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
Fault isolation in dynamical systems is a challenging task due to modeling uncertainty and measurement noise,interactive effects of multiple faults and fault propagation.This paper proposes a unified approach for isol...Fault isolation in dynamical systems is a challenging task due to modeling uncertainty and measurement noise,interactive effects of multiple faults and fault propagation.This paper proposes a unified approach for isolation of multiple actuator or sensor faults in a class of nonlinear uncertain dynamical systems.Actuator and sensor fault isolation are accomplished in two independent modules,that monitor the system and are able to isolate the potential faulty actuator(s)or sensor(s).For the sensor fault isolation(SFI)case,a module is designed which monitors the system and utilizes an adaptive isolation threshold on the output residuals computed via a nonlinear estimation scheme that allows the isolation of single/multiple faulty sensor(s).For the actuator fault isolation(AFI)case,a second module is designed,which utilizes a learning-based scheme for adaptive approximation of faulty actuator(s)and,based on a reasoning decision logic and suitably designed AFI thresholds,the faulty actuator(s)set can be determined.The effectiveness of the proposed fault isolation approach developed in this paper is demonstrated through a simulation example.展开更多
An inter-story shear model of asymmetric base-isolated structures incorporating deformation of each isolation bearing was built, and a method to simultaneously simulate bi-directional near-fault and far-field ground m...An inter-story shear model of asymmetric base-isolated structures incorporating deformation of each isolation bearing was built, and a method to simultaneously simulate bi-directional near-fault and far-field ground motions was proposed. A comparative study on the dynamic responses of asymmetric base-isolated structures under near-fault and far-field ground motions were conducted to investigate the effects of eccentricity in the isolation system and in the superstructures, the ratio of the uncoupled torsional to lateral frequency of the superstructure and the pulse period of near-fault ground motions on the nonlinear seismic response of asymmetric base-isolated structures. Numerical results show that eccentricity in the isolation system makes asymmetric base-isolated structure more sensitive to near-fault ground motions, and the pulse period of near-fault ground motions plays an import role in governing the seismic responses of asymmetric base-isolated structures.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
This paper focuses on the investigation of a hybrid seismic isolation system with passive variable friction dampers for protection of structures against near fault earthquakes. The seismic isolation can be implemented...This paper focuses on the investigation of a hybrid seismic isolation system with passive variable friction dampers for protection of structures against near fault earthquakes. The seismic isolation can be implemented by replacing the conventional columns fixed to the foundations by seismic isolating ones. These columns allow horizontal displacement between the superstructure and the foundations and decouple the building from the damaging earthquake motion. As a result, the forces in the structural elements decrease and damage that may be caused to the building by the earthquake significantly decreases. However, this positive effect is achieved on account of displacements occurring in the isolating columns. These displacements become very large when the structure is subjected to a strong earthquake. In this case, impact may occur between the parts of the isolating column yielding their damage or collapse. In order to limit the displacements in the isolating columns, it is proposed to add variable friction dampers. A method for selecting the dampers＇ properties is proposed. It is carried out using an artificial ground motion record and optimal active control algorithm. Numerical simulation of a sevenstory structure shows that the proposed method allows efficient reduction in structural response and limits the displacements at the seismic isolating columns.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
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
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 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
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
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
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
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 a...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
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
On February 3rd,2020,an isolated MS5.1 earthquake occurred in the northern section of the Longquanshan fault zone.This study aims at defining the geometry of seismogenic structures of this earthquake.In detail,centroi...On February 3rd,2020,an isolated MS5.1 earthquake occurred in the northern section of the Longquanshan fault zone.This study aims at defining the geometry of seismogenic structures of this earthquake.In detail,centroid moment tensor inversion results show that the earthquake is characterized by a focal depth of 3.8 km with no corresponding surface faults.The strike/dip/rake angles for the two nodal planes are 205°/54°/96°and 15°/36°/82°,respectively.With the analyses of coseismic deformation of the surface obtained from InSAR measurements,together with the information of relocated hypocenters for a small number of aftershocks,it is concluded that a northwest-dipping nodal plane corresponds well to the source fault.The fault is suggested to have a length of about 2.8 km and a depth range of 2-5 km,and the centroid of the earthquake is located at 104.48°E and 30.71°N.Furthermore,multiple pieces of evidence indicate that this earthquake is partly driven by the overpressure effect associated with the adjacent natural gas packets,which is similar to several other moderate natural earthquakes in Sichuan Basin.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.
基金the European Research Council(ERC)under the ERC Synergy grant agreement No.951424(Water-Futures)the European Union’s Horizon 2020 research and innovation programme under grant agreement No.739551(KIOS CoE)the Government of the Republic of Cyprus through the Directorate General for European Programmes,Coordination and Development。
文摘Fault isolation in dynamical systems is a challenging task due to modeling uncertainty and measurement noise,interactive effects of multiple faults and fault propagation.This paper proposes a unified approach for isolation of multiple actuator or sensor faults in a class of nonlinear uncertain dynamical systems.Actuator and sensor fault isolation are accomplished in two independent modules,that monitor the system and are able to isolate the potential faulty actuator(s)or sensor(s).For the sensor fault isolation(SFI)case,a module is designed which monitors the system and utilizes an adaptive isolation threshold on the output residuals computed via a nonlinear estimation scheme that allows the isolation of single/multiple faulty sensor(s).For the actuator fault isolation(AFI)case,a second module is designed,which utilizes a learning-based scheme for adaptive approximation of faulty actuator(s)and,based on a reasoning decision logic and suitably designed AFI thresholds,the faulty actuator(s)set can be determined.The effectiveness of the proposed fault isolation approach developed in this paper is demonstrated through a simulation example.
基金The National Natural Science Foundation of China (No. 50778078)
文摘An inter-story shear model of asymmetric base-isolated structures incorporating deformation of each isolation bearing was built, and a method to simultaneously simulate bi-directional near-fault and far-field ground motions was proposed. A comparative study on the dynamic responses of asymmetric base-isolated structures under near-fault and far-field ground motions were conducted to investigate the effects of eccentricity in the isolation system and in the superstructures, the ratio of the uncoupled torsional to lateral frequency of the superstructure and the pulse period of near-fault ground motions on the nonlinear seismic response of asymmetric base-isolated structures. Numerical results show that eccentricity in the isolation system makes asymmetric base-isolated structure more sensitive to near-fault ground motions, and the pulse period of near-fault ground motions plays an import role in governing the seismic responses of asymmetric base-isolated structures.
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This paper focuses on the investigation of a hybrid seismic isolation system with passive variable friction dampers for protection of structures against near fault earthquakes. The seismic isolation can be implemented by replacing the conventional columns fixed to the foundations by seismic isolating ones. These columns allow horizontal displacement between the superstructure and the foundations and decouple the building from the damaging earthquake motion. As a result, the forces in the structural elements decrease and damage that may be caused to the building by the earthquake significantly decreases. However, this positive effect is achieved on account of displacements occurring in the isolating columns. These displacements become very large when the structure is subjected to a strong earthquake. In this case, impact may occur between the parts of the isolating column yielding their damage or collapse. In order to limit the displacements in the isolating columns, it is proposed to add variable friction dampers. A method for selecting the dampers＇ properties is proposed. It is carried out using an artificial ground motion record and optimal active control algorithm. Numerical simulation of a sevenstory structure shows that the proposed method allows efficient reduction in structural response and limits the displacements at the seismic isolating columns.
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 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.
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We thank editor board members for the encouragement and recom-mendation,and thank the two anonymous reviewers for the detailed constructive comments.Jinrong Su acknowledge the support of China National Key R&D Project(2018YFC1504501-02).
文摘On February 3rd,2020,an isolated MS5.1 earthquake occurred in the northern section of the Longquanshan fault zone.This study aims at defining the geometry of seismogenic structures of this earthquake.In detail,centroid moment tensor inversion results show that the earthquake is characterized by a focal depth of 3.8 km with no corresponding surface faults.The strike/dip/rake angles for the two nodal planes are 205°/54°/96°and 15°/36°/82°,respectively.With the analyses of coseismic deformation of the surface obtained from InSAR measurements,together with the information of relocated hypocenters for a small number of aftershocks,it is concluded that a northwest-dipping nodal plane corresponds well to the source fault.The fault is suggested to have a length of about 2.8 km and a depth range of 2-5 km,and the centroid of the earthquake is located at 104.48°E and 30.71°N.Furthermore,multiple pieces of evidence indicate that this earthquake is partly driven by the overpressure effect associated with the adjacent natural gas packets,which is similar to several other moderate natural earthquakes in Sichuan Basin.
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.
