced Land Observing Satellite (ALOS)/Phased Array-type L-band Synthetic Aperture Radar (PALSAR) InSAR data sets from both ascending and descending orbits that allow us to more completely detect the deformation signals around the epicentral region. The results indicated that the shock sequence can be explained by two right-lateral and two left-lateral strike-slip faults that also included reverse slip. The right-lateral faults have a curved geometry. Moreover, whereas previous studies have explained the aftershock crustal deformation with a different fault source, we found that the same left-lateral segment of the conjugate fault was responsible for the aftershocks. We thus confirmed the complex surface deformation signals from the moderate-sized earthquake. Intra-plate crustal bending and shortening often seem to be accommodated as conjugate faulting, without any single preferred fault orientation. We also detected two possible landslide areas along with the crustal deformation pattern.

  9. Adaptive FTC based on Control Allocation and Fault Accommodation for Satellite Reaction Wheels

    DEFF Research Database (Denmark)

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

    2016-01-01

    This paper proposes an active fault tolerant control scheme to cope with faults or failures affecting the flywheel spin rate sensors or satellite reaction wheel motors. The active fault tolerant control system consists of a fault detection and diagnosis module along with a control allocation...... estimation filters, which do not need a priori information about the internal model of the signal to be estimated. The adaptive control allocation and sensor fault accommodation can handle both temporal faults and failures. Simulation results illustrate the convincing fault correction and attitude control...

  10. Design of fault diagnosis system for inertial navigation system based on virtual technology

    Science.gov (United States)

    Hu, Baiqing; Wang, Boxiong; Li, An; Zhang, Mingzhao; Qin, Fangjun; Pan, Hua

    2006-11-01

    With regard to the complex structure of the inertial navigation system and the low rate of fault detection with BITE (built-in test equipment), a fault diagnosis system for INS based on virtual technologies (virtual instrument and virtual equipment) is proposed in this paper. The hardware of the system is a PXI computer with highly stable performance and strong extensibility. In addition to the basic functions of digital multimeter, oscilloscope and cymometer, it can also measure the attitude of the ship in real-time, connect and control the measurement instruments with digital interface. The software is designed with the languages of Measurement Studio for VB, JAVA, and CULT3D. Using the extensively applied fault-tree reasoning and fault cases makes fault diagnosis. To suit the system to the diagnosis for various navigation electronic equipments, the modular design concept is adopted for the software programming. Knowledge of the expert system is digitally processed and the parameters of the system's interface and the expert diagnosis knowledge are stored in the database. The application shows that system is stable in operation, easy to use, quick and accurate in fault diagnosis.

  11. Induction motor rotor fault diagnosis method based on double PQ transformation

    Institute of Scientific and Technical Information of China (English)

    HUANG Jin; NIU Faliang; YANG Jiaqiang

    2007-01-01

    This Paper presents a new rotor fault diagnosis method for induction motors which is based on the double PQ transformation.We construct the PQ transformation matrix with the positive sequence fundamental voltage components and their Hilbert transformation as elements.The active power P and the reactive power Q are obtained through the PO transformation of the stator currents.As both P and Q are constant for a healthy motor,they are represented by a dot on the PQ plane.Whereas the P and Q for a rotor broken bar motor are represented by an ellipse because they comprise an additional frequency component 2sfs (s is the slip and js is the supply frequency).Thus,by distinguishing these two different patterns.the rotor broken bar fault is detected.We use the major radius of the ellipse as the fault indicator and the distance between the point of no-load condition and the center of the ellipse on the PQ plane as its normalization value.We thus arrive at the fault severity factor which is fairly independent of the load level and the inertia value of the induction motors.Experimental results have demonstrated that the proposed method is effective in identifying the rotor-broken-bars fault and at determining the severity of the fault.

  12. Wavelet neural network based fault diagnosis in nonlinear analog circuits

    Institute of Scientific and Technical Information of China (English)

    Yin Shirong; Chen Guangju; Xie Yongle

    2006-01-01

    The theories of diagnosing nonlinear analog circuits by means of the transient response testing are studied. Wavelet analysis is made to extract the transient response signature of nonlinear circuits and compress the signature dada. The best wavelet function is selected based on the between-category total scatter of signature. The fault dictionary of nonlinear circuits is constructed based on improved back-propagation(BP) neural network. Experimental results demonstrate that the method proposed has high diagnostic sensitivity and fast fault identification and deducibility.

  13. Design of sensor and actuator multi model fault detection and isolation system using state space neural networks

    Science.gov (United States)

    Czajkowski, Andrzej

    2015-11-01

    This paper deals with the application of state space neural network model to design a Fault Detection and Isolation diagnostic system. The work describes approach based on multimodel solution where the SIMO process is decomposed into simple models (SISO and MISO). With such models it is possible to generate different residual signals which later can be evaluated with simple thresholding method into diagnostic signals. Further, such diagnostic signals with the application of Binary Diagnostic Table (BDT) can be used to fault isolation. All data used in experiments is obtain from the simulator of the real-time laboratory stand of Modular Servo under Matlab/Simulink environment.

  14. 基于残余动量的两连杆柔性臂驱动器故障检测%Based on the Residual Momentum of Driver Fault Detection for Two-link Flexible Manipulator

    Institute of Scientific and Technical Information of China (English)

    邵丹璐; 王斌锐; 金英连

    2015-01-01

    For two-linkflexible manipulators in the vertical plane,the deadweight of arms and the weight of end loads are comprehensively considered,the dynamics model is established based on the lagrange equations and assumed modes method. And based on the derivative of momentum,residual momentum operatoris designed,and the influence of flexibility deformation on the rotational angle in the calculation of residual momentum is taken into account. Using flexible hinges,the simulation platform is set up in Matlab/ Simulink. Aiming at simulation of manipulators swing under the drives are trouble-free,drives have a single fault and drives have multiple faults,then give the change curves of corresponding residual momentum respectively. The results show that the residual momentum operator model established could be applied to rigid arms and flexible arms. When drives are trouble-free,the residual momentum is approximate to 0. When drives have faults, the size and trend of residual momentum reflect the size and trend of input torque when there is no fault approximately. The driver fault mostly affects the link directly driven by the driver with fault.%综合考虑柔性臂质量与末端负载质量,采用 Largrange 方程和假设模态法,建立两连杆柔性臂动力学方程。基于动量导数,设计了残余动量算子。利用 Matlab/ Simulink 中的弹性铰链搭建了柔性臂仿真平台;对驱动器无故障、单个故障及多个故障进行了仿真,对比分析了刚性臂和柔性臂相应残余动量的变化曲线。结果表明,残余动量适用于柔性臂;驱动器无故障时,残余动量近似为0;有故障时,残余动量变化曲线能反应出故障时间内驱动器力矩应有的大小和趋势,且对直接驱动连杆的残余动量影响最大。

  15. Hybrid Support Vector Machines-Based Multi-fault Classification

    Institute of Scientific and Technical Information of China (English)

    GAO Guo-hua; ZHANG Yong-zhong; ZHU Yu; DUAN Guang-huang

    2007-01-01

    Support Vector Machines (SVM) is a new general machine-learning tool based on structural risk minimization principle. This characteristic is very signific ant for the fault diagnostics when the number of fault samples is limited. Considering that SVM theory is originally designed for a two-class classification, a hybrid SVM scheme is proposed for multi-fault classification of rotating machinery in our paper. Two SVM strategies, 1-v-1 (one versus one) and 1-v-r (one versus rest), are respectively adopted at different classification levels. At the parallel classification level, using 1-v-1 strategy, the fault features extracted by various signal analysis methods are transferred into the multiple parallel SVM and the local classification results are obtained. At the serial classification level, these local results values are fused by one serial SVM based on 1-v-r strategy. The hybrid SVM scheme introduced in our paper not only generalizes the performance of signal binary SVMs but improves the precision and reliability of the fault classification results. The actually testing results show the availability suitability of this new method.

  16. 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...

  17. Development and Test of Methods for Fault Detection and Isolation

    DEFF Research Database (Denmark)

    Jørgensen, R.B.

    Almost all industrial systemns are automated to ensure optimal production both in relation to energy consumtion and safety to equipment and humans. All working parts are individually subject to faults. This can lead to unacceptable economic loss or injury to people. This thesis deals with a...

  18. Fault detection method based on sparse non-negative matrix factorization%基于稀疏性非负矩阵分解的故障监测方法

    Institute of Scientific and Technical Information of China (English)

    王帆; 杨雅伟; 谭帅; 侍洪波

    2015-01-01

    In this paper, a novel fault detection method based on sparse non-negative matrix factorization (SNMF) is proposed. NMF (non-negative matrix factorization) is a new dimension reduction technique that can find a low-rank matrix approximation from the original data. In contrast to the conventional multivariate statistical process monitoring methods, for example PCA, NMF has no assumption about the nature of latent variables, except for non-negativity. Combining linear sparse coding and NMF, SNMF can learn much sparser representation via imposing sparseness constraints. During factorization, low-rank matrix is orthogonalized to remove redundant information and concentrate information on fewer directions of projection. Then, SNMF is used to extract the latent variables that drive a process and new statistical metrics are defined for fault detection. Kernel density estimation (KDE) is adopted to calculate the confidence limits of defined statistical metrics. Afterwards, the proposed method is applied to the Tennessee Eastman process to evaluate the monitoring performance, comparing with conventional NMF and PCA. The results from the experiment show the feasibility of the new method.%提出了基于稀疏性非负矩阵分解(SNMF)的故障监测方法。非负矩阵分解(NMF)是一种新的降维方法,可以得到原始数据的低秩近似矩阵。与传统的多元统计过程监控方法如主成分分析(PCA)相比,NMF对潜变量的性质没有假设,除了非负性的要求。将稀疏编码和非负矩阵分解方法结合在一起,因为施加了稀疏性的约束,稀疏性非负矩阵分解方法可以得到对数据更稀疏的表示。在分解时对低秩近似矩阵进行正交化处理,从而在降维时除去变量中的冗余信息,将信息集中到更少的投影方向上。然后,用SNMF方法来提取过程的潜变量,并定义新的监测指标来进行故障监测。使用核密度估计(KDE)方法来计算新

  19. 基于UKF的水下机器人执行器故障检测方法研究%Research on actuator fault detection of unmanned underwater vehicle based on unscented kalman filter

    Institute of Scientific and Technical Information of China (English)

    林昌龙; 封锡盛; 李一平

    2011-01-01

    水下机器人需要在无人干预或者少量干预的情况下自主地完成使命,这就要求它在作业过程中能够自主地检测子系统、传感器和执行器的故障.在确认发生故障的时候,它还需要尽最大可能地进行修复,以确保使命的顺利进行.水下机器人常用的执行器包括推进器、舵、机械手等,针对推进器的故障检测问题,首先建立了水下机器人的运动学和动力学模型,然后采用UKF对系统的状态和参数进行联合估计,最后通过外场试验验证了算法的有效性和正确性.%Unmanned underwater vehicle needs to complete the mission with little or even without human intervention.Thus,it should be able to detect the faults of its subsystems,sensors, and actuators during the mission.Furthermore,when identifying any fault,it should try to fix the problemto resume the mission.Generally,the actuators for unmanned underwater vehicles consist of thrusters,undders,manipulators and so on.Concerning the problem of actuator faults detecting,the kinematical and kinetic models of an unmanned underwater vehicle are firstly constructed it.Then using an Unscented Kalman Filter,the status and parameters of the system are estimated.Finally,field experiment is carried out and the result validates the effectiveness and correctness of this algorithm.

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

    Science.gov (United States)

    Yuan, Kaijuan; Xiao, Fuyuan; Fei, Liguo; Kang, Bingyi; Deng, Yong

    2016-01-18

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

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

    Science.gov (United States)

    Yuan, Kaijuan; Xiao, Fuyuan; Fei, Liguo; Kang, Bingyi; Deng, Yong

    2016-01-01

    Sensor data fusion plays an important role in fault diagnosis. Dempster-Shafer (D-R) evidence theory is widely used in fault diagnosis, since it is efficient to combine evidence from different sensors. However, under the situation where the evidence highly conflicts, it may obtain a counterintuitive result. To address the issue, a new method is proposed in this paper. Not only the statistic sensor reliability, but also the dynamic sensor reliability are taken into consideration. The evidence distance function and the belief entropy are combined to obtain the dynamic reliability of each sensor report. A weighted averaging method is adopted to modify the conflict evidence by assigning different weights to evidence according to sensor reliability. The proposed method has better performance in conflict management and fault diagnosis due to the fact that the information volume of each sensor report is taken into consideration. An application in fault diagnosis based on sensor fusion is illustrated to show the efficiency of the proposed method. The results show that the proposed method improves the accuracy of fault diagnosis from 81.19% to 89.48% compared to the existing methods. PMID:26797611

  2. Automatic software fault localization based on ar tificial bee colony

    Institute of Scientific and Technical Information of China (English)

    Linzhi Huang∗; Jun Ai

    2015-01-01

    Software debugging accounts for a vast majority of the financial and time costs in software developing and maintenance. Thus, approaches of software fault localization that can help au-tomate the debugging process have become a hot topic in the field of software engineering. Given the great demand for software fault localization, an approach based on the artificial bee colony (ABC) algorithm is proposed to be integrated with other related techniques. In this process, the source program is initial y instru-mented after analyzing the dependence information. The test case sets are then compiled and run on the instrumented program, and execution results are input to the ABC algorithm. The algorithm can determine the largest fitness value and best food source by calculating the average fitness of the employed bees in the iter-ative process. The program unit with the highest suspicion score corresponding to the best test case set is regarded as the final fault localization. Experiments are conducted with the TCAS program in the Siemens suite. Results demonstrate that the proposed fault localization method is effective and efficient. The ABC algorithm can efficiently avoid the local optimum, and ensure the validity of the fault location to a larger extent.

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

    Science.gov (United States)

    Yuan, Kaijuan; Xiao, Fuyuan; Fei, Liguo; Kang, Bingyi; Deng, Yong

    2016-01-01

    Sensor data fusion plays an important role in fault diagnosis. Dempster–Shafer (D-R) evidence theory is widely used in fault diagnosis, since it is efficient to combine evidence from different sensors. However, under the situation where the evidence highly conflicts, it may obtain a counterintuitive result. To address the issue, a new method is proposed in this paper. Not only the statistic sensor reliability, but also the dynamic sensor reliability are taken into consideration. The evidence distance function and the belief entropy are combined to obtain the dynamic reliability of each sensor report. A weighted averaging method is adopted to modify the conflict evidence by assigning different weights to evidence according to sensor reliability. The proposed method has better performance in conflict management and fault diagnosis due to the fact that the information volume of each sensor report is taken into consideration. An application in fault diagnosis based on sensor fusion is illustrated to show the efficiency of the proposed method. The results show that the proposed method improves the accuracy of fault diagnosis from 81.19% to 89.48% compared to the existing methods. PMID:26797611

  4. Investigation of Sumatra fault based on magnetotelluric and GPS measurements

    International Nuclear Information System (INIS)

    Complete text of publication follows. On 26 December 2004, a large earthquake of Mw=9 occurred in the Sumatra area located in the western part of subduction zone, Indonesia. The Sumatran subduction is where the convergence between the Indo-Australian plate and the Sundaland plate occur at 4-5 cm/yr. Several multidisciplinary studies have been involved to investigate the cause of the earthquake such as seismic and geodynamic studies. Through research collaboration among Nagoya University, Tokyo Institute of Technology, Bandung Institute of Technology and Syiah Kuala University, We were carried out Magnetotelluric and GPS surveys. Magnetotelluric measurement was done at 12 sites crossing the Sumatra fault and Seulimeum fault . Two components of horizontal electric field, two components of the horizontal magnetic field and one component of the vertical magnetic field were used in this survey. In this profile, we have carried out MT measurement along a 65 km long SW-NE profile. MT data have been modeled using two dimensional inversion including sea to became reality. The 2D final model has shown an extreme resistivity value has been found around the fault at a depth of 5 km from the surface. Western part of Sumatra fault is characterized by low resistivity. In contrast just beneath the Sumatra fault characterized by high resistivity. By comparison to GPS result, there is a good agreement between resistivity structure and relative motion based on GPS measurement.

  5. Development Ground Fault Detecting System for D.C Voltage Line

    Energy Technology Data Exchange (ETDEWEB)

    Kim Taek Soo; Song Ung Il; Gwon, Young Dong; Lee Hyoung Kee [Korea Electric Power Research Institute, Taejon (Korea, Republic of)

    1996-12-31

    It is necessary to keep the security of reliability and to maximize the efficiency of maintenance by prompt detection of a D.C feeder ground fault point at the built ed or a building power plants. At present, the most of the power plants are set up the ground fault indicator lamp in the monitor room. If a ground fault occurs on DC voltage feeder, a current through the ground fault relay is adjusted and the lamps have brightened while the current flows the relay coil. In order to develop such a system, it is analyzed a D.C feeder ground circuit theoretically and studied a principles which can determine ground fault point or a polarity discrimination and a phase discrimination of the line. So, the developed system through this principles can compute a resistance ground fault current and a capacitive ground fault current. It shows that the system can defect a ground fault point or a bad insulated line by measuring a power plant D.C feeder insulation resistance at the un interruptible power status, and therefore the power plant could protect an unexpected service interruption . (author). 18 refs., figs.

  6. Bayes-Based Fault Discrimination in Wide Area Backup Protection

    Directory of Open Access Journals (Sweden)

    WANG, Z.

    2012-02-01

    Full Text Available Multivariate statistical analysis is an effective tool to finish the fault location for electric power system. In Bayesian discriminant analysis as a subbranch, by the research of several populations, one can calculate the conditional probability that some samples belong to these populations, and compare the corresponding probability. The sample will be classified as population with maximum probability. In this paper, based on Bayesian discriminant analysis principle, a great number of simulation examples have confirmed that the results of Bayesian fault discriminant in wide area backup protection are accurate and reliable.

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

    Directory of Open Access Journals (Sweden)

    Andres Bustillo

    2011-03-01

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

  8. A Cooperation-Based Fault Management Method for Satellite Networks

    Directory of Open Access Journals (Sweden)

    Wenbo Zhang

    2012-07-01

    Full Text Available In order to efficiently diagnose the satellite network, a three level management architecture was proposed and a cooperated-based fault management method was put forward. In this method the traditional fault management method was used through network management technique when a satellite agent could respond to the network management instruction received from the management station. However, if the satellite agent could not respond to the network management demands, the intra-domain cooperation or inter-domain cooperation would be activated. The suspected fault satellite could be tested through cooperation among the satellite agents. The simulation results shows that in the circumstance of the low faulty frequency, the new method could be effectively used in satellite network with short cooperative time and low throughput.

  9. TRSTR: A Fault- Tolerant Microprocessor Architecture Based on SMT

    Institute of Scientific and Technical Information of China (English)

    YANG Hua; CUI Gang; YANG Xiao-zong

    2005-01-01

    Based on Simultaneous Multithreading (SMT),we propose a fault-tolerant scheme called Tri-modular Redundantly and Simultaneously Threaded processor with Recovery (TRSTR). TRSTR features as following: First, we introduce an arbitrator context into the conventional SRT (Simultaneous and Redundantly Threaded), which acts as an arbitrator when results from the other two contexts disagree, or acts as an ordinary thread generally, thus making full use of SMT' s parallelism. Second, we append reconfigurable feature to sphere of replication in SRT, making it more flexible for changing demands and situations. Third, TRSTR has two working modes: Tri-Simultaneous with Voting (TSV) and Dual-Simultaneous with Arbitrator (DSA), which can switch at will. Finally, in addition to transient-fault coverage,TRSTR has on-line self-checking and self-recovering abilities,so as to shield off some permanent faults and reconfigure itself without stopping the crucial job, improving its reliability and availability.

  10. Fault Detection in the Blade and Pitch System of a Wind Turbine with H2 PI Observers

    Science.gov (United States)

    Sales-Setién, Ester; Peñarrocha, Ignacio; Dolz, Daniel; Sanchis, Roberto

    2015-11-01

    In this work, we present a fault detection strategy applicable to the blade and pitch system in offshore wind turbines. First, we model the system and possible faults and propose a PI observer to identify the faults. Then, the observer is designed accounting the sensors measurement noise, and addressing a trade off between the needs of false alarm rate, minimum detectable fault and detection time. By means of a well known benchmark, several simulations show the goodness of the approach and its flexibility to explicitly fix the fault detector performance.

  11. 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.

  12. Detection and location of electrical insulation faults on the LHC superconducting circuits during hardware commissioning

    CERN Document Server

    Bozzini, D; Mess, K H; Russenschuck, Stephan

    2008-01-01

    As part of the electrical quality assurance program (ELQA), the insulation of all superconducting circuits of the LHC has to be tested with a d.c. voltage of up to 1.9 kV. Fault location within a ± 3 m range over the total length of 2700 m has been achieved in order to limit the number of interconnection openings for repair. In this paper, the methods, tooling, and procedures for the detection and location of electrical faults will be presented in view of the practical experience gained in the LHC tunnel. Three particular cases of localized faults during LHC hardware commissioning will be discussed.

  13. Sensor Fault Detection and Diagnosis for autonomous vehicles

    OpenAIRE

    Realpe Miguel; Vintimilla Boris; Vlacic Ljubo

    2015-01-01

    In recent years testing autonomous vehicles on public roads has become a reality. However, before having autonomous vehicles completely accepted on the roads, they have to demonstrate safe operation and reliable interaction with other traffic participants. Furthermore, in real situations and long term operation, there is always the possibility that diverse components may fail. This paper deals with possible sensor faults by defining a federated sensor data fusion architecture. The proposed ar...

  14. An Imperfect-debugging Fault-detection Dependent-parameter Software

    Institute of Scientific and Technical Information of China (English)

    2007-01-01

    Software reliability growth models (SRGMs) incorporating the imperfect debugging and learning phenomenon of developers have recently been developed by many researchers to estimate software reliability measures such as the number of remaining faults and software reliability. However, the model parameters of both the fault content rate function and fault detection rate function of the SRGMs are often considered to be independent from each other. In practice, this assumption may not be the case and it is worth to investigate what if it is not. In this paper, we aim for such study and propose a software reliability model connecting the imperfect debugging and learning phenomenon by a common parameter among the two functions, called the imperfect-debugging fault-detection dependent-parameter model. Software testing data collected from real applications are utilized to illustrate the proposed model for both the descriptive and predictive power by determining the non-zero initial debugging process.

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

    Energy Technology Data Exchange (ETDEWEB)

    Hu, Q.P. [Quality and Innovation Research Centre, Department of Industrial and Systems Engineering, National University of Singapore, Singapore 119260 (Singapore)]. E-mail: g0305835@nus.edu.sg; Xie, M. [Quality and Innovation Research Centre, Department of Industrial and Systems Engineering, National University of Singapore, Singapore 119260 (Singapore)]. E-mail: mxie@nus.edu.sg; Ng, S.H. [Quality and Innovation Research Centre, Department of Industrial and Systems Engineering, National University of Singapore, Singapore 119260 (Singapore)]. E-mail: isensh@nus.edu.sg; Levitin, G. [Israel Electric Corporation, Reliability and Equipment Department, R and D Division, Aaifa 31000 (Israel)]. E-mail: levitin@iec.co.il

    2007-03-15

    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.

  16. Detection and Diagnosis of Gear Fault By the Single Gear Tooth Analysis Technique

    Institute of Scientific and Technical Information of China (English)

    MENG Tao; LIAO Ming-fu

    2003-01-01

    This paper presents a procedure of single gear tooth analysis for early detection and diagnosis of gear faults. The objective of this procedure is to develop a method for more sensitive detection of the incipient faults and locating the faults in the gear. The main idea of the single gear tooth analysis is that the vibration signals collected with a high sampling rate are divided into a number of segments with the same time interval. The number of signal segments is equal to that of the gear teeth. The analysis of individual segments reveals more sensitively the changes of the vibration signals in both time and frequency domain caused by gear faults. In addition, the location of a failed tooth can be indicated in terms of the position of the segment that deviates from the normal segments. An experimental investigation verified the advantages of the single gear tooth analysis.

  17. A robust model-based approach to diagnosing faults in air-handling units

    Energy Technology Data Exchange (ETDEWEB)

    Ngo, D.; Dexter, A.L.

    1999-07-01

    This paper describes the development of a robust model-based approach to diagnosing faults in air-handling units that avoids false alarms caused by sensor bias but does not require application-dependent thresholds to be selected. The diagnosis is based on a semi-qualitative analysis of the measured data using generic fuzzy reference models to describe the behavior of the equipment, with and without faults. The scheme is applied to the cooling-coil subsystem of an air-handling unit, and the sensitivity of the diagnosis to sensor bias and fault size is examined. The results of the diagnosis are compared to those obtained using reference models that describe the behavior of a specific design. The scheme is also used to commission the cooling-coil subsystem of an air-handling unit in an office building. Results are presented that demonstrate the proposed scheme does not generate false alarms in practice. It is concluded that the accuracy of sensors currently used means it is likely that only large faults can be detected in practice and that more accurate measurements are required if a higher level of fault sensitivity is needed.

  18. Simulation and fault-detection of a pressure control servosystem in a Boiling Water Reactor

    International Nuclear Information System (INIS)

    This master thesis describes a Simnon model of a boiling water reactor to be used in simulating faults and disturbances. These faults and disturbanses will be detected by noise analysis. Some methods in identification and noise analysis are also described and are applied on some malfunctions of a servo. A Pascal program for recursive parameter identification was also written and tested. This program is to be used in an expert system for noise analysis on the nuclear power plant Barsebaeck. (author)

  19. [Early warning for various internal faults of GIS based on ultraviolet spectroscopy].

    Science.gov (United States)

    Zhao, Yu; Wang, Xian-pei; Hu, Hong-hong; Dai, Dang-dang; Long, Jia-chuan; Tian, Meng; Zhu, Guo-wei; Huang, Yun-guang

    2015-02-01

    As the basis of accurate diagnosis, fault early-warning of gas insulation switchgear (GIS) focuses on the time-effectiveness and the applicability. It would be significant to research the method of unified early-warning for partial discharge (PD) and overheated faults in GIS. In the present paper, SO2 is proposed as the common and typical by-product. The unified monitoring could be achieved through ultraviolet spectroscopy (UV) detection of SO2. The derivative method and Savitzky-Golay filtering are employed for baseline correction and smoothing. The wavelength range of 290-310 nm is selected for quantitative detection of SO2. Through UV method, the spectral interference of SF6 and other complex by-products, e.g., SOF2 and SOF2, can be avoided and the features of trace SO2 in GIS can be extracted. The detection system is featured by compacted structure, low maintenance and satisfactory suitability in filed surveillance. By conducting SF6 decomposition experiments, including two types of PD faults and the overheated faults between 200-400 degrees C, the feasibility of proposed UV method has been verified. Fourier transform infrared spectroscopy and gas chromatography methods can be used for subsequent fault diagnosis. The different decomposition features in two kinds of faults are confirmed and the diagnosis strategy has been briefly analyzed. The main by-products under PD are SOF2 and SO2F2. The generated SO2 is significantly less than SOF2. More carbonous by-products will be generated when PD involves epoxy. By contrast, when the material of heater is stainless steel, SF6 decomposes at about 300 "C and the main by-products in overheated faults are SO2 and SO2F2. When heated over 350 degrees C, SO2 is generated much faster. SOz content stably increases when the GIS fault lasts. The faults types could be preliminarily identified based on the generation features of SO2. PMID:25970908

  20. A hybrid fault detection and isolation strategy for a team of cooperating unmanned vehicles

    Science.gov (United States)

    Tousi, M. M.; Khorasani, K.

    2015-01-01

    In this paper, a hybrid fault detection and isolation (FDI) methodology is developed for a team of cooperating unmanned vehicles. The proposed approach takes advantage of the cooperative nature of the team to detect and isolate relatively low-severity actuator faults that are otherwise not detectable and isolable by the vehicles themselves individually. The approach is hybrid and consists of both low-level (agent/team level) and high-level [discrete-event systems (DES) level] FDI modules. The high-level FDI module is formulated in the DES supervisory control framework, whereas the low-level FDI module invokes classical FDI techniques. By properly integrating the two FDI modules, a larger class of faults can be detected and isolated as compared to the existing techniques in the literature that rely on each level separately. Simulation results for a team of five unmanned aerial vehicles are also presented to demonstrate the effectiveness and capabilities of our proposed methodology.