This research aims to address the challenges of fault detection and isolation(FDI)in digital grids,focusing on improving the reliability and stability of power systems.Traditional fault detection techniques,such as ru...This research aims to address the challenges of fault detection and isolation(FDI)in digital grids,focusing on improving the reliability and stability of power systems.Traditional fault detection techniques,such as rule-based fuzzy systems and conventional FDI methods,often struggle with the dynamic nature of modern grids,resulting in delays and inaccuracies in fault classification.To overcome these limitations,this study introduces a Hybrid NeuroFuzzy Fault Detection Model that combines the adaptive learning capabilities of neural networks with the reasoning strength of fuzzy logic.The model’s performance was evaluated through extensive simulations on the IEEE 33-bus test system,considering various fault scenarios,including line-to-ground faults(LGF),three-phase short circuits(3PSC),and harmonic distortions(HD).The quantitative results show that the model achieves 97.2%accuracy,a false negative rate(FNR)of 1.9%,and a false positive rate(FPR)of 2.3%,demonstrating its high precision in fault diagnosis.The qualitative analysis further highlights the model’s adaptability and its potential for seamless integration into smart grids,micro grids,and renewable energy systems.By dynamically refining fuzzy inference rules,the model enhances fault detection efficiency without compromising computational feasibility.These findings contribute to the development of more resilient and adaptive fault management systems,paving the way for advanced smart grid technologies.展开更多
Test selection design(TSD)is an important technique for improving product maintainability,reliability and reducing lifecycle costs.In recent years,although some researchers have addressed the design problem of test se...Test selection design(TSD)is an important technique for improving product maintainability,reliability and reducing lifecycle costs.In recent years,although some researchers have addressed the design problem of test selection,the correlation between test outcomes has not been sufficiently considered in test metrics modeling.This study proposes a new approach that combines copula and D-Vine copula to address the correlation issue in TSD.First,the copula is utilized to model FIR on the joint distribution.Furthermore,the D-Vine copula is applied to model the FDR and FAR.Then,a particle swarm optimization is employed to select the optimal testing scheme.Finally,the efficacy of the proposed method is validated through experimentation on a negative feedback circuit.展开更多
In this paper, a geometric approach to fault detection and isolation （FDI） is applied to a Multiple-Input Multipie-Output （MIMO） model of a frame and the FDI results are compared to the ones obtained in the Single...In this paper, a geometric approach to fault detection and isolation （FDI） is applied to a Multiple-Input Multipie-Output （MIMO） model of a frame and the FDI results are compared to the ones obtained in the Single-Input Single-Output （SISO）, Multiple-Input Single-Output （MISO）, and Single-Input Multiple-Output （SIMO） cases. A proper distance function based on parameters obtained from parametric system identification method is used in the geometric approach. ARX （Auto Regressive with exogenous input） and VARX （Vector ARX） models with 12 parameters are used in all of the above-mentioned models. The obtained results reveal that by increasing the number of inputs, the classification errors reduce, even in the case of applying only one of the inputs in the computations. Furthermore, increasing the number of measured outputs in the FDI scheme results in decreasing classification errors. Also, it is shown that by using probabilistic space in the distance function, fault diagnosis scheme has better performance in comparison with the deterministic one.展开更多
An application of the multiobjective fault detection and isolation（FDI） approach to an air-breathing hypersonic vehicle（HSV） longitudinal dynamics subject to disturbances is presented.Maintaining sustainable and s...An application of the multiobjective fault detection and isolation（FDI） approach to an air-breathing hypersonic vehicle（HSV） longitudinal dynamics subject to disturbances is presented.Maintaining sustainable and safe flight of HSV is a challenging task due to its strong coupling effects,variable operating conditions and possible failures of system components.A common type of system faults for aircraft including HSV is the loss of effectiveness of its actuators and sensors.To detect and isolate multiple actuator/sensor failures,a faulty linear parameter-varying（LPV） model of HSV is derived by converting actuator/system component faults into equivalent sensor faults.Then a bank of LPV FDI observers is designed to track individual fault with minimum error and suppress the effects of disturbances and other fault signals.The simulation results based on the nonlinear flexible HSV model and a nominal LPV controller demonstrate the effectiveness of the fault estimation technique for HSV.展开更多
The complex systems are often in the structure of multi-operating modes, and the components implementing system functions are different under different operation modes, which results in the problems that components of...The complex systems are often in the structure of multi-operating modes, and the components implementing system functions are different under different operation modes, which results in the problems that components often fail in different operating modes, faults can be only detected in specified operating modes, tests can be available in specified operating modes,and the cost and efficiency of detecting and isolating faults are different under different operating modes and isolation levels. Aiming at these problems, an optimal test selection method for fault detection and isolation in the multi-operating mode system is proposed by using the fault pair coding and rollout algorithm. Firstly,the faults in fault-test correlation matrices under different operating modes are combined to fault-pairs, which is used to construct the fault pair-test correlation matrices under different operating modes.Secondly, the final fault pair-test correlation matrix of the multioperating mode system is obtained by operating the fault pair-test correlation matrices under different operating modes. Based on the final fault pair-test correlation matrix, the necessary tests are selected by the rollout algorithm orderly. Finally, the effectiveness of the proposed method is verified by examples of the optimal test selection in the multi-operating mode system with faults isolated to different levels. The result shows that the proposed method can effectively mine the fault detection and isolation ability of tests and it is suitable for the optimal test selection of the multi-operating mode system with faults isolated to the replacement unit and specific fault.展开更多
In chemical process, a large number of measured and manipulated variables are highly correlated. Principal component analysis(PCA) is widely applied as a dimension reduction technique for capturing strong correlation ...In chemical process, a large number of measured and manipulated variables are highly correlated. Principal component analysis(PCA) is widely applied as a dimension reduction technique for capturing strong correlation underlying in the process measurements. However, it is difficult for PCA based fault detection results to be interpreted physically and to provide support for isolation. Some approaches incorporating process knowledge are developed, but the information is always shortage and deficient in practice. Therefore, this work proposes an adaptive partitioning PCA algorithm entirely based on operation data. The process feature space is partitioned into several sub-feature spaces. Constructed sub-block models can not only reflect the local behavior of process change, namely to grasp the intrinsic local information underlying the process changes, but also improve the fault detection and isolation through the combination of local fault detection results and reduction of smearing effect.The method is demonstrated in TE process, and the results show that the new method is much better in fault detection and isolation compared to conventional PCA method.展开更多
State reconstruction approach is very useful for sensor fault isolation, reconstruction of faulty measurement and the determination of the number of components retained in the principal components analysis （PCA） mod...State reconstruction approach is very useful for sensor fault isolation, reconstruction of faulty measurement and the determination of the number of components retained in the principal components analysis （PCA） model. An extension of this approach based on a Nonlinear PCA （NLPCA） model is described in this paper. The NLPCA model is obtained using five layer neural network. A simulation example is given to show the performances of the proposed approach.展开更多
Multi-way principal component analysis（MPCA）has received considerable attention and been widely used in process monitoring.A traditional MPCA algorithm unfolds multiple batches of historical data into a two-dimensio...Multi-way principal component analysis（MPCA）has received considerable attention and been widely used in process monitoring.A traditional MPCA algorithm unfolds multiple batches of historical data into a two-dimensional matrix and cut the matrix along the time axis to form subspaces.However,low efficiency of subspaces and difficult fault isolation are the common disadvantages for the principal component model.This paper presents a new subspace construction method based on kernel density estimation function that can effectively reduce the storage amount of the subspace information.The MPCA model and the knowledge base are built based on the new subspace.Then,fault detection and isolation with the squared prediction error（SPE）statistic and the Hotelling（T2）statistic are also realized in process monitoring.When a fault occurs,fault isolation based on the SPE statistic is achieved by residual contribution analysis of different variables.For fault isolation of subspace based on the T2 statistic,the relationship between the statistic indicator and state variables is constructed,and the constraint conditions are presented to check the validity of fault isolation.Then,to improve the robustness of fault isolation to unexpected disturbances,the statistic method is adopted to set the relation between single subspace and multiple subspaces to increase the corrective rate of fault isolation.Finally fault detection and isolation based on the improved MPCA is used to monitor the automatic shift control system（ASCS）to prove the correctness and effectiveness of the algorithm.The research proposes a new subspace construction method to reduce the required storage capacity and to prove the robustness of the principal component model,and sets the relationship between the state variables and fault detection indicators for fault isolation.展开更多
A robust nonlinear analytical redundancy （RNLAR） technique is presented to detect and isolate actuator and sensor faults in a mobile robot. Both model-plant-mismatch （MPM） and process disturbance are considered du...A robust nonlinear analytical redundancy （RNLAR） technique is presented to detect and isolate actuator and sensor faults in a mobile robot. Both model-plant-mismatch （MPM） and process disturbance are considered during fault detection. The RNLAR is used to design primary residual vectors （PRV）, which are highly sensitive to the faults and less sensitive to MPM and process disturbance, for sensor and actuator fault detection. The PRVs are then transformed into a set of structured residual vectors （SRV） for fault isolation. Experimental results on a Pioneer 3-DX mobile robot are presented to justify the effectiveness of the RNLAR scheme.展开更多
This work proposes a robust fault detection and isolation scheme for discrete-time systems subject to actuator faults,in which a bank of H_/H∞fault detection unknown input observers(UIOs)and a zonotopic threshold ana...This work proposes a robust fault detection and isolation scheme for discrete-time systems subject to actuator faults,in which a bank of H_/H∞fault detection unknown input observers(UIOs)and a zonotopic threshold analysis strategy are considered.In observer design,finite-frequency H_index based on the generalized Kalman-Yakubovich-Popov lemma and H∞technique are utilized to evaluate worst-case fault sensitivity and disturbance attenuation performance,respectively.The proposed H_/H∞fault detection observers are designed to be insensitive to the corresponding actuator fault only,but sensitive to others.Then,to overcome the weakness of predefining threshold for FDI decision-making,this work proposes a zonotopic threshold analysis method to evaluate the generated residuals.The FDI decision-making relies on the evaluation with a dynamical zonotopic threshold.Finally,numerical simulations are provided to show the feasibility of the proposed scheme.展开更多
A Fault detection and isolation(FDI)scheme for discrete time-delay system is proposed in this paper,which can not only detect but also isolate the faults.A time delay operator is introduced to resolve the problem bro...A Fault detection and isolation(FDI)scheme for discrete time-delay system is proposed in this paper,which can not only detect but also isolate the faults.A time delay operator is introduced to resolve the problem brought by the time-delay system.The design and computation for the FDI system is carried by computer math tool Maple,which can easily deal with the symbolic computation.Residuals in the form of parity space can be deduced from the recursion of the system equations.Further more,a generalized residual set is created using the freedom of the parity space redundancy.Thus,both fault detection and fault isolation have been accomplished.The proposed method has been verified by a numerical example.展开更多
The diagnoses in industrial systems represent an important economic objective in process industrial automation area. To guarantee the safety and the continuity in production exploitation and to record the useful event...The diagnoses in industrial systems represent an important economic objective in process industrial automation area. To guarantee the safety and the continuity in production exploitation and to record the useful events with the feedback experience for the curative maintenance. We propose in this work to examine and illustrate the application ability of the spectral analysis approach, in the area of fault detection and isolation industrial systems. In this work, we use a combined analysis diagram of time-frequency, in order to make this approach exploitable in the proposed supervision strategy with decision making module. The obtained results, show clearly how to guarantee a reliable and sure exploitation in industrial system, thus allowing better performances at the time of its exploitation on the supervision strategy.展开更多
Reliability analysis of a leak detection system developed by OSYRIS R＆D is dealed with in this paper. The developed algorithm is based on signal processing theory; and it uses the properties of the cross-correlation ...Reliability analysis of a leak detection system developed by OSYRIS R＆D is dealed with in this paper. The developed algorithm is based on signal processing theory; and it uses the properties of the cross-correlation function in order to distinguish the fluid leak from a various disturbances. Experimental results obtained on different processes, in presence of thermal and hydraulic disturbances, show the advantages and limits of the proposed approach.展开更多
Support vector machines and a Kalman-like observer are used for fault detection and isolation in a variable speed horizontalaxis wind turbine composed of three blades and a full converter. The support vector approach ...Support vector machines and a Kalman-like observer are used for fault detection and isolation in a variable speed horizontalaxis wind turbine composed of three blades and a full converter. The support vector approach is data-based and is therefore robust to process knowledge. It is based on structural risk minimization which enhances generalization even with small training data set and it allows for process nonlinearity by using flexible kernels. In this work, a radial basis function is used as the kernel. Different parts of the process are investigated including actuators and sensors faults. With duplicated sensors, sensor faults in blade pitch positions,generator and rotor speeds can be detected. Faults of type stuck measurements can be detected in 2 sampling periods. The detection time of offset/scaled measurements depends on the severity of the fault and on the process dynamics when the fault occurs. The converter torque actuator fault can be detected within 2 sampling periods. Faults in the actuators of the pitch systems represents a higher difficulty for fault detection which is due to the fact that such faults only affect the transitory state(which is very fast) but not the final stationary state. Therefore, two methods are considered and compared for fault detection and isolation of this fault: support vector machines and a Kalman-like observer. Advantages and disadvantages of each method are discussed. On one hand, support vector machines training of transitory states would require a big amount of data in different situations, but the fault detection and isolation results are robust to variations in the input/operating point. On the other hand, the observer is model-based, and therefore does not require training, and it allows identification of the fault level, which is interesting for fault reconfiguration. But the observability of the system is ensured under specific conditions, related to the dynamics of the inputs and outputs. The whole fault detection and isolation scheme is evaluated using a wind turbine benchmark with a real sequence of wind speed.展开更多
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
Three-phase grid-connected inverters(GCIs)are essential components in distributed generation systems,where the accuracy of current measurement circuits is fundamental for reliable closed-loop operation.Nevertheless,th...Three-phase grid-connected inverters(GCIs)are essential components in distributed generation systems,where the accuracy of current measurement circuits is fundamental for reliable closed-loop operation.Nevertheless,the presence of a DC offset in the measured current can disrupt the regulation of grid currents and significantly degrade system performance.In this work,a fault-tolerant control approach is introduced to counteract the impact of such offset faults through a dedicated current compensation mechanism.The proposed solution is built around two main stages:(i)detecting and isolating DC offset faults that may appear in one or multiple phases of the measured grid currents,and(ii)estimating the fault magnitude and reconstructing the corrected current signal.The offset magnitude is obtained analytically by examining the grid current projected onto the synchronous d-axis at the grid angular frequency,eliminating the need for any additional sensing hardware.Simulation and experimental investigations conducted under several fault scenarios confirm the robustness of the proposed strategy and highlight significant improvements in detection speed and diagnostic accuracy.展开更多
The primary objective of this study is to explore novel applications of data-driven machine learning methods for isolation of nonlinear systems with a case study for an in-orbit closed-loop controlled satellite with r...The primary objective of this study is to explore novel applications of data-driven machine learning methods for isolation of nonlinear systems with a case study for an in-orbit closed-loop controlled satellite with reaction wheels as actuators.Highfidelity models of the three-axis controlled satellite are developed to provide an abundance of data for both healthy and various faulty conditions of the satellite.These data are then used as input for the proposed data-driven fault isolation method.Once a fault is detected,the fault isolation module is activated,where it employs a machine learning technique that incorporates ensemble methods involving random forests,decision trees,and nearest neighbors.Results of the classified faulty condition are then cross-validated using k-fold and leave-one-out methods.Performance comparison among different combinations for the ensemble architecture shows promising fault isolation of the non-linear systems using ensemble methods.展开更多
In this paper,a combined robust fault detection and isolation scheme is studied for satellite system subject to actuator faults,external disturbances,and parametric uncertainties.The proposed methodology incorporates ...In this paper,a combined robust fault detection and isolation scheme is studied for satellite system subject to actuator faults,external disturbances,and parametric uncertainties.The proposed methodology incorporates a residual generation module,including a bank of filters,into an intelligent residual evaluation module.First,residual filters are designed based on an improved nonlinear differential algebraic approach so that they are not affected by external disturbances.The residual evaluation module is developed based on the suggested series and parallel forms.Further,a new ensemble classification scheme defined as blended learning integrates heterogeneous classifiers to enhance the performance.A wide range of simulations is carried out in a high-fidelity satellite simulator subject to the constant and time-varying actuator faults in the presence of disturbances,manoeuvres,uncertainties,and noises.The obtained results demonstrate the effectiveness of the proposed robust fault detection and isolation method compared to the traditional nonlinear differential algebraic approach.展开更多
A discrete gain-varying unknown input observer （UIO） method is presented for actuator fault detection and isolation （FDI） problems in this paper. A novel residual scheme together with a moving horizon threshold is...A discrete gain-varying unknown input observer （UIO） method is presented for actuator fault detection and isolation （FDI） problems in this paper. A novel residual scheme together with a moving horizon threshold is proposed. This design methodology is applied to a nonlinear F16 system with polynomial aerodynamics coefficient expressions, where the coefficient expressions for the F16 system and UIOs may be slightly different. The simulation results illustrate that a satisfactory FDI performance can be achieved even when the F16 system is under the environment of model uncertainties, exogenous noise and measurement errors.展开更多
文摘This research aims to address the challenges of fault detection and isolation(FDI)in digital grids,focusing on improving the reliability and stability of power systems.Traditional fault detection techniques,such as rule-based fuzzy systems and conventional FDI methods,often struggle with the dynamic nature of modern grids,resulting in delays and inaccuracies in fault classification.To overcome these limitations,this study introduces a Hybrid NeuroFuzzy Fault Detection Model that combines the adaptive learning capabilities of neural networks with the reasoning strength of fuzzy logic.The model’s performance was evaluated through extensive simulations on the IEEE 33-bus test system,considering various fault scenarios,including line-to-ground faults(LGF),three-phase short circuits(3PSC),and harmonic distortions(HD).The quantitative results show that the model achieves 97.2%accuracy,a false negative rate(FNR)of 1.9%,and a false positive rate(FPR)of 2.3%,demonstrating its high precision in fault diagnosis.The qualitative analysis further highlights the model’s adaptability and its potential for seamless integration into smart grids,micro grids,and renewable energy systems.By dynamically refining fuzzy inference rules,the model enhances fault detection efficiency without compromising computational feasibility.These findings contribute to the development of more resilient and adaptive fault management systems,paving the way for advanced smart grid technologies.
基金supported by the National Natural Science Foundation of China(No.62303293,62303414)the China Postdoctoral Science Foundation(No.2023M732176,2023M741821)the Zhejiang Province Postdoctoral Selected Foundation(No.ZJ2023143).
文摘Test selection design(TSD)is an important technique for improving product maintainability,reliability and reducing lifecycle costs.In recent years,although some researchers have addressed the design problem of test selection,the correlation between test outcomes has not been sufficiently considered in test metrics modeling.This study proposes a new approach that combines copula and D-Vine copula to address the correlation issue in TSD.First,the copula is utilized to model FIR on the joint distribution.Furthermore,the D-Vine copula is applied to model the FDR and FAR.Then,a particle swarm optimization is employed to select the optimal testing scheme.Finally,the efficacy of the proposed method is validated through experimentation on a negative feedback circuit.
文摘In this paper, a geometric approach to fault detection and isolation （FDI） is applied to a Multiple-Input Multipie-Output （MIMO） model of a frame and the FDI results are compared to the ones obtained in the Single-Input Single-Output （SISO）, Multiple-Input Single-Output （MISO）, and Single-Input Multiple-Output （SIMO） cases. A proper distance function based on parameters obtained from parametric system identification method is used in the geometric approach. ARX （Auto Regressive with exogenous input） and VARX （Vector ARX） models with 12 parameters are used in all of the above-mentioned models. The obtained results reveal that by increasing the number of inputs, the classification errors reduce, even in the case of applying only one of the inputs in the computations. Furthermore, increasing the number of measured outputs in the FDI scheme results in decreasing classification errors. Also, it is shown that by using probabilistic space in the distance function, fault diagnosis scheme has better performance in comparison with the deterministic one.
文摘An application of the multiobjective fault detection and isolation（FDI） approach to an air-breathing hypersonic vehicle（HSV） longitudinal dynamics subject to disturbances is presented.Maintaining sustainable and safe flight of HSV is a challenging task due to its strong coupling effects,variable operating conditions and possible failures of system components.A common type of system faults for aircraft including HSV is the loss of effectiveness of its actuators and sensors.To detect and isolate multiple actuator/sensor failures,a faulty linear parameter-varying（LPV） model of HSV is derived by converting actuator/system component faults into equivalent sensor faults.Then a bank of LPV FDI observers is designed to track individual fault with minimum error and suppress the effects of disturbances and other fault signals.The simulation results based on the nonlinear flexible HSV model and a nominal LPV controller demonstrate the effectiveness of the fault estimation technique for HSV.
基金supported by the Natural Science Foundation of Shannxi Province(2017JQ5016)the Joint Laboratory for Sea Measurement and Control of Aircraft(DOM2016OF011)
文摘The complex systems are often in the structure of multi-operating modes, and the components implementing system functions are different under different operation modes, which results in the problems that components often fail in different operating modes, faults can be only detected in specified operating modes, tests can be available in specified operating modes,and the cost and efficiency of detecting and isolating faults are different under different operating modes and isolation levels. Aiming at these problems, an optimal test selection method for fault detection and isolation in the multi-operating mode system is proposed by using the fault pair coding and rollout algorithm. Firstly,the faults in fault-test correlation matrices under different operating modes are combined to fault-pairs, which is used to construct the fault pair-test correlation matrices under different operating modes.Secondly, the final fault pair-test correlation matrix of the multioperating mode system is obtained by operating the fault pair-test correlation matrices under different operating modes. Based on the final fault pair-test correlation matrix, the necessary tests are selected by the rollout algorithm orderly. Finally, the effectiveness of the proposed method is verified by examples of the optimal test selection in the multi-operating mode system with faults isolated to different levels. The result shows that the proposed method can effectively mine the fault detection and isolation ability of tests and it is suitable for the optimal test selection of the multi-operating mode system with faults isolated to the replacement unit and specific fault.
基金Support by the National Natural Science Foundation of China(61174114)the Research Fund for the Doctoral Program of Higher Education in China(20120101130016)Zhejiang Provincial Science and Technology Planning Projects of China(2014C31019)
文摘In chemical process, a large number of measured and manipulated variables are highly correlated. Principal component analysis(PCA) is widely applied as a dimension reduction technique for capturing strong correlation underlying in the process measurements. However, it is difficult for PCA based fault detection results to be interpreted physically and to provide support for isolation. Some approaches incorporating process knowledge are developed, but the information is always shortage and deficient in practice. Therefore, this work proposes an adaptive partitioning PCA algorithm entirely based on operation data. The process feature space is partitioned into several sub-feature spaces. Constructed sub-block models can not only reflect the local behavior of process change, namely to grasp the intrinsic local information underlying the process changes, but also improve the fault detection and isolation through the combination of local fault detection results and reduction of smearing effect.The method is demonstrated in TE process, and the results show that the new method is much better in fault detection and isolation compared to conventional PCA method.
文摘State reconstruction approach is very useful for sensor fault isolation, reconstruction of faulty measurement and the determination of the number of components retained in the principal components analysis （PCA） model. An extension of this approach based on a Nonlinear PCA （NLPCA） model is described in this paper. The NLPCA model is obtained using five layer neural network. A simulation example is given to show the performances of the proposed approach.
基金Supported by National Hi-tech Research and Development Program of China(863 Program,Grant No.2011AA11A223)
文摘Multi-way principal component analysis（MPCA）has received considerable attention and been widely used in process monitoring.A traditional MPCA algorithm unfolds multiple batches of historical data into a two-dimensional matrix and cut the matrix along the time axis to form subspaces.However,low efficiency of subspaces and difficult fault isolation are the common disadvantages for the principal component model.This paper presents a new subspace construction method based on kernel density estimation function that can effectively reduce the storage amount of the subspace information.The MPCA model and the knowledge base are built based on the new subspace.Then,fault detection and isolation with the squared prediction error（SPE）statistic and the Hotelling（T2）statistic are also realized in process monitoring.When a fault occurs,fault isolation based on the SPE statistic is achieved by residual contribution analysis of different variables.For fault isolation of subspace based on the T2 statistic,the relationship between the statistic indicator and state variables is constructed,and the constraint conditions are presented to check the validity of fault isolation.Then,to improve the robustness of fault isolation to unexpected disturbances,the statistic method is adopted to set the relation between single subspace and multiple subspaces to increase the corrective rate of fault isolation.Finally fault detection and isolation based on the improved MPCA is used to monitor the automatic shift control system（ASCS）to prove the correctness and effectiveness of the algorithm.The research proposes a new subspace construction method to reduce the required storage capacity and to prove the robustness of the principal component model,and sets the relationship between the state variables and fault detection indicators for fault isolation.
基金This work was supported by Army Research Office (No. DAAD19-02-1-0160)Office of Naval Research (No. N00014-03-1-0052 and N00014-06-1-0146).
文摘A robust nonlinear analytical redundancy （RNLAR） technique is presented to detect and isolate actuator and sensor faults in a mobile robot. Both model-plant-mismatch （MPM） and process disturbance are considered during fault detection. The RNLAR is used to design primary residual vectors （PRV）, which are highly sensitive to the faults and less sensitive to MPM and process disturbance, for sensor and actuator fault detection. The PRVs are then transformed into a set of structured residual vectors （SRV） for fault isolation. Experimental results on a Pioneer 3-DX mobile robot are presented to justify the effectiveness of the RNLAR scheme.
基金partially supported by National Key R&D Program of China(2018YFB1304600)National Natural Science Foundation of China(51805021,U1813220)+1 种基金China Postdoctoral Science Foundation Grant(2018M631311)the Fundamental Research Funds for the Central Universities(XK1802-4)
文摘This work proposes a robust fault detection and isolation scheme for discrete-time systems subject to actuator faults,in which a bank of H_/H∞fault detection unknown input observers(UIOs)and a zonotopic threshold analysis strategy are considered.In observer design,finite-frequency H_index based on the generalized Kalman-Yakubovich-Popov lemma and H∞technique are utilized to evaluate worst-case fault sensitivity and disturbance attenuation performance,respectively.The proposed H_/H∞fault detection observers are designed to be insensitive to the corresponding actuator fault only,but sensitive to others.Then,to overcome the weakness of predefining threshold for FDI decision-making,this work proposes a zonotopic threshold analysis method to evaluate the generated residuals.The FDI decision-making relies on the evaluation with a dynamical zonotopic threshold.Finally,numerical simulations are provided to show the feasibility of the proposed scheme.
基金National Natural Science Foundation of China(No.60574081)
文摘A Fault detection and isolation(FDI)scheme for discrete time-delay system is proposed in this paper,which can not only detect but also isolate the faults.A time delay operator is introduced to resolve the problem brought by the time-delay system.The design and computation for the FDI system is carried by computer math tool Maple,which can easily deal with the symbolic computation.Residuals in the form of parity space can be deduced from the recursion of the system equations.Further more,a generalized residual set is created using the freedom of the parity space redundancy.Thus,both fault detection and fault isolation have been accomplished.The proposed method has been verified by a numerical example.
文摘The diagnoses in industrial systems represent an important economic objective in process industrial automation area. To guarantee the safety and the continuity in production exploitation and to record the useful events with the feedback experience for the curative maintenance. We propose in this work to examine and illustrate the application ability of the spectral analysis approach, in the area of fault detection and isolation industrial systems. In this work, we use a combined analysis diagram of time-frequency, in order to make this approach exploitable in the proposed supervision strategy with decision making module. The obtained results, show clearly how to guarantee a reliable and sure exploitation in industrial system, thus allowing better performances at the time of its exploitation on the supervision strategy.
文摘Reliability analysis of a leak detection system developed by OSYRIS R＆D is dealed with in this paper. The developed algorithm is based on signal processing theory; and it uses the properties of the cross-correlation function in order to distinguish the fluid leak from a various disturbances. Experimental results obtained on different processes, in presence of thermal and hydraulic disturbances, show the advantages and limits of the proposed approach.
文摘Support vector machines and a Kalman-like observer are used for fault detection and isolation in a variable speed horizontalaxis wind turbine composed of three blades and a full converter. The support vector approach is data-based and is therefore robust to process knowledge. It is based on structural risk minimization which enhances generalization even with small training data set and it allows for process nonlinearity by using flexible kernels. In this work, a radial basis function is used as the kernel. Different parts of the process are investigated including actuators and sensors faults. With duplicated sensors, sensor faults in blade pitch positions,generator and rotor speeds can be detected. Faults of type stuck measurements can be detected in 2 sampling periods. The detection time of offset/scaled measurements depends on the severity of the fault and on the process dynamics when the fault occurs. The converter torque actuator fault can be detected within 2 sampling periods. Faults in the actuators of the pitch systems represents a higher difficulty for fault detection which is due to the fact that such faults only affect the transitory state(which is very fast) but not the final stationary state. Therefore, two methods are considered and compared for fault detection and isolation of this fault: support vector machines and a Kalman-like observer. Advantages and disadvantages of each method are discussed. On one hand, support vector machines training of transitory states would require a big amount of data in different situations, but the fault detection and isolation results are robust to variations in the input/operating point. On the other hand, the observer is model-based, and therefore does not require training, and it allows identification of the fault level, which is interesting for fault reconfiguration. But the observability of the system is ensured under specific conditions, related to the dynamics of the inputs and outputs. The whole fault detection and isolation scheme is evaluated using a wind turbine benchmark with a real sequence of wind speed.
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.
文摘Three-phase grid-connected inverters(GCIs)are essential components in distributed generation systems,where the accuracy of current measurement circuits is fundamental for reliable closed-loop operation.Nevertheless,the presence of a DC offset in the measured current can disrupt the regulation of grid currents and significantly degrade system performance.In this work,a fault-tolerant control approach is introduced to counteract the impact of such offset faults through a dedicated current compensation mechanism.The proposed solution is built around two main stages:(i)detecting and isolating DC offset faults that may appear in one or multiple phases of the measured grid currents,and(ii)estimating the fault magnitude and reconstructing the corrected current signal.The offset magnitude is obtained analytically by examining the grid current projected onto the synchronous d-axis at the grid angular frequency,eliminating the need for any additional sensing hardware.Simulation and experimental investigations conducted under several fault scenarios confirm the robustness of the proposed strategy and highlight significant improvements in detection speed and diagnostic accuracy.
文摘The primary objective of this study is to explore novel applications of data-driven machine learning methods for isolation of nonlinear systems with a case study for an in-orbit closed-loop controlled satellite with reaction wheels as actuators.Highfidelity models of the three-axis controlled satellite are developed to provide an abundance of data for both healthy and various faulty conditions of the satellite.These data are then used as input for the proposed data-driven fault isolation method.Once a fault is detected,the fault isolation module is activated,where it employs a machine learning technique that incorporates ensemble methods involving random forests,decision trees,and nearest neighbors.Results of the classified faulty condition are then cross-validated using k-fold and leave-one-out methods.Performance comparison among different combinations for the ensemble architecture shows promising fault isolation of the non-linear systems using ensemble methods.
文摘In this paper,a combined robust fault detection and isolation scheme is studied for satellite system subject to actuator faults,external disturbances,and parametric uncertainties.The proposed methodology incorporates a residual generation module,including a bank of filters,into an intelligent residual evaluation module.First,residual filters are designed based on an improved nonlinear differential algebraic approach so that they are not affected by external disturbances.The residual evaluation module is developed based on the suggested series and parallel forms.Further,a new ensemble classification scheme defined as blended learning integrates heterogeneous classifiers to enhance the performance.A wide range of simulations is carried out in a high-fidelity satellite simulator subject to the constant and time-varying actuator faults in the presence of disturbances,manoeuvres,uncertainties,and noises.The obtained results demonstrate the effectiveness of the proposed robust fault detection and isolation method compared to the traditional nonlinear differential algebraic approach.
文摘A discrete gain-varying unknown input observer （UIO） method is presented for actuator fault detection and isolation （FDI） problems in this paper. A novel residual scheme together with a moving horizon threshold is proposed. This design methodology is applied to a nonlinear F16 system with polynomial aerodynamics coefficient expressions, where the coefficient expressions for the F16 system and UIOs may be slightly different. The simulation results illustrate that a satisfactory FDI performance can be achieved even when the F16 system is under the environment of model uncertainties, exogenous noise and measurement errors.
