The multi-terminal direct current(DC)grid has extinctive superiorities over the traditional alternating current system in integrating large-scale renewable energy.Both the DC circuit breaker(DCCB)and the current flow ...The multi-terminal direct current(DC)grid has extinctive superiorities over the traditional alternating current system in integrating large-scale renewable energy.Both the DC circuit breaker(DCCB)and the current flow controller(CFC)are demanded to ensure the multiterminal DC grid to operates reliably and flexibly.However,since the CFC and the DCCB are all based on fully controlled semiconductor switches(e.g.,insulated gate bipolar transistor,integrated gate commutated thyristor,etc.),their separation configuration in the multiterminal DC grid will lead to unaffordable implementation costs and conduction power losses.To solve these problems,integrated equipment with both current flow control and fault isolation abilities is proposed,which shares the expensive and duplicated components of CFCs and DCCBs among adjacent lines.In addition,the complicated coordination control of CFCs and DCCBs can be avoided by adopting the integrated equipment in themultiterminal DC grid.In order to examine the current flow control and fault isolation abilities of the integrated equipment,the simulation model of a specific meshed four-terminal DC grid is constructed in the PSCAD/EMTDC software.Finally,the comparison between the integrated equipment and the separate solution is presented a specific result or conclusion needs to be added to the abstract.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
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
Aiming at various faults in an air conditioning system,the fault characteristics are analyzed.The influence of the faults on the energy consumption and thermal comfort of the system are also discussed.The simulation r...Aiming at various faults in an air conditioning system,the fault characteristics are analyzed.The influence of the faults on the energy consumption and thermal comfort of the system are also discussed.The simulation results show that the measurement faults of the supply air temperature can lead to the increase in energy consumption.According to the fault characteristics,a data-driven method based on a neural network is presented to detect and diagnose the faults of air handling units.First,the historical data are selected to train the neural network so that it can recognize and predict the operation of the system.Then,the faults can be diagnosed by calculating the relative errors denoting the difference between the measuring values and the prediction outputs.Finally,the fault diagnosis strategy using the neural network is validated by using a simulator based on the TRNSYS platform.The results show that the neural network can diagnose different faults of the temperature,the flow rate and the pressure sensors in the air conditioning system.展开更多
Improving fault tolerant performance of permanent magnet synchronous motor has always been the central issue of the electrically supplied actuator for aerospace application. In this paper, a novel fault tolerant perma...Improving fault tolerant performance of permanent magnet synchronous motor has always been the central issue of the electrically supplied actuator for aerospace application. In this paper, a novel fault tolerant permanent magnet synchronous motor is proposed, which is character- ized by two stators and two rotors on the same shaft with a circumferential displacement of mechanical angle of 4.5°. It helps to reduce the cogging torque. Each segment of the stator and the rotor can be considered as an 8-pole/10-slot five-phase permanent magnet synchronous motor with concentrated, single-layer and alternate teeth wound winding, which enhance the fault isola- tion capacity of the motor. Furthermore, the motor has high phase inductance to restrain the short-circuit current. In addition, an improved optimal torque control strategy is proposed to make the motor work well under the open-circuit fault and short-circuit fault conditions. Simulation and experiment results show that the proposed fault tolerant motor system has excellent fault tolerant capacity, which is able to operate continuously under the third open-circuit fault and second short- circuit fault condition without system performance degradation, which was not available earlier.展开更多
Principal component analysis （PCA） is a useful tool in process fault detection, but offers little support on fault isolation. In this article, structured residual with strong isolation property is introduced. Althou...Principal component analysis （PCA） is a useful tool in process fault detection, but offers little support on fault isolation. In this article, structured residual with strong isolation property is introduced. Although it is easy to get the residual by transformation matrix in static process, unfortunately, it becomes hard in dynamic process under control loop. Therefore, partial dynamic PCA（PDPCA） is proposed to obtain structured residual and enhance the isolation ability of dynamic process monitoring, and a compound statistic is introduced to resolve the problem resulting from independent variables in every variable subset. Simulations on continuous stirred tank reactor （CSTR） show the effectiveness of the proposed method.展开更多
This paper is devoted to develop an expert system to manage the fault isolation and maintenance knowledge of the engine indication and crew alerting system (EICAS). The object oriented programming (OOP) technique and...This paper is devoted to develop an expert system to manage the fault isolation and maintenance knowledge of the engine indication and crew alerting system (EICAS). The object oriented programming (OOP) technique and the microsoft foundation class (MFC) are applied to set up a frame decision tree (FDT) which incorporates the expert system′s knowledge base, inference engine and user interface. Once a fault symptom indicated by the EICAS is input, by inferring step by step, the expert system can locate it in the engine and provide some homologous constructive maintenance advice.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
In industrial processes,there exist faults that have complex effect on process variables.Complex and simple faults are defined according to their effect dimensions.The conventional approaches based on structured resid...In industrial processes,there exist faults that have complex effect on process variables.Complex and simple faults are defined according to their effect dimensions.The conventional approaches based on structured residuals cannot isolate complex faults.This paper presents a multi-level strategy for complex fault isolation.An extraction procedure is employed to reduce the complex faults to simple ones and assign them to several levels.On each level,faults are isolated by their different responses in the structured residuals.Each residual is obtained insensitive to one fault but more sensitive to others.The faults on different levels are verified to have different residual responses and will not be confused.An entire incidence matrix containing residual response characteristics of all faults is obtained,based on which faults can be isolated.The proposed method is applied in the Tennessee Eastman process example,and the effectiveness and advantage are demonstrated.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
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
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
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
A robust fault diagnosis approach is developed by incorporating a set-membership identification (SMI) method. A class of systems with linear models in the form of fault related parameters is investigated, with model u...A robust fault diagnosis approach is developed by incorporating a set-membership identification (SMI) method. A class of systems with linear models in the form of fault related parameters is investigated, with model uncertainties and parameter variations taken into account explicitly and treated as bounded errors. An ellipsoid bounding set-membership identification algorithm is proposed to propagate bounded uncertainties rigorously and the guaranteed feasible set of faults parameters enveloping true parameter values is given. Faults arised from abrupt parameter variations can be detected and isolated on-line by consistency check between predicted and observed parameter sets obtained in the identification procedure. The proposed approach provides the improved robustness with its ability to distinguish real faults from model uncertainties, which comes with the inherent guaranteed robustness of the set-membership framework. Efforts are also made in this work to balance between conservativeness and computation complexity of the overall algorithm. Simulation results for the mobile robot with several slipping faults scenarios demonstrate the correctness of the proposed approach for faults detection and isolation (FDI).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
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
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
One kind of steepest descent incremental projection learning algorithm for improving the training of radial basis function(RBF)neural network is proposed,which is applied to analog circuit fault isolation.This algorit...One kind of steepest descent incremental projection learning algorithm for improving the training of radial basis function(RBF)neural network is proposed,which is applied to analog circuit fault isolation.This algorithm simplified the structure of network through optimum output layer coefficient with incremental projection learning(IPL)algorithm,and adjusted the parameters of the neural activation function to control the network scale and improve the network approximation ability.Compared to the traditional algorithm,the improved algorithm has quicker convergence rate and higher isolation precision.Simulation results show that this improved RBF network has much better performance,which can be used in analog circuit fault isolation field.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
基金supported in part by Natural Science Foundation of Jiangsu Province under Grant BK20230255Natural Science Foundation of Shandong Province under Grant ZR2023QE281.
文摘The multi-terminal direct current(DC)grid has extinctive superiorities over the traditional alternating current system in integrating large-scale renewable energy.Both the DC circuit breaker(DCCB)and the current flow controller(CFC)are demanded to ensure the multiterminal DC grid to operates reliably and flexibly.However,since the CFC and the DCCB are all based on fully controlled semiconductor switches(e.g.,insulated gate bipolar transistor,integrated gate commutated thyristor,etc.),their separation configuration in the multiterminal DC grid will lead to unaffordable implementation costs and conduction power losses.To solve these problems,integrated equipment with both current flow control and fault isolation abilities is proposed,which shares the expensive and duplicated components of CFCs and DCCBs among adjacent lines.In addition,the complicated coordination control of CFCs and DCCBs can be avoided by adopting the integrated equipment in themultiterminal DC grid.In order to examine the current flow control and fault isolation abilities of the integrated equipment,the simulation model of a specific meshed four-terminal DC grid is constructed in the PSCAD/EMTDC software.Finally,the comparison between the integrated equipment and the separate solution is presented a specific result or conclusion needs to be added to the abstract.
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Aiming at various faults in an air conditioning system,the fault characteristics are analyzed.The influence of the faults on the energy consumption and thermal comfort of the system are also discussed.The simulation results show that the measurement faults of the supply air temperature can lead to the increase in energy consumption.According to the fault characteristics,a data-driven method based on a neural network is presented to detect and diagnose the faults of air handling units.First,the historical data are selected to train the neural network so that it can recognize and predict the operation of the system.Then,the faults can be diagnosed by calculating the relative errors denoting the difference between the measuring values and the prediction outputs.Finally,the fault diagnosis strategy using the neural network is validated by using a simulator based on the TRNSYS platform.The results show that the neural network can diagnose different faults of the temperature,the flow rate and the pressure sensors in the air conditioning system.
文摘Improving fault tolerant performance of permanent magnet synchronous motor has always been the central issue of the electrically supplied actuator for aerospace application. In this paper, a novel fault tolerant permanent magnet synchronous motor is proposed, which is character- ized by two stators and two rotors on the same shaft with a circumferential displacement of mechanical angle of 4.5°. It helps to reduce the cogging torque. Each segment of the stator and the rotor can be considered as an 8-pole/10-slot five-phase permanent magnet synchronous motor with concentrated, single-layer and alternate teeth wound winding, which enhance the fault isola- tion capacity of the motor. Furthermore, the motor has high phase inductance to restrain the short-circuit current. In addition, an improved optimal torque control strategy is proposed to make the motor work well under the open-circuit fault and short-circuit fault conditions. Simulation and experiment results show that the proposed fault tolerant motor system has excellent fault tolerant capacity, which is able to operate continuously under the third open-circuit fault and second short- circuit fault condition without system performance degradation, which was not available earlier.
基金the National Natural Science Foundation of China （No.60421002）.
文摘Principal component analysis （PCA） is a useful tool in process fault detection, but offers little support on fault isolation. In this article, structured residual with strong isolation property is introduced. Although it is easy to get the residual by transformation matrix in static process, unfortunately, it becomes hard in dynamic process under control loop. Therefore, partial dynamic PCA（PDPCA） is proposed to obtain structured residual and enhance the isolation ability of dynamic process monitoring, and a compound statistic is introduced to resolve the problem resulting from independent variables in every variable subset. Simulations on continuous stirred tank reactor （CSTR） show the effectiveness of the proposed method.
文摘This paper is devoted to develop an expert system to manage the fault isolation and maintenance knowledge of the engine indication and crew alerting system (EICAS). The object oriented programming (OOP) technique and the microsoft foundation class (MFC) are applied to set up a frame decision tree (FDT) which incorporates the expert system′s knowledge base, inference engine and user interface. Once a fault symptom indicated by the EICAS is input, by inferring step by step, the expert system can locate it in the engine and provide some homologous constructive maintenance advice.
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 by the National Natural Science Foundation of China(60574047)the National High Technology Research and Development Program of China(2007AA04Z168,2009AA04Z154)the Research Fund for the Doctoral Program of Higher Education in China(20050335018)
文摘In industrial processes,there exist faults that have complex effect on process variables.Complex and simple faults are defined according to their effect dimensions.The conventional approaches based on structured residuals cannot isolate complex faults.This paper presents a multi-level strategy for complex fault isolation.An extraction procedure is employed to reduce the complex faults to simple ones and assign them to several levels.On each level,faults are isolated by their different responses in the structured residuals.Each residual is obtained insensitive to one fault but more sensitive to others.The faults on different levels are verified to have different residual responses and will not be confused.An entire incidence matrix containing residual response characteristics of all faults is obtained,based on which faults can be isolated.The proposed method is applied in the Tennessee Eastman process example,and the effectiveness and advantage are demonstrated.
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.
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.
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.
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.
基金supported by the National Natural Science Foundation of China(616732546157310061573101)
文摘A robust fault diagnosis approach is developed by incorporating a set-membership identification (SMI) method. A class of systems with linear models in the form of fault related parameters is investigated, with model uncertainties and parameter variations taken into account explicitly and treated as bounded errors. An ellipsoid bounding set-membership identification algorithm is proposed to propagate bounded uncertainties rigorously and the guaranteed feasible set of faults parameters enveloping true parameter values is given. Faults arised from abrupt parameter variations can be detected and isolated on-line by consistency check between predicted and observed parameter sets obtained in the identification procedure. The proposed approach provides the improved robustness with its ability to distinguish real faults from model uncertainties, which comes with the inherent guaranteed robustness of the set-membership framework. Efforts are also made in this work to balance between conservativeness and computation complexity of the overall algorithm. Simulation results for the mobile robot with several slipping faults scenarios demonstrate the correctness of the proposed approach for faults detection and isolation (FDI).
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.
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.
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.
基金Pre-research Projects Fund of the National Ar ming Department,the 11th Five-year Projects
文摘One kind of steepest descent incremental projection learning algorithm for improving the training of radial basis function(RBF)neural network is proposed,which is applied to analog circuit fault isolation.This algorithm simplified the structure of network through optimum output layer coefficient with incremental projection learning(IPL)algorithm,and adjusted the parameters of the neural activation function to control the network scale and improve the network approximation ability.Compared to the traditional algorithm,the improved algorithm has quicker convergence rate and higher isolation precision.Simulation results show that this improved RBF network has much better performance,which can be used in analog circuit fault isolation field.
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.
