The stable and reliable operation of grid-integrated renewable energy systems requires advanced control and coordination of grid-side converters(GSCs),utilizing the feedback measurements of voltage and current sensors...The stable and reliable operation of grid-integrated renewable energy systems requires advanced control and coordination of grid-side converters(GSCs),utilizing the feedback measurements of voltage and current sensors from both the direct current(DC)and alternating current(AC)sides of the converter.However,the effective operation of the converter is susceptible to sensor failures or divergence from their proper operation.Although sensor fault detection algorithms are usually effective under abrupt faults,the fault propagation effect caused by the physical interconnection between the DC and AC sides of the converter may limit the performance of the sensor fault isolation process in revealing the exact location of a potential faulty sensor.Therefore,this work proposes a robust,model-based fault isolation and accommodation scheme.Specifically,a synergistic sensor fault isolation framework based on adaptive estimation schemes is proposed for both single and multiple faults in the DC voltage and AC current sensors,considering modeling uncertainty and measurement noise.The performance analysis in terms of stability,learning capability,and fault isolability is rigorously examined.An accommodation scheme based on a virtual sensor utilizing dynamic sensor fault estimation with realtime learning capabilities is applied to a GSC.Finally,the performance of the proposed fault isolation and accommodation scheme is evaluated through simulation analysis under several scenarios involving single and multiple sensor faults.展开更多
Fault isolation in dynamical systems is a challenging task due to modeling uncertainty and measurement noise,interactive effects of multiple faults and fault propagation.This paper proposes a unified approach for isol...Fault isolation in dynamical systems is a challenging task due to modeling uncertainty and measurement noise,interactive effects of multiple faults and fault propagation.This paper proposes a unified approach for isolation of multiple actuator or sensor faults in a class of nonlinear uncertain dynamical systems.Actuator and sensor fault isolation are accomplished in two independent modules,that monitor the system and are able to isolate the potential faulty actuator(s)or sensor(s).For the sensor fault isolation(SFI)case,a module is designed which monitors the system and utilizes an adaptive isolation threshold on the output residuals computed via a nonlinear estimation scheme that allows the isolation of single/multiple faulty sensor(s).For the actuator fault isolation(AFI)case,a second module is designed,which utilizes a learning-based scheme for adaptive approximation of faulty actuator(s)and,based on a reasoning decision logic and suitably designed AFI thresholds,the faulty actuator(s)set can be determined.The effectiveness of the proposed fault isolation approach developed in this paper is demonstrated through a simulation example.展开更多
A duty in development of an on-line fault detection algorithm is to make it associate with estimation of engine s health degradation. For this purpose,an on-line diagnostic algorithm is put forward. Using a tracking f...A duty in development of an on-line fault detection algorithm is to make it associate with estimation of engine s health degradation. For this purpose,an on-line diagnostic algorithm is put forward. Using a tracking filter to estimate the engine s health condition over its lifetime,can be reconstructed an onboard model,which is then made to match a real aircraft gas turbine engine. Finally,a bank of Kalman filters is applied in fault detection and isola-tion (FDI) of sensors for the engine. Through the bank...展开更多
A new sensor fault diagnosis method based on structured kernel principal component analysis （KPCA） is proposed for nonlinear processes. By performing KPCA on subsets of variables, a set of structured residuals, i.e....A new sensor fault diagnosis method based on structured kernel principal component analysis （KPCA） is proposed for nonlinear processes. By performing KPCA on subsets of variables, a set of structured residuals, i.e., scaled powers of KPCA, can be obtained in the same way as partial PCA. The structured residuals are utilized in composing an isolation scheme for sensor fault diagnosis, according to a properly designed incidence matrix. Sensor fault sensitivity and critical sensitivity are defined, based on which an incidence matrix optimization algorithm is proposed to improve the performance of the structured KPCA. The effectiveness of the proposed method is demonstrated on the simulated continuous stirred tank reactor （CSTR） process.展开更多
Component failures can cause multi-agent system(MAS)performance degradation and even disasters,which provokes the demand of the fault diagnosis method.A distributed sliding mode observer-based fault diagnosis method f...Component failures can cause multi-agent system(MAS)performance degradation and even disasters,which provokes the demand of the fault diagnosis method.A distributed sliding mode observer-based fault diagnosis method for MAS is developed in presence of actuator and sensor faults.Firstly,the actuator and sensor faults are extended to the system state,and the system is transformed into a descriptor system form.Then,a sliding mode-based distributed unknown input observer is proposed to estimate the extended state.Furthermore,adaptive laws are introduced to adjust the observer parameters.Finally,the effectiveness of the proposed method is demonstrated with numerical simulations.展开更多
This paper proposes a novel scoring index for the early sensor fault detection in order to make full use of massive archived spacecraft telemetry data.The early detection of sensor faults is made by using the index co...This paper proposes a novel scoring index for the early sensor fault detection in order to make full use of massive archived spacecraft telemetry data.The early detection of sensor faults is made by using the index constructed by the K-means algorithm and PCA model.The sensor fault detection includes the learning phase and monitoring phase.The amplitude of sensor fault has been always increasing when the performance of sensors deteriorates during a period.The proposed index can detect the smaller sensor faults than the squared prediction error( SPE) index which means it can discover the sensor faults earlier than the later.The simulation results demonstrate the effectiveness and feasibility of the proposed index which can decrease the check-limit as much as 40% than SPE in the same magnitude of bias sensor fault.展开更多
This paper deals with the problem of the state estimation and the sensor faults detection for nonlinear perturbed systems described by Takagi-Sugeno （T-S） fuzzy models with unmeasurable premise variables. Indeed, a ...This paper deals with the problem of the state estimation and the sensor faults detection for nonlinear perturbed systems described by Takagi-Sugeno （T-S） fuzzy models with unmeasurable premise variables. Indeed, a T-S observer is synthesized, in descriptor form, to estimate both the system states and the sensor faults simultaneously. The idea of the proposed approach is to introduce the sensor fault as an auxiliary variable in the state vector. Besides, the T-S model with unmeasurable premise variables is reduced to a perturbed model with measurable variables. Convergence conditions are established with Lyapunov theory and the H∞ performance in order to guarantee the best robustness to disturbances. These conditions are expressed in terms of linear matrix inequalities （LMIs）. The parameters of the observer are computed using the solution of the LMI conditions. Finally, a numerical example is given to illustrate the design procedures. Simulation results show the satisfactory performances.展开更多
In this paper, a robust sensor fault diagnosis observer with non-singular structure is proposed for a class of linear sampled-data descriptor system with state time-vary delay. Firstly, a sampled-data descriptor model...In this paper, a robust sensor fault diagnosis observer with non-singular structure is proposed for a class of linear sampled-data descriptor system with state time-vary delay. Firstly, a sampled-data descriptor model with time-vary delay is proposed and transformed into a discrete-time non-singular one. Then, a robust sensor fault diagnosis observer is proposed based on the state estimation error and the measurement residual, this observer can guarantee the robustness of the residual against the augmented disturbance and the sensor fault, which means the H∞ performance index is satisfied. As the confining matrix of the designed observer parameters does not meet the Linear Matrix Inequality (LMI), a cone complementary linearization (CCL) algorithm is proposed to solve this problem. The decision logic of the residual is obtained by the residual evaluation function. Simulation results show the effectiveness of the method.展开更多
The topology and property of Autoassociative Neural Networks(AANN) and theAANN's application to sensor fault diagnosis and reconstruction of engine control system arestudied. The key feature of AANN is feature ext...The topology and property of Autoassociative Neural Networks(AANN) and theAANN's application to sensor fault diagnosis and reconstruction of engine control system arestudied. The key feature of AANN is feature extract and noise filtering. Sensor fault detection isaccomplished by integrating the optimal estimation and fault detection logic. Digital simulationshows that the scheme can detect hard and soft failures of sensors at the absence of models forengines which have performance deteriorate in the service life, and can provide good analyticalredundancy.展开更多
To diagnose the fault of attitude sensors in satellites, this paper proposes a novel approach based on the Kalman filter of the discrete-time descriptor system. By regarding the sensor fault term as the auxiliary stat...To diagnose the fault of attitude sensors in satellites, this paper proposes a novel approach based on the Kalman filter of the discrete-time descriptor system. By regarding the sensor fault term as the auxiliary state vector, the attitude measurement system subjected to the attitude sensor fault is modeled by the discrete-time descriptor system. The condition of estimability of such systems is given. And then a Kalman filter of the discrete-time descriptor system is established based on the methodology of the maximum likelihood estimation. With the descriptor Kalman filter, the state vector of the original system and sensor fault can be estimated simultaneously. The proposed method is able to esti-mate an abrupt sensor fault as well as the incipient one. Moreover, it is also effective in the multiple faults scenario. Simulations are conducted to confirm the effectiveness of the proposed method.展开更多
This paper presents a fault diagnosis method for process faults and sensor faults in a class of nonlinear uncertain systems.The fault detection and isolation architecture consists of a fault detection estimator and a ...This paper presents a fault diagnosis method for process faults and sensor faults in a class of nonlinear uncertain systems.The fault detection and isolation architecture consists of a fault detection estimator and a bank of adaptive isolation estimators,each corresponding to a particular fault type.Adaptive thresholds for fault detection and isolation are presented.Fault detectability conditions characterizing the class of process faults and sensor faults that are detectable by the presented method are derived.A simulation example of robotic arm is used to illustrate the effectiveness of the fault diagnosis method.展开更多
This Paper presents a methodology for solving the sensor failure detection, isolation and accommodation of aeroengine control systems using on line learning neural networks(NN), which has one main NN and a set of dec...This Paper presents a methodology for solving the sensor failure detection, isolation and accommodation of aeroengine control systems using on line learning neural networks(NN), which has one main NN and a set of decentralized NNs. Changes in the system dynamics are monitored by the on line learning NN. When a failure occurs in some sensor, the sensor failure detection can be accomplished with high precision, and the sensor failure accommodation can be achieved by replacing the value from the failed sensor with its estimate from the decentralized NN. By integrating the optimal estimation and failure logic, this method can detect soft failures. Simulation of one kind of turboshaft engine control system with this multiple neural network architecture shows that the ANN developed can detect and isolate hard and soft sensor failures timely and provide accurate accommodation.展开更多
The paper considers a class of interconnected nonlinear systems with unknown sensor faults and uncertain interactions.Each subsystem is subject not only to the local sensor faults but also to the possible effects of f...The paper considers a class of interconnected nonlinear systems with unknown sensor faults and uncertain interactions.Each subsystem is subject not only to the local sensor faults but also to the possible effects of faults from other subsystems through uncertain interactions.Both multiplicative and time-varying additive sensor faults are taken into consideration,which are allowed to be unknown.A decentralized adaptive fault-tolerant control(FTC)scheme has been established.To eliminate the effects of faults in the control loop,several auxiliary quantities are constructed wisely and estimated by the designed adaptive mechanism.A smooth function is proposed,by which the uncertain interactions can be compensated even if they are coupled with sensor faults.It is proved that,by using only the corrupted states,all the closed-loop signals are globally uniformly bounded,and the output tracking error converges into an adjustable residual set.Finally,simulation and comparison studies are presented to illustrate the effectiveness of the proposed scheme.展开更多
As the energy crisis intensifies,the organic Rankine cycle(ORC)is increasingly employed for efficient recovery of low-temperature waste heat.The operation of the ORC system necessitates the use of numerous sensors to ...As the energy crisis intensifies,the organic Rankine cycle(ORC)is increasingly employed for efficient recovery of low-temperature waste heat.The operation of the ORC system necessitates the use of numerous sensors to monitor its status.Over time,these sensors may become faulty,rendering accurate and timely diagnosis is critical for proper and safe functioning of the ORC system.Currently,there is a lack of rapid diagnostic methods for sensor faults in ORC systems.This study establishes an ORC test bench utilizing cyclopentane as the working fluid.Experimental data incorporating induced faults from the ORC test bench is employed to train machine learning-based models for sensor fault diagnosis.The test results indicate that the diagnostic model developed in this study can accurately diagnose various sensor faults in the ORC system,thereby ensuring its safe operation.Notably,the method based on Bayesian-optimized long short-term memory network(BO-LSTM)achieved the highest diagnostic accuracy,reaching up to 95.92%.展开更多
The widespread adoption of the Internet of Things (IoT) has transformed various sectors globally, making themmore intelligent and connected. However, this advancement comes with challenges related to the effectiveness...The widespread adoption of the Internet of Things (IoT) has transformed various sectors globally, making themmore intelligent and connected. However, this advancement comes with challenges related to the effectiveness ofIoT devices. These devices, present in offices, homes, industries, and more, need constant monitoring to ensuretheir proper functionality. The success of smart systems relies on their seamless operation and ability to handlefaults. Sensors, crucial components of these systems, gather data and contribute to their functionality. Therefore,sensor faults can compromise the system’s reliability and undermine the trustworthiness of smart environments.To address these concerns, various techniques and algorithms can be employed to enhance the performance ofIoT devices through effective fault detection. This paper conducted a thorough review of the existing literature andconducted a detailed analysis.This analysis effectively links sensor errors with a prominent fault detection techniquecapable of addressing them. This study is innovative because it paves theway for future researchers to explore errorsthat have not yet been tackled by existing fault detection methods. Significant, the paper, also highlights essentialfactors for selecting and adopting fault detection techniques, as well as the characteristics of datasets and theircorresponding recommended techniques. Additionally, the paper presents amethodical overview of fault detectiontechniques employed in smart devices, including themetrics used for evaluation. Furthermore, the paper examinesthe body of academic work related to sensor faults and fault detection techniques within the domain. This reflectsthe growing inclination and scholarly attention of researchers and academicians toward strategies for fault detectionwithin the realm of the Internet of Things.展开更多
Batterymanagement systems(BMSs)are essential in ensuring the safe and stable operation of lithium-ion batteries(LIBs)in electric vehicles(EVs).Accurate sensor signals,particularly voltage,current,and temperature senso...Batterymanagement systems(BMSs)are essential in ensuring the safe and stable operation of lithium-ion batteries(LIBs)in electric vehicles(EVs).Accurate sensor signals,particularly voltage,current,and temperature sensor signals,are essential for a BMS to performfunctions such as state estimation,balance control,and fault diagnosis.The smooth operation of a BMS depends primarily on sensor signals,which provide current,voltage,and temperature information to maintain the battery pack in a safe running state.However,sensor failures and inaccurate measurement data can easily occur because of external interference and complex operating conditions.Therefore,an investigation into the faultdiagnosis of battery sensors and fault-tolerant control(FTC)is necessary to ensure the normal operation of a BMS.This paper analyzes themodes of sensor faults,fault diagnosismethods,and fault-tolerant control techniques.First,the differentmodes of sensor faults are analyzed,andmathematical expressions for these faults are provided.Second,diagnostic methods for sensor faults based on models,signal processing,and data-drivenmethods are analyzed in detail.Finally,FTC techniques are introduced to ensure stable sensor operation.Based on an analysis of the research status of sensor fault diagnosis,a new development direction for sensor fault diagnosis is proposed.展开更多
Aircraft engine component and sensor fault detection and isolation approach was proposed,which included fault type detection module and component-sensor simultaneous fault isolation module.The approach can not only di...Aircraft engine component and sensor fault detection and isolation approach was proposed,which included fault type detection module and component-sensor simultaneous fault isolation module.The approach can not only distinguish among sensor fault,component fault and component-sensor simultaneous fault,but also isolate and locate sensor fault and the type of engine component fault when the engine component fault and the sensor faults occur simultaneously.The double-threshold mechanism has been proposed,in which the fault diagnostic threshold changed with the sensor type and the engine condition,and it greatly improved the accuracy and robustness of sensor fault diagnosis system.Simulation results show that the approach proposed can diagnose and isolate the sensor and engine component fault with improved accuracy.It effectively improves the fault diagnosis ability of aircraft engine.展开更多
This paper addresses the gas path component and sensor fault diagnosis and isolation(FDI) for the auxiliary power unit(APU). A nonlinear dynamic model and a distributed state estimator are combined for the distributed...This paper addresses the gas path component and sensor fault diagnosis and isolation(FDI) for the auxiliary power unit(APU). A nonlinear dynamic model and a distributed state estimator are combined for the distributed control system. The distributed extended Kalman filter(DEKF)is served as a state estimator,which is utilized to estimate the gas path components’ flow capacity. The DEKF includes one main filter and five sub-filter groups related to five sensors of APU and each sub-filter yields local state flow capacity. The main filter collects and fuses the local state information,and then the state estimations are feedback to the sub-filters. The packet loss model is introduced in the DEKF algorithm in the APU distributed control architecture. FDI strategy with a performance index named weight sum of squared residuals(WSSR) is designed and used to identify the APU sensor fault by removing one sub-filter each time. The very sensor fault occurs as its performance index WSSR is different from the remaining sub-filter combinations. And the estimated value of the soft redundancy replaces the fault sensor measurement to isolate the fault measurement. It is worth noting that the proposed approach serves for not only the sensor failure but also the hybrid fault issue of APU gas path components and sensors. The simulation and comparison are systematically carried out by using the APU test data,and the superiority of the proposed methodology is verified.展开更多
Statistical data analysis and visualization approaches to identify ship speed power performance under relative wind(i.e.apparent wind)profiles are considered in this study.Ship performance and navigation data of a sel...Statistical data analysis and visualization approaches to identify ship speed power performance under relative wind(i.e.apparent wind)profiles are considered in this study.Ship performance and navigation data of a selected vessel are analyzed,where various data anomalies,i.e.sensor related erroneous data conditions,are identified.Those erroneous data conditions are investigated and several approaches to isolate such situations are also presented by considering appropriate data visualization methods.Then,the cleaned data are used to derive various relationships among ship performance and navigation parameters that have been visualized in this study,appropriately.The results show that the wind profiles along ship routes can be used to evaluate vessel performance and navigation conditions by assuming the respective sea states relate to their wind conditions.Hence,the results are useful to derive appropriate mathematical models that represent ship performance and navigation conditions.Such mathematical models can be used for weather routing type applications(i.e.voyage planning),where the respective weather forecast can be used to derive optimal ship routes to improve vessel performance and reduce fuel consumption.This study presents not only an overview of statistical data analysis of ship performance and navigation data but also the respective challenges in data anomalies(i.e.erroneous data intervals and sensor faults)due to onboard sensors and data handling systems.Furthermore,the respective solutions to such challenges in data quality have also been presented by considering data visualization approaches.展开更多
This paper addresses the problem on sensor fault estimation and fault-tolerant control for a class of Takagi-Sugeno Markovian jump systems,which are subjected to sensor faults and partially unknown transition rates.Fi...This paper addresses the problem on sensor fault estimation and fault-tolerant control for a class of Takagi-Sugeno Markovian jump systems,which are subjected to sensor faults and partially unknown transition rates.First,the original plant is extended to a descriptor system,where the original states and the sensor faults are assembled into the new state vector.Then,a novel reduced- order observer is designed for the extended system to simultaneously estimate the immeasurable states and sensor faults.Second,by using the estimated states obtained from the designed observer,a state- feedback fault-tolerant control strategy is developed to make the resulting closed-loop control system stochastically stable.Based on linear matrix inequality technique,algorithms are presented to compute the observer gains and control gains.The effectiveness of the proposed observer and controller are validated by a numerical example and a compared study,respectively,and the simulation results reveal that the proposed method can successfully estimate the sensor faults and guarantee the stochastic stability of the resulting closed-loop system.展开更多
基金supported in part by the Republic of Cyprus through the Research and Innovation Foundation under Project CULTURE/AWARD-YR/0322B/0003(INVERGE)in part by the Horizon Europe Research and Innovation Programme under Grant Agreement No. 101075747 (TRANSIT)supported by the European Union’s Horizon 2020 research and innovation programme under Grant Agreement No. 739551 (KIOS CoE) and from the Republic of Cyprus through the Deputy Ministry of Research,Innovation and Digital Policy
文摘The stable and reliable operation of grid-integrated renewable energy systems requires advanced control and coordination of grid-side converters(GSCs),utilizing the feedback measurements of voltage and current sensors from both the direct current(DC)and alternating current(AC)sides of the converter.However,the effective operation of the converter is susceptible to sensor failures or divergence from their proper operation.Although sensor fault detection algorithms are usually effective under abrupt faults,the fault propagation effect caused by the physical interconnection between the DC and AC sides of the converter may limit the performance of the sensor fault isolation process in revealing the exact location of a potential faulty sensor.Therefore,this work proposes a robust,model-based fault isolation and accommodation scheme.Specifically,a synergistic sensor fault isolation framework based on adaptive estimation schemes is proposed for both single and multiple faults in the DC voltage and AC current sensors,considering modeling uncertainty and measurement noise.The performance analysis in terms of stability,learning capability,and fault isolability is rigorously examined.An accommodation scheme based on a virtual sensor utilizing dynamic sensor fault estimation with realtime learning capabilities is applied to a GSC.Finally,the performance of the proposed fault isolation and accommodation scheme is evaluated through simulation analysis under several scenarios involving single and multiple sensor faults.
基金the European Research Council(ERC)under the ERC Synergy grant agreement No.951424(Water-Futures)the European Union’s Horizon 2020 research and innovation programme under grant agreement No.739551(KIOS CoE)the Government of the Republic of Cyprus through the Directorate General for European Programmes,Coordination and Development。
文摘Fault isolation in dynamical systems is a challenging task due to modeling uncertainty and measurement noise,interactive effects of multiple faults and fault propagation.This paper proposes a unified approach for isolation of multiple actuator or sensor faults in a class of nonlinear uncertain dynamical systems.Actuator and sensor fault isolation are accomplished in two independent modules,that monitor the system and are able to isolate the potential faulty actuator(s)or sensor(s).For the sensor fault isolation(SFI)case,a module is designed which monitors the system and utilizes an adaptive isolation threshold on the output residuals computed via a nonlinear estimation scheme that allows the isolation of single/multiple faulty sensor(s).For the actuator fault isolation(AFI)case,a second module is designed,which utilizes a learning-based scheme for adaptive approximation of faulty actuator(s)and,based on a reasoning decision logic and suitably designed AFI thresholds,the faulty actuator(s)set can be determined.The effectiveness of the proposed fault isolation approach developed in this paper is demonstrated through a simulation example.
文摘A duty in development of an on-line fault detection algorithm is to make it associate with estimation of engine s health degradation. For this purpose,an on-line diagnostic algorithm is put forward. Using a tracking filter to estimate the engine s health condition over its lifetime,can be reconstructed an onboard model,which is then made to match a real aircraft gas turbine engine. Finally,a bank of Kalman filters is applied in fault detection and isola-tion (FDI) of sensors for the engine. Through the bank...
基金supported by Scientific Reserch Fund of SiChuan Provincial Education Department (No.07ZB013)by the Scientific ResearchFoundation of CUIT (No.CSRF200704)
文摘A new sensor fault diagnosis method based on structured kernel principal component analysis （KPCA） is proposed for nonlinear processes. By performing KPCA on subsets of variables, a set of structured residuals, i.e., scaled powers of KPCA, can be obtained in the same way as partial PCA. The structured residuals are utilized in composing an isolation scheme for sensor fault diagnosis, according to a properly designed incidence matrix. Sensor fault sensitivity and critical sensitivity are defined, based on which an incidence matrix optimization algorithm is proposed to improve the performance of the structured KPCA. The effectiveness of the proposed method is demonstrated on the simulated continuous stirred tank reactor （CSTR） process.
基金supported by the National Natural Science Foundation of China(62020106003,62003162)111 project(B20007)+1 种基金the Natural Science Foundation of Jiangsu Province of China(BK20200416)the China Postdoctoral Science Foundation(2020TQ0151,2020M681590).
文摘Component failures can cause multi-agent system(MAS)performance degradation and even disasters,which provokes the demand of the fault diagnosis method.A distributed sliding mode observer-based fault diagnosis method for MAS is developed in presence of actuator and sensor faults.Firstly,the actuator and sensor faults are extended to the system state,and the system is transformed into a descriptor system form.Then,a sliding mode-based distributed unknown input observer is proposed to estimate the extended state.Furthermore,adaptive laws are introduced to adjust the observer parameters.Finally,the effectiveness of the proposed method is demonstrated with numerical simulations.
基金Sponsored by the National Basic Research Program of China(Grant No.2012CB720003)
文摘This paper proposes a novel scoring index for the early sensor fault detection in order to make full use of massive archived spacecraft telemetry data.The early detection of sensor faults is made by using the index constructed by the K-means algorithm and PCA model.The sensor fault detection includes the learning phase and monitoring phase.The amplitude of sensor fault has been always increasing when the performance of sensors deteriorates during a period.The proposed index can detect the smaller sensor faults than the squared prediction error( SPE) index which means it can discover the sensor faults earlier than the later.The simulation results demonstrate the effectiveness and feasibility of the proposed index which can decrease the check-limit as much as 40% than SPE in the same magnitude of bias sensor fault.
文摘This paper deals with the problem of the state estimation and the sensor faults detection for nonlinear perturbed systems described by Takagi-Sugeno （T-S） fuzzy models with unmeasurable premise variables. Indeed, a T-S observer is synthesized, in descriptor form, to estimate both the system states and the sensor faults simultaneously. The idea of the proposed approach is to introduce the sensor fault as an auxiliary variable in the state vector. Besides, the T-S model with unmeasurable premise variables is reduced to a perturbed model with measurable variables. Convergence conditions are established with Lyapunov theory and the H∞ performance in order to guarantee the best robustness to disturbances. These conditions are expressed in terms of linear matrix inequalities （LMIs）. The parameters of the observer are computed using the solution of the LMI conditions. Finally, a numerical example is given to illustrate the design procedures. Simulation results show the satisfactory performances.
基金Sponsored by the National Natural Science Foundation of China(Grant No.61021002)
文摘In this paper, a robust sensor fault diagnosis observer with non-singular structure is proposed for a class of linear sampled-data descriptor system with state time-vary delay. Firstly, a sampled-data descriptor model with time-vary delay is proposed and transformed into a discrete-time non-singular one. Then, a robust sensor fault diagnosis observer is proposed based on the state estimation error and the measurement residual, this observer can guarantee the robustness of the residual against the augmented disturbance and the sensor fault, which means the H∞ performance index is satisfied. As the confining matrix of the designed observer parameters does not meet the Linear Matrix Inequality (LMI), a cone complementary linearization (CCL) algorithm is proposed to solve this problem. The decision logic of the residual is obtained by the residual evaluation function. Simulation results show the effectiveness of the method.
文摘The topology and property of Autoassociative Neural Networks(AANN) and theAANN's application to sensor fault diagnosis and reconstruction of engine control system arestudied. The key feature of AANN is feature extract and noise filtering. Sensor fault detection isaccomplished by integrating the optimal estimation and fault detection logic. Digital simulationshows that the scheme can detect hard and soft failures of sensors at the absence of models forengines which have performance deteriorate in the service life, and can provide good analyticalredundancy.
基金supported by the National Natural Science Foundation of China (60874054)
文摘To diagnose the fault of attitude sensors in satellites, this paper proposes a novel approach based on the Kalman filter of the discrete-time descriptor system. By regarding the sensor fault term as the auxiliary state vector, the attitude measurement system subjected to the attitude sensor fault is modeled by the discrete-time descriptor system. The condition of estimability of such systems is given. And then a Kalman filter of the discrete-time descriptor system is established based on the methodology of the maximum likelihood estimation. With the descriptor Kalman filter, the state vector of the original system and sensor fault can be estimated simultaneously. The proposed method is able to esti-mate an abrupt sensor fault as well as the incipient one. Moreover, it is also effective in the multiple faults scenario. Simulations are conducted to confirm the effectiveness of the proposed method.
文摘This paper presents a fault diagnosis method for process faults and sensor faults in a class of nonlinear uncertain systems.The fault detection and isolation architecture consists of a fault detection estimator and a bank of adaptive isolation estimators,each corresponding to a particular fault type.Adaptive thresholds for fault detection and isolation are presented.Fault detectability conditions characterizing the class of process faults and sensor faults that are detectable by the presented method are derived.A simulation example of robotic arm is used to illustrate the effectiveness of the fault diagnosis method.
文摘This Paper presents a methodology for solving the sensor failure detection, isolation and accommodation of aeroengine control systems using on line learning neural networks(NN), which has one main NN and a set of decentralized NNs. Changes in the system dynamics are monitored by the on line learning NN. When a failure occurs in some sensor, the sensor failure detection can be accomplished with high precision, and the sensor failure accommodation can be achieved by replacing the value from the failed sensor with its estimate from the decentralized NN. By integrating the optimal estimation and failure logic, this method can detect soft failures. Simulation of one kind of turboshaft engine control system with this multiple neural network architecture shows that the ANN developed can detect and isolate hard and soft sensor failures timely and provide accurate accommodation.
文摘The paper considers a class of interconnected nonlinear systems with unknown sensor faults and uncertain interactions.Each subsystem is subject not only to the local sensor faults but also to the possible effects of faults from other subsystems through uncertain interactions.Both multiplicative and time-varying additive sensor faults are taken into consideration,which are allowed to be unknown.A decentralized adaptive fault-tolerant control(FTC)scheme has been established.To eliminate the effects of faults in the control loop,several auxiliary quantities are constructed wisely and estimated by the designed adaptive mechanism.A smooth function is proposed,by which the uncertain interactions can be compensated even if they are coupled with sensor faults.It is proved that,by using only the corrupted states,all the closed-loop signals are globally uniformly bounded,and the output tracking error converges into an adjustable residual set.Finally,simulation and comparison studies are presented to illustrate the effectiveness of the proposed scheme.
基金supported by the National Key R&D Program of China(2024YFE0213200)supported by the Tianjin Municipal Science and Technology Bureau(23jCjQjC00260).
文摘As the energy crisis intensifies,the organic Rankine cycle(ORC)is increasingly employed for efficient recovery of low-temperature waste heat.The operation of the ORC system necessitates the use of numerous sensors to monitor its status.Over time,these sensors may become faulty,rendering accurate and timely diagnosis is critical for proper and safe functioning of the ORC system.Currently,there is a lack of rapid diagnostic methods for sensor faults in ORC systems.This study establishes an ORC test bench utilizing cyclopentane as the working fluid.Experimental data incorporating induced faults from the ORC test bench is employed to train machine learning-based models for sensor fault diagnosis.The test results indicate that the diagnostic model developed in this study can accurately diagnose various sensor faults in the ORC system,thereby ensuring its safe operation.Notably,the method based on Bayesian-optimized long short-term memory network(BO-LSTM)achieved the highest diagnostic accuracy,reaching up to 95.92%.
文摘The widespread adoption of the Internet of Things (IoT) has transformed various sectors globally, making themmore intelligent and connected. However, this advancement comes with challenges related to the effectiveness ofIoT devices. These devices, present in offices, homes, industries, and more, need constant monitoring to ensuretheir proper functionality. The success of smart systems relies on their seamless operation and ability to handlefaults. Sensors, crucial components of these systems, gather data and contribute to their functionality. Therefore,sensor faults can compromise the system’s reliability and undermine the trustworthiness of smart environments.To address these concerns, various techniques and algorithms can be employed to enhance the performance ofIoT devices through effective fault detection. This paper conducted a thorough review of the existing literature andconducted a detailed analysis.This analysis effectively links sensor errors with a prominent fault detection techniquecapable of addressing them. This study is innovative because it paves theway for future researchers to explore errorsthat have not yet been tackled by existing fault detection methods. Significant, the paper, also highlights essentialfactors for selecting and adopting fault detection techniques, as well as the characteristics of datasets and theircorresponding recommended techniques. Additionally, the paper presents amethodical overview of fault detectiontechniques employed in smart devices, including themetrics used for evaluation. Furthermore, the paper examinesthe body of academic work related to sensor faults and fault detection techniques within the domain. This reflectsthe growing inclination and scholarly attention of researchers and academicians toward strategies for fault detectionwithin the realm of the Internet of Things.
基金funded by the Key Industrial Chain Technology Research Program of Xi’an(24ZDCYJSGG0048)the Key Research and Development Program of Xianyang(L2023-ZDYF-SF-077)the Fundamental Research Funds for the Central Universities in Chang'an University(300102384201).
文摘Batterymanagement systems(BMSs)are essential in ensuring the safe and stable operation of lithium-ion batteries(LIBs)in electric vehicles(EVs).Accurate sensor signals,particularly voltage,current,and temperature sensor signals,are essential for a BMS to performfunctions such as state estimation,balance control,and fault diagnosis.The smooth operation of a BMS depends primarily on sensor signals,which provide current,voltage,and temperature information to maintain the battery pack in a safe running state.However,sensor failures and inaccurate measurement data can easily occur because of external interference and complex operating conditions.Therefore,an investigation into the faultdiagnosis of battery sensors and fault-tolerant control(FTC)is necessary to ensure the normal operation of a BMS.This paper analyzes themodes of sensor faults,fault diagnosismethods,and fault-tolerant control techniques.First,the differentmodes of sensor faults are analyzed,andmathematical expressions for these faults are provided.Second,diagnostic methods for sensor faults based on models,signal processing,and data-drivenmethods are analyzed in detail.Finally,FTC techniques are introduced to ensure stable sensor operation.Based on an analysis of the research status of sensor fault diagnosis,a new development direction for sensor fault diagnosis is proposed.
基金Program Sponsored for Scientific Innovation Research of College Graduate in Jiangsu Province(CX10B_108Z)
文摘Aircraft engine component and sensor fault detection and isolation approach was proposed,which included fault type detection module and component-sensor simultaneous fault isolation module.The approach can not only distinguish among sensor fault,component fault and component-sensor simultaneous fault,but also isolate and locate sensor fault and the type of engine component fault when the engine component fault and the sensor faults occur simultaneously.The double-threshold mechanism has been proposed,in which the fault diagnostic threshold changed with the sensor type and the engine condition,and it greatly improved the accuracy and robustness of sensor fault diagnosis system.Simulation results show that the approach proposed can diagnose and isolate the sensor and engine component fault with improved accuracy.It effectively improves the fault diagnosis ability of aircraft engine.
基金supported by the National Natural Science Foundation of China(No.91960110)the National Science and Technology Major Project(No. 2017-I0006-0007)the Fundamental Research Funds for the Central Universities(NP2022418)。
文摘This paper addresses the gas path component and sensor fault diagnosis and isolation(FDI) for the auxiliary power unit(APU). A nonlinear dynamic model and a distributed state estimator are combined for the distributed control system. The distributed extended Kalman filter(DEKF)is served as a state estimator,which is utilized to estimate the gas path components’ flow capacity. The DEKF includes one main filter and five sub-filter groups related to five sensors of APU and each sub-filter yields local state flow capacity. The main filter collects and fuses the local state information,and then the state estimations are feedback to the sub-filters. The packet loss model is introduced in the DEKF algorithm in the APU distributed control architecture. FDI strategy with a performance index named weight sum of squared residuals(WSSR) is designed and used to identify the APU sensor fault by removing one sub-filter each time. The very sensor fault occurs as its performance index WSSR is different from the remaining sub-filter combinations. And the estimated value of the soft redundancy replaces the fault sensor measurement to isolate the fault measurement. It is worth noting that the proposed approach serves for not only the sensor failure but also the hybrid fault issue of APU gas path components and sensors. The simulation and comparison are systematically carried out by using the APU test data,and the superiority of the proposed methodology is verified.
基金This work has been conducted under the project of“SFI Smart Maritime(237917/O30)-Norwegian Centre for im-proved energy-efficiency and reduced emissions from the mar-itime sector”that is partly funded by the Research Council of Norway.
文摘Statistical data analysis and visualization approaches to identify ship speed power performance under relative wind(i.e.apparent wind)profiles are considered in this study.Ship performance and navigation data of a selected vessel are analyzed,where various data anomalies,i.e.sensor related erroneous data conditions,are identified.Those erroneous data conditions are investigated and several approaches to isolate such situations are also presented by considering appropriate data visualization methods.Then,the cleaned data are used to derive various relationships among ship performance and navigation parameters that have been visualized in this study,appropriately.The results show that the wind profiles along ship routes can be used to evaluate vessel performance and navigation conditions by assuming the respective sea states relate to their wind conditions.Hence,the results are useful to derive appropriate mathematical models that represent ship performance and navigation conditions.Such mathematical models can be used for weather routing type applications(i.e.voyage planning),where the respective weather forecast can be used to derive optimal ship routes to improve vessel performance and reduce fuel consumption.This study presents not only an overview of statistical data analysis of ship performance and navigation data but also the respective challenges in data anomalies(i.e.erroneous data intervals and sensor faults)due to onboard sensors and data handling systems.Furthermore,the respective solutions to such challenges in data quality have also been presented by considering data visualization approaches.
基金supported by the National Natural Science Foundation under Grant No.61803256Shanghai Sailing Plan under Grant No.17YF1407300in part by the Talent Program of Shanghai University of Engineering Science
文摘This paper addresses the problem on sensor fault estimation and fault-tolerant control for a class of Takagi-Sugeno Markovian jump systems,which are subjected to sensor faults and partially unknown transition rates.First,the original plant is extended to a descriptor system,where the original states and the sensor faults are assembled into the new state vector.Then,a novel reduced- order observer is designed for the extended system to simultaneously estimate the immeasurable states and sensor faults.Second,by using the estimated states obtained from the designed observer,a state- feedback fault-tolerant control strategy is developed to make the resulting closed-loop control system stochastically stable.Based on linear matrix inequality technique,algorithms are presented to compute the observer gains and control gains.The effectiveness of the proposed observer and controller are validated by a numerical example and a compared study,respectively,and the simulation results reveal that the proposed method can successfully estimate the sensor faults and guarantee the stochastic stability of the resulting closed-loop system.
