Journal articles on the topic 'Fault detection/estimation'
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Zhang, Chuang, Xiubin Zhao, Chunlei Pang, Yong Wang, Liang Zhang, and Bo Feng. "Improved Fault Detection Method Based on Robust Estimation and Sliding Window Test for INS/GNSS Integration." Journal of Navigation 73, no. 4 (February 28, 2020): 776–96. http://dx.doi.org/10.1017/s0373463319000778.
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Real-time and accurate fault detection and isolation is very important to ensure the reliability and precision of integrated inertial navigation and global navigation satellite systems. In this paper, the detection performance of a residual chi-square method is analysed, and on this basis an improved method of fault detection is proposed. The local test based on a standardised residual is introduced to detect and identify faulty measurements directly. Differing from the traditional method, two appropriate thresholds are selected to calculate the weight factor of each measurement, and the gain matrix is adjusted adaptively to reduce the influence of the undetected faulty measurement. The sliding window test, which uses past measurements, is also added to further improve the fault detection performance for small faults when the local test based on current measurements cannot judge whether a fault has occurred or not. Several simulations are conducted to evaluate the proposed method. The results show that the improved method has better fault detection performance than the traditional detection method, especially for small faults, and can improve the reliability and precision of the navigation system effectively.
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Mharakurwa, Edwell T., G. N. Nyakoe, and A. O. Akumu. "Power Transformer Fault Severity Estimation Based on Dissolved Gas Analysis and Energy of Fault Formation Technique." Journal of Electrical and Computer Engineering 2019 (February 3, 2019): 1–10. http://dx.doi.org/10.1155/2019/9674054.
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Decision making on transformer insulation condition based on the evaluated incipient faults and aging stresses has been the norm for many asset managers. Despite being the extensively applied methodology in power transformer incipient fault detection, solely dissolved gas analysis (DGA) techniques cannot quantify the detected fault severity. Fault severity is the core property in transformer maintenance rankings. This paper presents a fuzzy logic methodology in determining transformer faults and severity through use of energy of fault formation of the evolved gasses during transformer faulting event. Additionally, the energy of fault formation is a temperature-dependent factor for all the associated evolved gases. Instead of using the energy-weighted DGA, the calculated total energy of related incipient fault is used for severity determination. Severity of faults detected by fuzzy logic-based key gas method is evaluated through the use of collected data from several in-service and faulty transformers. DGA results of oil samples drawn from transformers of different specifications and age are used to validate the model. Model results show that correctly detecting fault type and its severity determination based on total energy released during faults can enhance decision-making in prioritizing maintenance of faulty transformers.
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Hajiyev, Chingiz, Demet Cilden-Guler, and Ulviye Hacizade. "Two-Stage Kalman Filter for Fault Tolerant Estimation of Wind Speed and UAV Flight Parameters." Measurement Science Review 20, no. 1 (February 1, 2020): 35–42. http://dx.doi.org/10.2478/msr-2020-0005.
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AbstractIn this study, an estimation algorithm based on a two-stage Kalman filter (TSKF) was developed for wind speed and Unmanned Aerial Vehicle (UAV) motion parameters. In the first stage, the wind speed estimation algorithm is used with the help of the Global Positioning System (GPS) and dynamic pressure measurements. Extended Kalman Filter (EKF) is applied to the system. The state vector is composed of the wind speed components and the pitot scale factor. In the second stage, in order to estimate the state parameters of the UAV, GPS, and Inertial Measurement Unit (IMU) measurements are considered in a Linear Kalman filter. The second stage filter uses the first stage EKF estimates of the wind speed values. Between these two stages, a sensor fault detection algorithm is placed. The sensor fault detection algorithm is based on the first stage EKF innovation process. After detecting the fault on the sensor measurements, the state parameters of the UAV are estimated via robust Kalman filter (RKF) against sensor faults. The robust Kalman filter algorithm, which brings the fault tolerance feature to the filter, secures accurate estimation results in case of a faulty measurement without affecting the remaining good estimation characteristics. In simulations, noise increment and bias type of sensor faults are considered.
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Liu, Zhao, and Anwar Sohel. "Application of MMAE to the Fault Detection of Lithium-Ion Battery." Applied Mechanics and Materials 598 (July 2014): 342–46. http://dx.doi.org/10.4028/www.scientific.net/amm.598.342.
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With the advantage of high energy density, long cycle life and environmental friendliness, Lithium-ion battery has become a promising power source for hybrid and electric vehicles, which are liable to two kinds of failure, overcharge and overdischarge. Because of the capability of detecting multiple faults, Multiple Model Adaptive Estimation (MMAE) method was applied to a model-based fault detection of a lithium-ion battery with a two-order linear electrical model. Parameters that represent normal-mode and faulty-mode of the battery were obtained by a series of experiments, and three Kalman filters were designed thereafter. Finally, simulation verified the performance of the MMAE algorithm on fault detection of these two kinds of fault and it is shown that this technique is able to discern these faults rapidly and accurately.
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Swetapadma, Aleena, and Anamika Yadav. "Fuzzy Inference System Approach for Locating Series, Shunt, and Simultaneous Series-Shunt Faults in Double Circuit Transmission Lines." Computational Intelligence and Neuroscience 2015 (2015): 1–12. http://dx.doi.org/10.1155/2015/620360.
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Many schemes are reported for shunt fault location estimation, but fault location estimation of series or open conductor faults has not been dealt with so far. The existing numerical relays only detect the open conductor (series) fault and give the indication of the faulty phase(s), but they are unable to locate the series fault. The repair crew needs to patrol the complete line to find the location of series fault. In this paper fuzzy based fault detection/classification and location schemes in time domain are proposed for both series faults, shunt faults, and simultaneous series and shunt faults. The fault simulation studies and fault location algorithm have been developed using Matlab/Simulink. Synchronized phasors of voltage and current signals of both the ends of the line have been used as input to the proposed fuzzy based fault location scheme. Percentage of error in location of series fault is within 1% and shunt fault is 5% for all the tested fault cases. Validation of percentage of error in location estimation is done using Chi square test with both 1% and 5% level of significance.
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Burdusel, Constantin. "A Fault Detection Method for Attitude Sensors of Satellite." Applied Mechanics and Materials 325-326 (June 2013): 769–73. http://dx.doi.org/10.4028/www.scientific.net/amm.325-326.769.
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Any system based on data acquisition from different sensors has important characteristics of quality and fiability that are given by the detection and the isolation of the sensors faults. This paper presents two methods of fault detection, applicable in aerospace domain, used in satellite systems, and the simulation of the functionalities of the methods using Matlab. Both methods are based on the estimation of the sensors fault by analysing the differences between the measured value and the estimated one, using an EKF estimator [. Keywords: EKF filter, fault detection, satellite sensors, t-test
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Minh, Vu Trieu, Nitin Afzulpurkar, and W. M. Wan Muhamad. "Fault Detection and Control of Process Systems." Mathematical Problems in Engineering 2007 (2007): 1–20. http://dx.doi.org/10.1155/2007/80321.
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This paper develops a stochastic hybrid model-based control system that can determine online the optimal control actions, detect faults quickly in the control process, and reconfigure the controller accordingly using interacting multiple-model (IMM) estimator and generalized predictive control (GPC) algorithm. A fault detection and control system consists of two main parts: the first is the fault detector and the second is the controller reconfiguration. This work deals with three main challenging issues: design of fault model set, estimation of stochastic hybrid multiple models, and stochastic model predictive control of hybrid multiple models. For the first issue, we propose a simple scheme for designing faults for discrete and continuous random variables. For the second issue, we consider and select a fast and reliable fault detection system applied to the stochastic hybrid system. Finally, we develop a stochastic GPC algorithm for hybrid multiple-models controller reconfiguration with soft switching signals based on weighted probabilities. Simulations for the proposed system are illustrated and analyzed.
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Nikranjbar, A., M. Ebrahimi, and A. S. Wood. "Model-based fault diagnosis of induction motor eccentricity using particle swarm optimization." Proceedings of the Institution of Mechanical Engineers, Part C: Journal of Mechanical Engineering Science 223, no. 3 (December 1, 2008): 607–15. http://dx.doi.org/10.1243/09544062jmes1113.
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Much research works address model-free or signal processing and spectral analysis-based fault detection schemes for rotor eccentricity fault in induction motors. Nevertheless, despite existing reliable fault-embedded eccentricity mathematical models such as the winding function method an integrated model-based fault detection algorithm for detecting this fault yet has not been fully explored. This article presents model-based mixed-eccentricity fault detection and diagnosis for induction motors. The proposed algorithm can successfully detect faults and their severity using stator currents. To determine the values of the fault-related parameters, an adaptive synchronization-based parameter estimation algorithm is introduced using particle swarm optimization. Simulation and experiments demonstrate the ability of the algorithm to detect and diagnose these faults. The proposed algorithm can be employed to estimate the parameters, in addition to slowly time varying and abruptly changing parameters.
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Asokan, A., and D. Sivakumar. "Model based fault detection and diagnosis using structured residual approach in a multi-input multi-output system." Serbian Journal of Electrical Engineering 4, no. 2 (2007): 133–45. http://dx.doi.org/10.2298/sjee0702133a.
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Fault detection and isolation (FDI) is a task to deduce from observed variable of the system if any component is faulty, to locate the faulty components and also to estimate the fault magnitude present in the system. This paper provides a systematic method of fault diagnosis to detect leak in the three-tank process. The proposed scheme makes use of structured residual approach for detection, isolation and estimation of faults acting on the process [1]. This technique includes residual generation and residual evaluation. A literature review showed that the conventional fault diagnosis methods like the ordinary Chisquare (?2) test method, generalized likelihood ratio test have limitations such as the "false alarm" problem. From the results it is inferred that the proposed FDI scheme diagnoses better when compared to other conventional methods.
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Zhu, Linhai, Jinfu Liu, Yujia Ma, Weixing Zhou, and Daren Yu. "A Coupling Diagnosis Method for Sensor Faults Detection, Isolation and Estimation of Gas Turbine Engines." Energies 13, no. 18 (September 22, 2020): 4976. http://dx.doi.org/10.3390/en13184976.
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In this paper a novel fault detection, isolation, and identification (FDI&E) scheme using a coupling diagnosis method with the integration of the model-based method and unsupervised learning algorithm is proposed and developed for monitoring gas turbine sensor faults, which represents an integration of Square Root Cubature Kalman Filters (SRCKF) and an improved Density-Based Spatial Clustering of Application with Noise (DBSCAN) algorithm. A detection indicator produced by SRCKF with a specific hypothesis is used for extracting sensor fault features against process and measurement noise, as well as operating conditions. Then, an improved DBSCAN is implemented based on a voting scheme to detect and isolate the faulty sensors. Finally, a residual-based fault estimation scheme is proposed to track sensor fault evolution and help to judge the types of faults. Moreover, the observability of the model involved is analyzed to verify the stable operation of the FDI&E scheme. Various experiments for single and concurrent sensor fault scenarios in a dual-spool gas turbine prototype during a whole flight mission are conducted to demonstrate the effectiveness of the proposed FDI&E scheme. Moreover, comparative studies confirm the superiority of our proposed FDI&E scheme than the existing methods in terms of promptness and robustness of the sensor FDI.
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Shahbaz, Muhammad Hamza, and Arslan Ahmed Amin. "Design of hybrid fault-tolerant control system for air-fuel ratio control of internal combustion engines using artificial neural network and sliding mode control against sensor faults." Advances in Mechanical Engineering 15, no. 3 (March 2023): 168781322311607. http://dx.doi.org/10.1177/16878132231160729.
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This paper proposes a novel hybrid fault-tolerant control system (HFTCS) with dedicated non-linear controllers: artificial neural network (ANN) and sliding mode control (SMC) for active and passive parts, respectively. The proposed system can provide both desirable properties of stability to unexpected fast disturbances and post-fault optimal performance. In the active fault tolerant control system (AFTCS) part, the fault detection and isolation (FDI) unit is designed through the use of ANN for the estimation of faulty sensor values in the observer model. In the passive fault-tolerant system (PFTCS) part, the air-fuel ratio (AFR) controller is designed using a robust SMC that allows systems to manage faults in predefined limits without estimation. In the proposed system, SMC will form the passive part to react instantly to faults while ANN will optimize post-fault performance with active compensation. Moreover, Lyapunov stability analysis was also performed to make sure that the system remains stable in both normal and faulty conditions. The simulation results in the Matlab/Simulink environment show that the designed controller is robust to faults in normal and noisy measurements of the sensors. A comparison with the existing works also demonstrates the superior performance of the proposed hybrid algorithm.
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Wang, Chu-Tong, Jason S. H. Tsai, Chia-Wei Chen, You Lin, Shu-Mei Guo, and Leang-San Shieh. "An Active Fault-Tolerant PWM Tracker for Unknown Nonlinear Stochastic Hybrid Systems: NARMAX Model and OKID-Based State-Space Self-Tuning Control." Journal of Control Science and Engineering 2010 (2010): 1–27. http://dx.doi.org/10.1155/2010/217515.
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An active fault-tolerant pulse-width-modulated tracker using the nonlinear autoregressive moving average with exogenous inputs model-based state-space self-tuning control is proposed for continuous-time multivariable nonlinear stochastic systems with unknown system parameters, plant noises, measurement noises, and inaccessible system states. Through observer/Kalman filter identification method, a good initial guess of the unknown parameters of the chosen model is obtained so as to reduce the identification process time and enhance the system performances. Besides, by modifying the conventional self-tuning control, a fault-tolerant control scheme is also developed. For the detection of fault occurrence, a quantitative criterion is exploited by comparing the innovation process errors estimated by the Kalman filter estimation algorithm. In addition, the weighting matrix resetting technique is presented by adjusting and resetting the covariance matrix of parameter estimates to improve the parameter estimation for faulty system recovery. The technique can effectively cope with partially abrupt and/or gradual system faults and/or input failures with fault detection.
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Pouliezos, A., G. Stavrakakis, and C. Lefas. "Fault detection using parameter estimation." Quality and Reliability Engineering International 5, no. 4 (October 1989): 283–90. http://dx.doi.org/10.1002/qre.4680050407.
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Shin, Donghoon, Kang-moon Park, and Manbok Park. "Development of Fail-Safe Algorithm for Exteroceptive Sensors of Autonomous Vehicles." Electronics 9, no. 11 (October 26, 2020): 1774. http://dx.doi.org/10.3390/electronics9111774.
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This paper presents a fail-safe algorithm for the exteroceptive sensors of autonomous vehicles. The proposed fault diagnosis mechanism consists of three parts: (1) fault detecting by a duplication-comparison method, (2) fault isolating by possible area prediction and (3) in-vehicle sensor fail-safes. The main ideas are the usage of redundant external sensor pairs, which estimate the same target, whose results are compared to detect the fault by a modified duplication-comparison method and the novel fault isolation method using target predictions. By comparing the estimations of surrounding vehicles and the raw measurement data, the location of faults can be determined whether they are from sensors themselves or a software error. In addition, faults were isolated by defining possible areas where existing sensor coordinates could be measured, which can be predicted by using previous estimation results. The performance of the algorithm has been tested by using offline vehicle data analysis via MATLAB. Various fault injection experiments were conducted and the performance of the suggested algorithm was evaluated based on the time interval between injection and the detection of faults.
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Li, Qiuying, and Hoang Pham. "Modeling Software Fault-Detection and Fault-Correction Processes by Considering the Dependencies between Fault Amounts." Applied Sciences 11, no. 15 (July 29, 2021): 6998. http://dx.doi.org/10.3390/app11156998.
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Many NHPP software reliability growth models (SRGMs) have been proposed to assess software reliability during the past 40 years, but most of them have focused on modeling the fault detection process (FDP) in two ways: one is to ignore the fault correction process (FCP), i.e., faults are assumed to be instantaneously removed after the failure caused by the faults is detected. However, in real software development, it is not always reliable as fault removal usually needs time, i.e., the faults causing failures cannot always be removed at once and the detected failures will become more and more difficult to correct as testing progresses. Another way to model the fault correction process is to consider the time delay between the fault detection and fault correction. The time delay has been assumed to be constant and function dependent on time or random variables following some kind of distribution. In this paper, some useful approaches to the modeling of dual fault detection and correction processes are discussed. The dependencies between fault amounts of dual processes are considered instead of fault correction time-delay. A model aiming to integrate fault-detection processes and fault-correction processes, along with the incorporation of a fault introduction rate and testing coverage rate into the software reliability evaluation is proposed. The model parameters are estimated using the Least Squares Estimation (LSE) method. The descriptive and predictive performance of this proposed model and other existing NHPP SRGMs are investigated by using three real data-sets based on four criteria, respectively. The results show that the new model can be significantly effective in yielding better reliability estimation and prediction.
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Tharrault, Yvon, Gilles Mourot, José Ragot, and Didier Maquin. "Fault Detection and Isolation with Robust Principal Component Analysis." International Journal of Applied Mathematics and Computer Science 18, no. 4 (December 1, 2008): 429–42. http://dx.doi.org/10.2478/v10006-008-0038-3.
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Fault Detection and Isolation with Robust Principal Component AnalysisPrincipal component analysis (PCA) is a powerful fault detection and isolation method. However, the classical PCA, which is based on the estimation of the sample mean and covariance matrix of the data, is very sensitive to outliers in the training data set. Usually robust principal component analysis is applied to remove the effect of outliers on the PCA model. In this paper, a fast two-step algorithm is proposed. First, the objective was to find an accurate estimate of the covariance matrix of the data so that a PCA model might be developed that could then be used for fault detection and isolation. A very simple estimate derived from a one-step weighted variance-covariance estimate is used (Ruiz-Gazen, 1996). This is a "local" matrix of variance which tends to emphasize the contribution of close observations in comparison with distant observations (outliers). Second, structured residuals are used for multiple fault detection and isolation. These structured residuals are based on the reconstruction principle, and the existence condition of such residuals is used to determine the detectable faults and the isolable faults. The proposed scheme avoids the combinatorial explosion of faulty scenarios related to multiple faults to be considered. Then, this procedure for outliers detection and isolation is successfully applied to an example with multiple faults.
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Wei, Juhui, Zhangming He, Jiongqi Wang, Dayi Wang, and Xuanying Zhou. "Fault Detection Based on Multi-Dimensional KDE and Jensen–Shannon Divergence." Entropy 23, no. 3 (February 24, 2021): 266. http://dx.doi.org/10.3390/e23030266.
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Weak fault signals, high coupling data, and unknown faults commonly exist in fault diagnosis systems, causing low detection and identification performance of fault diagnosis methods based on T2 statistics or cross entropy. This paper proposes a new fault diagnosis method based on optimal bandwidth kernel density estimation (KDE) and Jensen–Shannon (JS) divergence distribution for improved fault detection performance. KDE addresses weak signal and coupling fault detection, and JS divergence addresses unknown fault detection. Firstly, the formula and algorithm of the optimal bandwidth of multidimensional KDE are presented, and the convergence of the algorithm is proved. Secondly, the difference in JS divergence between the data is obtained based on the optimal KDE and used for fault detection. Finally, the fault diagnosis experiment based on the bearing data from Case Western Reserve University Bearing Data Center is conducted. The results show that for known faults, the proposed method has 10% and 2% higher detection rate than T2 statistics and the cross entropy method, respectively. For unknown faults, T2 statistics cannot effectively detect faults, and the proposed method has approximately 15% higher detection rate than the cross entropy method. Thus, the proposed method can effectively improve the fault detection rate.
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Mid, Ernie Che, and Vivek Dua. "Fault Detection in Wastewater Treatment Systems Using Multiparametric Programming." Processes 6, no. 11 (November 20, 2018): 231. http://dx.doi.org/10.3390/pr6110231.
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In this work, a methodology for fault detection in wastewater treatment systems, based on parameter estimation, using multiparametric programming is presented. The main idea is to detect faults by estimating model parameters, and monitoring the changes in residuals of model parameters. In the proposed methodology, a nonlinear dynamic model of wastewater treatment was discretized to algebraic equations using Euler’s method. A parameter estimation problem was then formulated and transformed into a square system of parametric nonlinear algebraic equations by writing the optimality conditions. The parametric nonlinear algebraic equations were then solved symbolically to obtain the concentration of substrate in the inflow, , inhibition coefficient, , and specific growth rate, , as an explicit function of state variables (concentration of biomass, ; concentration of organic matter, ; concentration of dissolved oxygen, ; and volume, ). The estimated model parameter values were compared with values from the normal operation. If the residual of model parameters exceeds a certain threshold value, a fault is detected. The application demonstrates the viability of the approach, and highlights its ability to detect faults in wastewater treatment systems by providing quick and accurate parameter estimates using the evaluation of explicit parametric functions.
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Piltan, Farzin, Cheol-Hong Kim, and Jong-Myon Kim. "Advanced Adaptive Fault Diagnosis and Tolerant Control for Robot Manipulators." Energies 12, no. 7 (April 3, 2019): 1281. http://dx.doi.org/10.3390/en12071281.
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In this paper, an adaptive Takagi–Sugeno (T–S) fuzzy sliding mode extended autoregressive exogenous input (ARX)–Laguerre proportional integral (PI) observer is proposed. The proposed T–S fuzzy sliding mode extended-state ARX–Laguerre PI observer adaptively improves the reliability, robustness, estimation accuracy, and convergence of fault detection, estimation, and identification. For fault-tolerant control in the presence of uncertainties and unknown conditions, an adaptive fuzzy sliding mode estimation technique is introduced. The sliding surface slope gain is significant to improve the system’s stability, but the sliding mode technique increases high-frequency oscillation (chattering), which reduces the precision of the fault diagnosis and tolerant control. A fuzzy procedure using a sliding surface and actual output position as inputs can adaptively tune the sliding surface slope gain of the sliding mode fault-tolerant control technique. The proposed robust adaptive T–S fuzzy sliding mode estimation extended-state ARX–Laguerre PI observer was verified with six degrees of freedom (DOF) programmable universal manipulation arm (PUMA) 560 robot manipulator, proving qualified efficiency in detecting, isolating, identifying, and tolerant control for faults inherent in sensors and actuators. Experimental results showed that the proposed technique improves the reliability of the fault detection, estimation, identification, and tolerant control.
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Afaneen anwar, Dr, and Rana Ali Abttan. "Simultaneous fault detection and diagnosis in electric power system using hybrid method." International Journal of Engineering & Technology 7, no. 4 (September 26, 2018): 2692. http://dx.doi.org/10.14419/ijet.v7i4.14146.
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Simultaneous fault is one of the challenging issues. Faults are the major hurdles in power system designing and protection .Simultaneous fault is the combination of faults indicates that that two or more faults which occur at the same time.The main objective of simultaneous fault detection, classification and location is satisfy accelerates line restoration, maintains system, stability, repairs the fault, decreases the restoration time and increases the system reliability.This paper presents an approach for analysis, detection, classification and location for simultaneous faults in bus bar and transmission line. Two port network is adapted for analysis , voltage and current measurement method is adapted in the fault detection, neural network in the fault classification and location for different types of fault and places were to estimation accurately fault location by analyzing the data available after the beginning of disturbance.All programs were written in MATLAB environment. The programs were test on IEEE- 11 bus bar network. The results clarified that the voltage and current measurement method and impedance method is very effective for simultaneous fault detection, classification and location.
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Ma, Jie, and Jianan Xu. "Fault Prediction Algorithm for Multiple Mode Process Based on Reconstruction Technique." Mathematical Problems in Engineering 2015 (2015): 1–8. http://dx.doi.org/10.1155/2015/348729.
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In the framework of fault reconstruction technique, this paper studies the problems of multiple mode process fault detection, fault estimation, and fault prediction systematically based on multi-PCA model. First, a multi-PCA model is used for fault detection in steady state process under different conditions, while a weighted algorithm is applied to transition process. Then, describe the faults quantitatively and use the optimization method to derive the fault amplitude under the sense of fault reconstruction. Fault amplitude drifts under different conditions even if the same fault occurs. To solve the above problem, consistent estimation algorithm of fault amplitude under different conditions has been studied. Last, employ the support vector machine (SVM) to predict the trend of the fault amplitude. Effectiveness of the algorithms proposed in this paper has been verified using Tennessee Eastman process as the study object.
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Taimoor, Muhammad, and Li Aijun. "Neural-sliding mode approach-based adaptive estimation, isolation and tolerance of aircraft sensor fault." Aircraft Engineering and Aerospace Technology 92, no. 2 (December 12, 2019): 237–55. http://dx.doi.org/10.1108/aeat-05-2019-0106.
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Purpose The purpose of this paper is to propose an adaptive neural-sliding mode-based observer for the estimation and reconstruction of unknown faults and disturbances for time-varying nonlinear systems such as aircraft, to ensure preciseness in the diagnosis of fault magnitude as well as the shape without enhancement of system complexity and cost. Fault-tolerant control (FTC) strategy based on adaptive neural-sliding mode is also proposed in the existence of faults for ensuring the stability of the faulty system. Design/methodology/approach In this paper, three strategies are presented: adaptive radial basis functions neural network (ARBFNN), conventional radial basis functions neural network (CRBFNN) and integral-chain differentiator. For the purpose of enhancement of fault diagnosis and isolation, a new sliding mode-based concept is introduced for the weight updating parameters of radial basis functions neural network (RBFNN).The main objective of updating the weight parameters adaptively is to enhance the effectiveness of fault diagnosis and isolation without increasing the computational complexities of the system. Results depict the effectiveness of the proposed ARBFNN approach in fault detection (FD) and approximation compared to CRBFNN, integral-chain differentiator and schemes existing in literature. In the second step, the FTC strategy is presented separately for each observer in the presence of unknown faults and failures for ensuring the stability of the system, which is validated on Boeing 747 100/200 aircraft. Findings The proposed adaptive neural-sliding mode approach is investigated, which depicts more effectiveness in numerous situations such as faults, disturbances and uncertainties compared to algorithms used in literature. In this paper, both the fault approximation and isolation and the fault tolerance approaches are studied. Practical implications For the enhancement of safety level as well as for avoiding any kind of damage, timely FD and fault tolerance have always had a significant role; therefore, the algorithms proposed in this research ensure the tolerance of faults and failures, which plays a vital role in practical life for avoiding any kind of damage. Originality/value In this study, a new neural-sliding mode concept is adopted for the adaptive faults approximation and reconstruction, and then the FTC algorithms are studied for each observer separately, whereas in previous studies, only the fault detection and isolation (FDI) or the fault tolerance problems were studied. Results demonstrate the effectiveness of the proposed strategy compared to the approaches given in the literature.
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Georges, Jean-Philippe, Didier Theilliol, Vincent Cocquempot, Jean-Christophe Ponsart, and Christophe Aubrun. "Fault tolerance in networked control systems under intermittent observations." International Journal of Applied Mathematics and Computer Science 21, no. 4 (December 1, 2011): 639–48. http://dx.doi.org/10.2478/v10006-011-0050-x.
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Fault tolerance in networked control systems under intermittent observationsThis paper presents an approach to fault tolerant control based on the sensor masking principle in the case of wireless networked control systems. With wireless transmission, packet losses act as sensor faults. In the presence of such faults, the faulty measurements corrupt directly the behaviour of closed-loop systems. Since the controller aims at cancelling the error between the measurement and its reference input, the real outputs will, in such a networked control system, deviate from the desired value and may drive the system to its physical limitations or even to instability. The proposed method facilitates fault compensation based on an interacting multiple model approach developed in the framework of channel errors or network congestion equivalent to multiple sensors failures. The interacting multiple model method involved in a networked control system provides simultaneously detection and isolation of on-line packet losses, and also performs a suitable state estimation. Based on particular knowledge of packet losses, sensor fault-tolerant controls are obtained by computing a new control law using fault-free estimation of the faulty element to avoid intermittent observations that might develop into failures and to minimize the effects on system performance and safety.
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Jamouli, Hicham, Mohamed El Hail, and Dominique Sauter. "A mixed active and passive GLR test for a fault tolerant control system." International Journal of Applied Mathematics and Computer Science 22, no. 1 (March 1, 2012): 9–23. http://dx.doi.org/10.2478/v10006-012-0001-1.
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A mixed active and passive GLR test for a fault tolerant control system This paper presents an adaptive Generalized Likelihood Ratio (GLR) test for multiple Faults Detection and Isolation (FDI) in stochastic linear dynamic systems. Based on the work of Willsky and Jones (1976), we propose a modified generalized likelihood ratio test, allowing detection, isolation and estimation of multiple sequential faults. Our contribution aims to maximise the good decision rate of fault detection using another updating strategy. This is based on a reference model updated on-line after each detection and isolation of one fault. To reduce the computational requirement, the passive GLR test will be derived from a state estimator designed on a fixed reference model directly sensitive to system changes. We will show that active and passive GLR tests will be mixed and give interesting results compared with the GLR of Willsky and Jones (1976), and that they can be easily integrated in a reconfigurable Fault-Tolerant Control System (FTCS) to asymptotically recover the nominal system performances of the jump-free system.
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Piltan, Farzin, and Jong-Myon Kim. "Bearing Fault Diagnosis Using a Hybrid Fuzzy V-Structure Fault Estimator Scheme." Sensors 23, no. 2 (January 16, 2023): 1021. http://dx.doi.org/10.3390/s23021021.
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Bearings are critical components of motors. However, they can cause several issues. Proper and timely detection of faults in the bearings can play a decisive role in reducing damage to the entire system, thereby reducing economic losses. In this study, a hybrid fuzzy V-structure fuzzy fault estimator was used for fault diagnosis and crack size identification in the bearing using vibration signals. The estimator was designed based on the combination of a fuzzy algorithm and a V-structure approach to reduce the oscillation and improve the unknown condition’s estimation and prediction in using the V-structure method. The V-structure surface is developed by the proposed fuzzy algorithm, which reduces the vibrations and improves the stability. In addition, the parallel fuzzy method is used to improve the robustness and stability of the V-structure algorithm. For data modeling, the proposed combination of an external autoregression error, a Laguerre filter, and a support vector regression algorithm was employed. Finally, the support vector machine algorithm was used for data classification and crack size detection. The effectiveness of the proposed approach was evaluated by leveraging the vibration signals provided in the Case Western Reserve University bearing dataset. The dataset consists of four conditions: normal, ball failure, inner fault, and outer fault. The results showed that the average accuracy of fault classification and crack size identification using the hybrid fuzzy V-structure fuzzy fault estimation algorithm was 98.75% and 98%, respectively.
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Al-Zuriqat, Thamer, Carlos Chillón Geck, Kosmas Dragos, and Kay Smarsly. "Adaptive Fault Diagnosis for Simultaneous Sensor Faults in Structural Health Monitoring Systems." Infrastructures 8, no. 3 (February 22, 2023): 39. http://dx.doi.org/10.3390/infrastructures8030039.
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Structural health monitoring (SHM) is a non-destructive testing method that supports the condition assessment and lifetime estimation of civil infrastructure. Sensor faults may result in the loss of valuable data and erroneous structural condition assessments and lifetime estimations, in the worst case with structural damage remaining undetected. As a result, the concepts of fault diagnosis (FD) have been increasingly adopted by the SHM community. However, most FD concepts for SHM consider only single-fault occurrence, which may oversimplify actual fault occurrences in real-world SHM systems. This paper presents an adaptive FD approach for SHM systems that addresses simultaneous faults occurring in multiple sensors. The adaptive FD approach encompasses fault detection, isolation, and accommodation, and it builds upon analytical redundancy, which uses correlated data from multiple sensors of an SHM system. Specifically, faults are detected using the predictive capabilities of artificial neural network (ANN) models that leverage correlations within sensor data. Upon defining time instances of fault occurrences in the sensor data, faults are isolated by analyzing the moving average of individual sensor data around the time instances. For fault accommodation, the ANN models are adapted by removing faulty sensors and by using sensor data prior to the occurrence of faults to produce virtual outputs that substitute the faulty sensor data. The proposed adaptive FD approach is validated via two tests using sensor data recorded by an SHM system installed on a railway bridge. The results demonstrate that the proposed approach is capable of ensuring the accuracy, reliability, and performance of real-world SHM systems, in which faults in multiple sensors occur simultaneously.
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Taimoor, Muhammad, Xiao Lu, Wasif Shabbir, Chunyang Sheng, and Muhammad Samiuddin. "Novel neural observer based fault estimation, reconstruction and fault-tolerant control scheme for nonlinear systems." Journal of Intelligent & Fuzzy Systems 41, no. 1 (August 11, 2021): 355–86. http://dx.doi.org/10.3233/jifs-201830.
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This research is concerned with the adaptive neural network observer based fault approximation and fault-tolerant control of time-varying nonlinear systems. A new strategy for adaptively updating the weights of neural network parameters is proposed to enhance fault detection accuracy. Lyapunov function theory (LFT) is applied for adaptively updating the learning parameters weights of multi-layer neural network (MLNN). The purpose of using adaptive learning rates to update the weight parameters of MLNN is to obtain the global minima for highly nonlinear functions without increasing the computational complexities and costs and increase the efficacy of fault detection. Results of the proposed adaptive MLNN observer are compared with conventional MLNN observer and high gain observer. The effects of various faults or failures are studied in detail. The proposed strategy shows more robustness to disturbances, uncertainties, and unmodelled system dynamics compared to the conventional neural network, high gain observer and other existing techniques in literature. Fault tolerant control (FTC) schemes are also proposed to account for the presence of various faults and failures. Separate sliding mode control (SMC) based FTC schemes are designed for each observer to ensure stability of the faulty system. The suggested strategy is validated on Boeing 747 100/200 aircraft. Results demonstrate the effectiveness of both the proposed adaptive MLNN observer and the FTC based on the proposed adaptive MLNN compared to the conventional MLNN, high gain observer and other existing schemes in literature. Comparison of the performance of all the strategies validates the superiority of the proposed strategy and shows that the FTC based on proposed adaptive MLNN strategy provides better robustness to various situations such as disturbances and uncertainties. It is concluded that the proposed strategy can be integrated into the aircraft for the purpose of fault diagnosis, fault isolation and FTC scheme for increasing the performance of the system.
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Zhang, Fan, Ye Wang, and Yanbin Gao. "A Novel Method of Fault Detection and Identification in a Tightly Coupled, INS/GNSS-Integrated System." Sensors 21, no. 9 (April 21, 2021): 2922. http://dx.doi.org/10.3390/s21092922.
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Fault detection and identification are vital for guaranteeing the precision and reliability of tightly coupled inertial navigation system (INS)/global navigation satellite system (GNSS)-integrated navigation systems. A variance shift outlier model (VSOM) was employed to detect faults in the raw pseudo-range data in this paper. The measurements were partially excluded or included in the estimation process depending on the size of the associated shift in the variance. As an objective measure, likelihood ratio and score test statistics were used to determine whether the measurements inflated variance and were deemed to be faulty. The VSOM is appealing because the down-weighting of faulty measurements with the proper weighting factors in the analysis automatically becomes part of the estimation procedure instead of deletion. A parametric bootstrap procedure for significance assessment and multiple testing to identify faults in the VSOM is proposed. The results show that VSOM was validated through field tests, and it works well when single or multiple faults exist in GNSS measurements.
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Guo, Liang, Yingqi Huang, Hongli Gao, and Li Zhang. "Ball Screw Fault Detection and Location Based on Outlier and Instantaneous Rotational Frequency Estimation." Shock and Vibration 2019 (July 10, 2019): 1–12. http://dx.doi.org/10.1155/2019/7497363.
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Ball screw, as a crucial component, is widely used in various rotating machines. Its health condition significantly influences the efficiency and position precision of rotating machines. Therefore, it is important to accurately detect faults and estimate fault location in a ball screw system to make sure that the ball screw system runs safely and effectively. However, there are few research studies concerning the topic. The aim of this paper is to fill the gap. In this paper, we propose a method to automatically detect and locate faults in a ball screw system. The proposed method mainly consists of two steps: fault time estimation and instantaneous rotational frequency extraction. In the first step, a statistics-based outlier detection method is proposed to involve the fault information mixing in vibration signals and estimate the fault time. In the second step, a parameterized time-frequency analysis method is utilized to extract the instantaneous rotational frequency of the ball screw system. Once the fault time and instantaneous rotational frequency are estimated, the fault location in a ball screw system is calculated through an integral operation. In order to verify the effectiveness of the proposed method, two fault location experiments under the constant and varying speed conditions are conducted in a ball screw failure simulation testbed. The results demonstrate that the proposed method is able to accurately detect the faults in a ball screw system and estimate the fault location within an error of 22%.
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Li, Jian, Xinxin Guo, and Bo Li. "Robust Fault Diagnosis and Adaptive Parameter Identification for Single Phase Transformerless Inverters." Mathematical Problems in Engineering 2018 (August 13, 2018): 1–11. http://dx.doi.org/10.1155/2018/3025838.
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The paper presents the theoretical analysis and simulation verification of robust fault diagnosis and adaptive parameter identification for single phase transformerless inverters. The fault diagnosis is composed of two parts, fault detection and fault identification. In the fault detection part, a Luenberger observer is designed to realize the detection of faults. Then, we apply a bank of observers to identify the location of faults. Meanwhile, the fault identification observers based estimation along with a gradient descent algorithm are also used in the parameter identification to estimate the actual values of components in a single phase transformerless inverter. Not only we develop the design methodology for the robust fault diagnosis and adaptive parameter identifier but also we present simulation results. The simulation results show the effectiveness of fault diagnosis and the accurate tracking of changes in component parameters for a wide range.
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Lounici, Yacine, Youcef Touati, and Smail Adjerid. "Uncertain fault estimation using bicausal bond graph: Application to intelligent autonomous vehicle." Proceedings of the Institution of Mechanical Engineers, Part I: Journal of Systems and Control Engineering 234, no. 10 (December 23, 2019): 1150–71. http://dx.doi.org/10.1177/0959651819892379.
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This article addresses the fault detection and isolation problem of uncertain systems using the bond graph model–based approach. The latter provides through its causal and structural properties an automatic analytical redundancy relations generation. The numerical evaluation of analytical redundancy relations yields residuals, which are used to verify the coherence between the real system and reference behaviors describing the nominal operation. In fact, the residual is compared to its thresholds to detect the fault. In addition, the comparison between all fault signatures allows making a decision on fault isolation. Moreover, to isolate the faults that activate the same set of residuals, an additional residual must be calculated for each fault. This additional residual is the comparison between two estimations of the considered fault obtained using the sensitivity relations. However, due to the presence of uncertainties, errors can occur in the fault estimation allowing false decisions on fault isolation. The novelties and innovative interests in the present work are (1) to improve the fault estimation procedure based on the uncertainties modeling and bicausality notion, in order to overcome the problem related to errors in the estimated fault and (2) to suitably generate the isolation thresholds in a systematic way using the uncertain fault estimation procedure proposed in this article so that fault can be isolated successfully. The proposed methodology is studied under various scenarios via simulations over an electromechanical traction system corresponding to a quarter of intelligent autonomous vehicle, named RobuCar.
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Zhang, Milu, Tianzhen Wang, Tianhao Tang, Zhuo Liu, and Christophe Claramunt. "A Synchronous Sampling Based Harmonic Analysis Strategy for Marine Current Turbine Monitoring System under Strong Interference Conditions." Energies 12, no. 11 (June 3, 2019): 2117. http://dx.doi.org/10.3390/en12112117.
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Affected by high density, non-uniform, and unstructured seawater environment, fault detection of Marine Current Turbine (MCT) faces various fault features and strong interferences. To solve these problems, a harmonic analysis strategy based on zero-crossing estimation and Empirical Mode Decomposition (EMD) filter banks is proposed. First, the detection problems of rotor imbalance fault under strong interference conditions are described through an analysis of the fault mechanism and operation environment of MCT. Therefore, against various fault features, a zero-crossing estimation is proposed to calculate instantaneous frequency. Last, and in order to solve the problem that the frequency and amplitude of the operating parameters are partially or completely covered by interference, a band-pass filter based on EMD is used, together with a characteristic frequency selected by a Pearson correlation coefficient. This strategy can accurately detect the multiplicative faults under strong interference conditions, and can be applied to the MCT fault detection system. Theoretical and experimental results verify the effectiveness of the proposed strategy.
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Li, Xue, Zhikang Fan, Shengfeng Wang, Aibing Qiu, and Jingfeng Mao. "A Distributed Fault Diagnosis and Cooperative Fault-Tolerant Control Design Framework for Distributed Interconnected Systems." Sensors 22, no. 7 (March 23, 2022): 2480. http://dx.doi.org/10.3390/s22072480.
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This paper investigates a design framework for a class of distributed interconnected systems, where a fault diagnosis scheme and a cooperative fault-tolerant control scheme are included. First of all, fault detection observers are designed for the interconnected subsystems, and the detection results will be spread to all subsystems in the form of a broadcast. Then, to locate the faulty subsystem accurately, fault isolation observers are further designed for the alarming subsystems in turn with the aid of an adaptive fault estimation technique. Based on this, the fault estimation information is used to compensate for the residuals, and then isolation decision logic is conducted. Moreover, the cooperative fault-tolerant control unit, where state feedback and cooperative compensation are both utilized, is introduced to ensure the stability of the whole system. Finally, the simulation of intelligent unmanned vehicle platooning is adopted to demonstrate the applicability and effectiveness of the proposed design framework.
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Piltan, Farzin, Alexander E. Prosvirin, and Jong-Myon Kim. "Robot manipulator active fault-tolerant control using a machine learning-based automated robust hybrid observer." Journal of Intelligent & Fuzzy Systems 39, no. 5 (November 19, 2020): 6443–63. http://dx.doi.org/10.3233/jifs-189109.
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Robotic manipulators represent a class of nonlinear and multiple-degrees-of-freedom robots that have pronounced coupling effects and can be used in various applications. The challenge of understanding complexity in a system’s dynamic behavior, coupling effects, and sources of uncertainty presents substantial challenges regarding fault estimation, detection, identification, and tolerant-control (FEDIT) in a robot manipulator. Thus, a proposed active fault-tolerant control algorithm, based on an adaptive modern sliding mode observer, is represented. Due to the effect of the system’s complexities and uncertainties for fault estimation, detection, and identification (FEDI), a sliding mode observer (SMO) is proposed. To address the sliding mode observer drawbacks for FEDI such as high-frequency oscillation (chattering) and fault estimation accuracy, the modern (T-S fuzzy higher order) technique is represented. In addition, the adaptive technique is applied to the modern sliding mode observer (MSMO) to self-tune the coefficients of the fault estimation observer to increase the reliability and robustness of decision-making for diagnosis of the fault. Next, the residual delivered by the adaptive MSMO (AMSMO) is split into windows, and each window is characterized by a numerical parameter. Finally, the machine learning technique known as a decision tree adaptively derives the threshold values that are used for problems of fault detection and fault identification in this work. Due to control of the effective fault, a surface automated new sliding mode controller (SANSMC) is presented in this work. To address the challenge of chattering and unlimited uncertainties (faults), the AMSMO is applied to the sliding mode controller (SMC). In addition, the surface-automated technique is used to fine-tune the surface coefficient to reduce the chattering and faults in the robot manipulator. The results show that the machine learning-based automated robust hybrid observer significantly improves the robustness, reliability, and accuracy of FEDIT in unknown conditions.
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Idris, Muhd Hafizi, Mohd Rafi Adzman, Hazlie Mokhlis, Mohammad Faridun Naim Tajuddin, Haziah Hamid, and Melaty Amirruddin. "Two-terminal fault detection and location for hybrid transmission circuit." Indonesian Journal of Electrical Engineering and Computer Science 23, no. 2 (August 1, 2021): 639. http://dx.doi.org/10.11591/ijeecs.v23.i2.pp639-649.
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This paper presents the algorithms developed to detect and locate the faults ata hybrid circuit. First, the fault detection algorithm was developed using the comparison of total positive-sequence fault current between pre-fault and fault times to detect the occurrence of a fault. Then, the voltage check method was used to decide whether the fault occurred at overhead line (OHL) or cable section. Finally, the fault location algorithm using the impedance-based method and negative-sequence measurements from both terminals of the circuit were used to estimate the fault point from local terminal. From the tests of various fault conditions including different fault types, fault resistance and fault locations, the proposed method successfully detected all fault cases at around 1 cycle from fault initiation and with correct faulted section identification. Besides that, the fault location algorithm also has very accurate results of fault estimation with average error less than 1 km and 1%.<br /><div> </div>
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Patel, Ujjaval J., Nilesh G. Chothani, and Praghnesh J. Bhatt. "Adaptive quadrilateral distance relaying scheme for fault impedance compensation." Electrical, Control and Communication Engineering 14, no. 1 (July 1, 2018): 58–70. http://dx.doi.org/10.2478/ecce-2018-0007.
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Abstract Impedance reach of numerical distance relay is severely affected by Fault Resistance (RF), Fault Inception Angle (FIA), Fault Type (FT), Fault Location (FL), Power Flow Angle (PFA) and series compensation in transmission line. This paper presents a novel standalone adaptive distance protection algorithm for detection, classification and location of fault in presence of variable fault resistance. It is based on adaptive slope tracking method to detect and classify the fault in combination with modified Fourier filter algorithm for locating the fault. To realize the effectiveness of the proposed technique, simulations are performed in PSCAD using multiple run facility & validation is carried out in MATLAB® considering wide variation in power system disturbances. Due to adaptive setting of quadrilateral characteristics in accordance with variation in fault impedance, the proposed technique is 100 % accurate for detection & classification of faults with error in fault location estimation to be within 1 %. Moreover, the proposed technique provides significant improvement in response time and estimation of fault location as compared to existing distance relaying algorithms, which are the key attributes of multi-functional numerical relay
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Yan, Bingyong, Huazhong Wang, and Huifeng Wang. "Distributed Fault Detection for a Class of Nonlinear Stochastic Systems." Mathematical Problems in Engineering 2014 (2014): 1–8. http://dx.doi.org/10.1155/2014/498630.
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A novel distributed fault detection strategy for a class of nonlinear stochastic systems is presented. Different from the existing design procedures for fault detection, a novel fault detection observer, which consists of a nonlinear fault detection filter and a consensus filter, is proposed to detect the nonlinear stochastic systems faults. Firstly, the outputs of the nonlinear stochastic systems act as inputs of a consensus filter. Secondly, a nonlinear fault detection filter is constructed to provide estimation of unmeasurable system states and residual signals using outputs of the consensus filter. Stability analysis of the consensus filter is rigorously investigated. Meanwhile, the design procedures of the nonlinear fault detection filter are given in terms of linear matrix inequalities (LMIs). Taking the influence of the system stochastic noises into consideration, an outstanding feature of the proposed scheme is that false alarms can be reduced dramatically. Finally, simulation results are provided to show the feasibility and effectiveness of the proposed fault detection approach.
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Mousavi, Shima, and Khashayar Khorasani. "Fault detection of reaction wheels in attitude control subsystem of formation flying satellites." International Journal of Intelligent Unmanned Systems 2, no. 1 (February 4, 2014): 2–26. http://dx.doi.org/10.1108/ijius-02-2013-0011.
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Purpose – A decentralized dynamic neural network (DNN)-based fault detection (FD) system for the reaction wheels of satellites in a formation flying mission is proposed. The paper aims to discuss the above issue. Design/methodology/approach – The highly nonlinear dynamics of each spacecraft in the formation is modeled by using DNNs. The DNNs are trained based on the extended back-propagation algorithm by using the set of input/output data that are collected from the 3-axis of the attitude control subsystem of each satellite. The parameters of the DNNs are adjusted to meet certain performance requirements and minimize the output estimation error. Findings – The capability of the proposed methodology has been investigated under different faulty scenarios. The proposed approach is a decentralized FD strategy, implying that a fault occurrence in one of the spacecraft in the formation is detected by using both a local fault detector and fault detectors constructed specifically based on the neighboring spacecraft. It is shown that this method has the capability of detecting low severity actuator faults in the formation that could not have been detected by only a local fault detector. Originality/value – The nonlinear dynamics of the formation flying of spacecraft are represented by multilayer DNNs, in which conventional static neurons are replaced by dynamic neurons. In our proposed methodology, a DNN is utilized in each axis of every satellite that is trained based on the absolute attitude measurements in the formation that may nevertheless be incapable of detecting low severity faults. The DNNs that are utilized for the formation level are trained based on the relative attitude measurements of a spacecraft and its neighboring spacecraft that are then shown to be capable of detecting even low severity faults, thereby demonstrating the advantages and benefits of our proposed solution.
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Zhang, Mei, Ze-tao Li, Michel Cabassud, and Boutaïeb Dahhou. "An Integrated FDD Approach for an Intensified HEX/Reactor." Journal of Control Science and Engineering 2018 (2018): 1–16. http://dx.doi.org/10.1155/2018/5127505.
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In this paper, a fault detection and diagnosis (FDD) scheme is developed for a class of intensified HEX/reactor, in which faults caused by sensor, actuator, and process are taken into account in the unified framework. By considering overall heat transfer coefficient as a function of fouling and fluid flow rate, a dynamic model which is capable of identifying these two faults simultaneously is derived. Sensor measurements, together with estimation by adaptive high gain observers, are processed, aimed at identifying sensor faults and providing adequate estimation to substitute faulty measurements. Then reliable measurements are fed to several banks of interval filters to generate several banks of residuals; each bank of residuals is sensitive to a particular process parameter/actuator. By evaluating these residuals, process/actuator fault isolation and identification are achieved. The proposed strategy is applied to actual data retrieved from a new intensified heat exchanger reactor. Simulation results confirm the applicability and robustness of the proposed methodology.
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Kenyeres, Éva, and János Abonyi. "Goal-Oriented Tuning of Particle Filters for the Fault Diagnostics of Process Systems." Processes 11, no. 3 (March 9, 2023): 823. http://dx.doi.org/10.3390/pr11030823.
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This study introduces particle filtering (PF) for the tracking and fault diagnostics of complex process systems. In process systems, model equations are often nonlinear and environmental noise is non-Gaussian. We propose a method for state estimation and fault detection in a wastewater treatment system. The contributions of the paper are the following: (1) A method is suggested for sensor placement based on the state estimation performance; (2) based on the sensitivity analysis of the particle filter parameters, a tuning method is proposed; (3) a case study is presented to compare the performances of the classical PF and intelligent particle filtering (IPF) algorithms; (4) for fault diagnostics purposes, bias and impact sensor faults were examined; moreover, the efficiency of fault detection was evaluated. The results verify that particle filtering is applicable and highly efficient for tracking and fault diagnostics tasks in process systems.
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Kim, Donggil, and Dongik Lee. "Fault Parameter Estimation Using Adaptive Fuzzy Fading Kalman Filter." Applied Sciences 9, no. 16 (August 13, 2019): 3329. http://dx.doi.org/10.3390/app9163329.
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Early detection and diagnosis of wind turbine faults is critical for applying a possible maintenance and control strategy to avoid catastrophic incidents. This paper presents a novel method to estimate the parameter of faults in a wind turbine. In this work, the estimation of fault parameters is reformulated as the state estimation problem by augmenting the parameters as an additional state. The novelty of the proposed method lies in the use of an adaptive fuzzy fading algorithm for the adaptive Kalman filter so that the convergence property during the estimation of fault parameter can be improved. The performance of the proposed method is evaluated through a set of numerical simulations with both linear and non-linear models.
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Isermann, R., and B. Freyermuth. "Process Fault Diagnosis Based on Process Model Knowledge: Part I—Principles for Fault Diagnosis With Parameter Estimation." Journal of Dynamic Systems, Measurement, and Control 113, no. 4 (December 1, 1991): 620–26. http://dx.doi.org/10.1115/1.2896466.
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A computer assisted fault diagnosis system (CAFD) is considered which allows the early detection and localization of process faults during normal operation or on request. It is based on an on-line engineering expert system and consists of an analytic problem solution, a process knowledge base, a knowledge acquisition component and an inference mechanism. The analytic problem solution uses a process parameter estimation, and the detection of process coefficient changes, which are symptoms of process faults. The process knowledge base is comprised of analytical knowledge in the form of process models and heuristic knowledge in the form of fault trees and fault statistics. In the phase of knowledge acquisition the process specific knowledge like theoretical process models, the normal behavior and fault trees, is compiled. The inference mechanism performs the fault diagnosis, based on the observed symptoms, the fault trees, fault probabilities and the process history. This is described in Part I. In Part II case study experiments with a d.c. motor, centrifugal pump, a heat exchanger, and an industrial robot show practical results of the model based fault diagnosis.
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Niu, Gang, Yajun Zhao, and Van Tung Tran. "Fault detection and isolation based on bond graph modeling and empirical residual evaluation." Proceedings of the Institution of Mechanical Engineers, Part C: Journal of Mechanical Engineering Science 229, no. 3 (May 22, 2014): 417–28. http://dx.doi.org/10.1177/0954406214536381.
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Fault detection and isolation are critical for safety related complex systems like aircraft, trains, automobiles, power plants and chemical plants. In order to realize a robust and real time monitoring and diagnosis for these types of multi-energy domain systems, this paper presents a novel scheme that integrates bond graph modeling for fault signatures establishment, and a multivariate state estimation technique-based empirical estimation for residual generation followed by a Sequential Probability Ratio Test-based residual evaluation for monitoring alarm. Once a fault is detected and alerted, a synthesized non-null coherence vector is created, and then matched with the pre-designed fault signatures matrix to isolate possible faults. To identify the effectiveness of the proposed methodology, a simulation for pneumatic equalizer control unit of locomotive electronically controlled pneumatic brake is conducted. The experimental results show that satisfied performance of fault detection and isolation can be obtained with lower miss alarm and timely response, which make it suitable for complex systems modeling and intelligent maintenance.
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Zheng, Lin, Liang Liang Wu, Xue Ming Gu, Z. Shi, and Andrew Ball. "Application of EKF for an Electro-Hydraulic Servo System Using Model-Based Approach." Applied Mechanics and Materials 385-386 (August 2013): 609–13. http://dx.doi.org/10.4028/www.scientific.net/amm.385-386.609.
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This paper works on extended Kalman filter (EKF) for model-based fault detection of an electro-hydraulic system to deal with stochastic behaviour during control. A mathematical model of an electro-hydraulic system is developed. Some faults are introduced to evaluate the EKF fault detection method. Comparison of the EKF estimation accuracy and a linearised model-based accuracy shows the advantage of the EKF.
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Sun, Qing, Shunyi Zhao, and Yanyan Yin. "Optimal State Estimation for Discrete-Time Markov Jump Systems with Missing Observations." Abstract and Applied Analysis 2014 (2014): 1–11. http://dx.doi.org/10.1155/2014/568252.
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This paper is concerned with the optimal linear estimation for a class of direct-time Markov jump systems with missing observations. An observer-based approach of fault detection and isolation (FDI) is investigated as a detection mechanic of fault case. For systems with known information, a conditional prediction of observations is applied and fault observations are replaced and isolated; then, an FDI linear minimum mean square error estimation (LMMSE) can be developed by comprehensive utilizing of the correct information offered by systems. A recursive equation of filtering based on the geometric arguments can be obtained. Meanwhile, a stability of the state estimator will be guaranteed under appropriate assumption.
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Fu, Ke Chang, Peng Liu, Zhu Ming, and Shi Qi Jiang. "Sensor Fault Detection and Estimation for Nonlinear Process with Time-Delayed Variables." Applied Mechanics and Materials 182-183 (June 2012): 1435–39. http://dx.doi.org/10.4028/www.scientific.net/amm.182-183.1435.
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Time-delayed variables is of potentially critical impact on product quality within the processes of Metallurgical industry, Aviation industry and Chemical industry. It is very important to detect the sensor fault and estimate the values of time-delayed variables immediately as soon as faults occurred. Any fault of the time-delayed variable implies that a detrimental effect on the process will go unnoticed until the next sample become available. During this time, of course, poor quality product continues to be manufactured, a situation which is clearly unacceptable from a cost-effectiveness point of view. For nonlinear process with time-delayed variables, kernel partial least squares is introduced to monitor and estimate the fault of the Tennessee Eastman benchmark. Simulation results demonstrate the effectiveness of the proposed algorithm.
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Treetrong, Juggrapong. "Fault Detection of Electric Motors Application Using ML Estimation Method." Advanced Materials Research 591-593 (November 2012): 1958–61. http://dx.doi.org/10.4028/www.scientific.net/amr.591-593.1958.
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This paper proposes a new method of motor fault detection. ML Estimation is proposed as a key technique for signal processing. The stator current is used data for motor fault analysis. ML Estimation is generally applied to estimate signals for nonlinear model. The expectation is that the method can provide information for fault analysis. The method is tested on 3 different motor conditions: healthy, stator fault, and rotor fault motor at full load condition. Based on experiments, the method can differentiate conditions clearly and be also able to measure fault severity levels.
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Dong, Hongli, Zidong Wang, Steven X. Ding, and Huijun Gao. "A Survey on Distributed Filtering and Fault Detection for Sensor Networks." Mathematical Problems in Engineering 2014 (2014): 1–7. http://dx.doi.org/10.1155/2014/858624.
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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.
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Azzoug, Younes, Remus Pusca, Mohamed Sahraoui, Abdelkarim Ammar, Tarek Ameid, Raphael Romary, and Antonio J. Marques Cardoso. "An Active Fault-Tolerant Control Strategy for Current Sensors Failure for Induction Motor Drives Using a Single Observer for Currents Estimation and Axes Transformation." European Journal of Electrical Engineering 23, no. 6 (December 31, 2021): 467–74. http://dx.doi.org/10.18280/ejee.230606.
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This paper proposes a fault-tolerant control technique against current sensors failure in direct torque controlled induction motors drives, based on a new modification of Luenberger observer for currents estimation and axes transformation for vector rotation. Several important aspects are covered in the proposed algorithm, such as the detection of sensors failure, the isolation of faulty sensors, and the reconfiguration of the control system by a correct estimation. A logic circuit ensures fault detection by analyzing the residual signal between the measured and estimated quantities, while a single observer performs the task of estimating the line currents. In addition, a decision logic circuit isolates the erroneous signal and simultaneously selects the appropriate estimated current signal. An axes transformation ensures rotation from (a,b) to (α,β), which keeps a low-cost control using only two current sensors. The proposed scheme is tested on MATLAB/Simulink environment and experimentally validated in a laboratory prototype mainly containing a dS1104 card and 4 kW induction motor.
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Shabbir, Wasif, Li Aijun, Muhammad Taimoor, and Cui Yuwei. "Global fast terminal sliding mode based radial basis function neural network for accurate fault estimation in nonlinear systems." Journal of Intelligent & Fuzzy Systems 42, no. 3 (February 2, 2022): 2229–45. http://dx.doi.org/10.3233/jifs-211547.
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The problem of quick and accurate fault estimation in nonlinear systems is addressed in this article by combining the technique of radial basis function neural network (RBFNN) and global fast terminal sliding mode control (GFTSMC) concept. A new strategy to update the neural network weights, by using the global fast terminal sliding surface instead of conventional error back propagation method, is introduced to achieve real time, quick and accurate fault estimation which is critical for fault tolerant control system design. The combination of online learning ability of RBFNN, to approximate any nonlinear function, and finite time convergence property of GFTSMC ensures quick detection and accurate estimation of faults in real time. The effectiveness of the proposed strategy is demonstrated through simulations using a nonlinear model of a commercial aircraft and considering a wide range of sensors and actuators faults. The simulation results show that the proposed method is capable of quick and accurate online fault estimation in nonlinear systems and shows improved performance as compared to conventional RBFNN and other techniques existing in literature.