Fault diagnosis and monitoring are very important for complex chemical process. There are numerous methods that have been studied in this field, in which the effective visualization method is still challenging. In ord...Fault diagnosis and monitoring are very important for complex chemical process. There are numerous methods that have been studied in this field, in which the effective visualization method is still challenging. In order to get a better visualization effect, a novel fault diagnosis method which combines self-organizing map (SOM) with Fisher discriminant analysis (FDA) is proposed. FDA can reduce the dimension of the data in terms of maximizing the separability of the classes. After feature extraction by FDA, SOM can distinguish the different states on the output map clearly and it can also be employed to monitor abnormal states. Tennessee Eastman (TE) process is employed to illustrate the fault diagnosis and monitoring performance of the proposed method. The result shows that the SOM integrated with FDA method is efficient and capable for real-time monitoring and fault diagnosis in complex chemical process.展开更多
A multivariate method for fault diagnosis and process monitoring is proposed. This technique is based on a statistical pattern(SP) framework integrated with a self-organizing map(SOM). An SP-based SOM is used as a cla...A multivariate method for fault diagnosis and process monitoring is proposed. This technique is based on a statistical pattern(SP) framework integrated with a self-organizing map(SOM). An SP-based SOM is used as a classifier to distinguish various states on the output map, which can visually monitor abnormal states. A case study of the Tennessee Eastman(TE) process is presented to demonstrate the fault diagnosis and process monitoring performance of the proposed method. Results show that the SP-based SOM method is a visual tool for real-time monitoring and fault diagnosis that can be used in complex chemical processes.Compared with other SOM-based methods, the proposed method can more efficiently monitor and diagnose faults.展开更多
All kinds of reasons are analysed in theory and a fault repository combined with local expert experiences is establishedaccording to the structure and the operation characteristic of steam generator in this paper. At ...All kinds of reasons are analysed in theory and a fault repository combined with local expert experiences is establishedaccording to the structure and the operation characteristic of steam generator in this paper. At the same time, Kohonen algo-rithm is used for fault diagnoses system based on fuzzy neural networks. Fuzzy arithmetic is inducted into neural networks tosolve uncertain diagnosis induced by uncertain knowledge. According to its self-association in the course of default diagnosis. thesystem is provided with non-supervise, self-organizing, self-learning, and has strong cluster ability and fast cluster velocity.展开更多
In order to diagnose the working status of each module on sensor node and make sure the wireless sensor networks (WSN) work properly, the components of sensor node and their working characteristics are studied. An o...In order to diagnose the working status of each module on sensor node and make sure the wireless sensor networks (WSN) work properly, the components of sensor node and their working characteristics are studied. An on-line fault self-diagnosis method for sensor node is proposed. First, a flexible fault sensing circuit is designed as a state detection module on sensor node. Second, a self- diagnosis algorithm is proposed based on the hardware design and the failure analysis on sensor node. Finally, in order to ensure the WSN reliability, the voltage changes of each module working statuses can be observed using the state detection module and the faulty module will be found out timely. The experimental results show that this self-diagnosis method is suitable to sensor nodes in WSN.展开更多
To solve the fault diagnosis problem of liquid propellant rocket engine ground testing bed,a fault diagnosis approach based on self-organizing map(SOM)is proposed.The SOM projects the multidimensional ground testing b...To solve the fault diagnosis problem of liquid propellant rocket engine ground testing bed,a fault diagnosis approach based on self-organizing map(SOM)is proposed.The SOM projects the multidimensional ground testing bed data into a two-dimensional map.Visualization of the SOM is used to cluster the ground testing bed data.The out map of the SOM is divided to several regions.Each region is represented for one fault mode.The fault mode of testing data is determined according to the region of their labels belonged to.The method is evaluated using the testing data of a liquid-propellant rocket engine ground testing bed with sixteen fault states.The results show that it is a reliable and effective method for fault diagnosis with good visualization property.展开更多
Up to present,the problem of the evaluation of fault diagnosability for nonlinear systems has been investigated by many researchers.However,no attempt has been done to evaluate the diagnosability of multiple faults oc...Up to present,the problem of the evaluation of fault diagnosability for nonlinear systems has been investigated by many researchers.However,no attempt has been done to evaluate the diagnosability of multiple faults occurring simultaneously for nonlinear systems.This paper proposes a method based on differential geometry theories to solve this problem.Then the evaluation of fault diagnosability for affine nonlinear systems with multiple faults occurring simultaneously is achieved.To deal with the effect of control laws on the evaluation results of fault diagnosability,a design scheme of the evaluation of fault diagnosability is proposed.Then the influence of uncertainties on the evaluation results of fault diagnosability for affine nonlinear systems with multiple faults occurring simultaneously is analyzed.The numerical simulation results are obtained to show the effectiveness of the proposed evaluation scheme of fault diagnosability.展开更多
Aiming at the problem that ICA can only be confined to the condition that the number of observed signals is larger than the number of source signals;a single channel blind source separation method combining EEMD, PCA ...Aiming at the problem that ICA can only be confined to the condition that the number of observed signals is larger than the number of source signals;a single channel blind source separation method combining EEMD, PCA and RobustICA is proposed. Through the eemd decomposition of the single-channel mechanical vibration observation signal the multidimensional IMF components are obtained, and the principal component analysis (PCA) is performed on the matrix of these IMF components. The number of principal components is determined and a new matrix is generated to satisfy the overdetermined blind source separation conditions, the new matrix input RobustICA, to achieve the separation of the source signal. Finally, the isolated signals are respectively analyzed by the envelope spectrum, the fault frequency is extracted, and the fault type is judged according to the prior knowledge. The experiment was carried out by using the simulation signal and the mechanical signal. The results show that the algorithm is effective and can accurately diagnose the location of mechanical fault.展开更多
After research on a 2000t/h subcritical forced-circulation balanced ventilation were applied boilerand the structure and operation d its auxillary system builds up thls heat transfer model of a superheater's pipe ...After research on a 2000t/h subcritical forced-circulation balanced ventilation were applied boilerand the structure and operation d its auxillary system builds up thls heat transfer model of a superheater's pipe wall and analyze the effect of primary factors on the overtemperature of the pipe wall. Fault tree structure was used to uncover the multiplayer logic between the overternpemture of the superheater' s pipe wall and the faults.展开更多
In this paper, a t/(t+1)-diagnosable system is studied, which can locate a set S with |S|≤t+1 containing all faulty units only if the system has at most t faulty units. On the basis of the characterization of the t/(...In this paper, a t/(t+1)-diagnosable system is studied, which can locate a set S with |S|≤t+1 containing all faulty units only if the system has at most t faulty units. On the basis of the characterization of the t/(t+1)-diagnosable system, a necessary and sufficient condition is presented to judge whether a system is t/(t+1)-diagnosable. Meanwhile, this paper exposes some new and important properties of the t/(t+1)-diagnosable system to present the t/(t+1)-diagnosability of some networks. Furthermore, the following results for the t/(t+1)-diagnosability of some special networks are obtained: a hypercube network of n -dimensions is (3n-5)/(3n-4)-diagnosable, a star network of n -dimensions is (3n-5)/(3n-4)-diagnosable (n≥5) and a 2D-mesh (3D-mesh) with n 2(n 3) units is 8/9-diagnosable (11/12-diagnosable). This paper shows that in general, the t/(t+1)-diagnosability of a system is not only larger than its t/t -diagnosability , but also its classic diagnosability, specially the t/(t+1)-diagnosability of the hypercube network of n -dimensions is about 3 times as large as its classic t -diagnosability and about 1.5 times as large as its t/t -diagnosability.展开更多
Fault diagnose of the roller overrunning clutch is a headache problem in engineering at home and abroad. This paper introduces a new method to solve the problem by using the wavelet transform to separate fault si gnal...Fault diagnose of the roller overrunning clutch is a headache problem in engineering at home and abroad. This paper introduces a new method to solve the problem by using the wavelet transform to separate fault si gnal and further analyzing the impact frequency. The signal local singularities under the wavelet transform are studied. According to the propagation features of modulus maximums of the fault signal and the noise under the wavelet transfor m different on the scales, and by use of the signal wavelet decomposition-recon struction algorithm, the clutch shell vibration acceleration signal is decompose d, denoised, and reconstructed.The signal-to-noise of the monitored signal imp roved greatly.The fault characteristic signal on time domain is positioned.The f ault characteristic frequency is picked up. Experiment shows that it is quite effective.展开更多
In order to increase the efficiency and decrease the cost of machinerydiagnosis, a hybrid system of computational intelligence methods is presented. Firstly, thecontinuous attributes in diagnosis decision system are d...In order to increase the efficiency and decrease the cost of machinerydiagnosis, a hybrid system of computational intelligence methods is presented. Firstly, thecontinuous attributes in diagnosis decision system are discretized with the self-organizing map(SOM) neural network. Then, dynamic reducts are computed based on rough set method, and the keyconditions for diagnosis are found according to the maximum cluster ratio. Lastly, according to theoptimal reduct, the adaptive neuro-fuzzy inference system (ANFIS) is designed for faultidentification. The diagnosis of a diesel verifies the feasibility of engineering applications.展开更多
Petri net model is applied to diagnose the permanent fault of hydraulic system within the framework of interpreted Petri net. The permanent fault is described as redundant structure of the model. A definition and a th...Petri net model is applied to diagnose the permanent fault of hydraulic system within the framework of interpreted Petri net. The permanent fault is described as redundant structure of the model. A definition and a theorem are proposed to determine the diagnosability of the hydraulic system. The relations bwtween the diagnosability and other structure properties are also discussed. An example of actual hydraulic system is presented and its permanent fault can be diagnosed by the proposed method efficiently.展开更多
System-level fault identification is a key subject for maintaining the reliability of multiprocessor interconnected systems. This task requires fast and accurate inferences based on big volume of data, and the problem...System-level fault identification is a key subject for maintaining the reliability of multiprocessor interconnected systems. This task requires fast and accurate inferences based on big volume of data, and the problem of fault identification in an unstructured graph has been proved to be NP-hard (non-deterministic polynomial-time hard). In this paper, we adopt the PMC diagnostic model (first proposed by Preparata, Metze, and Chien) as the foundation of point-to-point probing technology, and a system contains only restricted-faults if every of its fault-free units has at least one fault-free neighbor. Under this condition we propose an efficient method of identifying restricted-faults in the folded hypercube, which is a promising alternative to the popular hypercube topology.展开更多
This paper describes, by means of a Voronoi hypersphere, the nearest neighbor relations of all the feature submatrices in the fault classification space and analyses the deviation disturbance angles between fault feat...This paper describes, by means of a Voronoi hypersphere, the nearest neighbor relations of all the feature submatrices in the fault classification space and analyses the deviation disturbance angles between fault feature submatrices and a k-dimension unitary matrix of the measured voltage change matrix. With the above two concepts, this paper discusses the diagnos-ability in the fault classification approach. The paper also classifies and defines the fault diagnosis problems. Finally, the paper derives the corresponding necessary and sufficient conditions for correct location of faults.展开更多
The prerequisite for the existing protocols' correctness is that protocols can be normally operated under the normal conditions, rather than dealing with abnormal conditions. In other words, protocols with the fau...The prerequisite for the existing protocols' correctness is that protocols can be normally operated under the normal conditions, rather than dealing with abnormal conditions. In other words, protocols with the fault-tolerance can not be provided when some fault occurs. This paper discusses the self fault-tolerance of protocols. It describes some concepts and methods for achieving self fault-tolerance of protocols. Meanwhile, it provides a case study, investigates a typical protocol that does not satisfy the self fault-tolerance, and gives a new redesign version of this existing protocol using the proposed approach.展开更多
In order to increase robustness of the AERS (Aero-engine Rotor System) and to solve the problem of lacking fault samples in fault diagnosis and the difficulty in identifying early weak fault, we proposed a new method ...In order to increase robustness of the AERS (Aero-engine Rotor System) and to solve the problem of lacking fault samples in fault diagnosis and the difficulty in identifying early weak fault, we proposed a new method that it not only can identify the early fault of AERS but also it can do self-recovery monitoring of fault. Our method is based on the analysis of the early fault features on AERS, and it combined the SVM (Support Vector Machine) with the stochastic resonance theory and the wavelet packet decomposition and fault self-recovery. First, we zoom the early fault feature signals by using the stochastic resonance theory. Second, we extract the feature vectors of early fault using the multi-resolution analysis of the wavelet packet. Third, we input the feature vectors to a fault classifier, which can be used to identify the early fault of AERS and carry out self-recovery monitoring of fault. In this paper, features of early fault on AERS, the zoom of early fault characteristics, the extraction method of early fault characteristics, the construction of multi-fault classifier and way of fault self-recovery monitoring are studied. Results show that our method can effectively identify the early fault of AERS, especially for identifying of fault with small samples, and it can carry on self-recovery monitoring of fault.展开更多
Dynamically reconfigurable Field Programmable Gate Array(dr-FPGA) based electronic systems on board mission-critical systems are highly susceptible to radiation induced hazards that may lead to faults in the logic or ...Dynamically reconfigurable Field Programmable Gate Array(dr-FPGA) based electronic systems on board mission-critical systems are highly susceptible to radiation induced hazards that may lead to faults in the logic or in the configuration memory. The aim of our research is to characterize self-test and repair processes in Fault Tolerant(FT) dr-FPGA systems in the presence of environmental faults and explore their interrelationships. We develop a Continuous Time Markov Chain(CTMC) model that captures the high level fail-repair processes on a dr-FPGA with periodic online Built-In Self-Test(BIST) and scrubbing to detect and repair faults with minimum latency. Simulation results reveal that given an average fault interval of 36 s, an optimum self-test interval of 48.3 s drives the system to spend 13% of its time in self-tests, remain in safe working states for 76% of its time and face risky fault-prone states for only 7% of its time. Further, we demonstrate that a well-tuned repair strategy boosts overall system availability, minimizes the occurrence of unsafe states, and accommodates a larger range of fault rates within which the system availability remains stable within 10% of its maximum level.展开更多
The article describes an approach to building a self-learning diagnostic algorithm. The self-learning algorithm creates models of the object under consideration. The models are formed periodically through a certain ti...The article describes an approach to building a self-learning diagnostic algorithm. The self-learning algorithm creates models of the object under consideration. The models are formed periodically through a certain time period. The model includes a set of functions that can describe whole object, or a part of the object, or a specified functionality of the object. Thus, information about fault location can be obtained. During operation of the object the algorithm collects data received from sensors. Then the algorithm creates samples related to steady state operation. Clustering of those samples is used for the functions definition. Values of the functions in the centers of clusters are stored in the computer’s memory. To illustrate the considered approach, its application to the diagnosis of turbomachines is described.展开更多
基金Supported by the National Basic Research Program of China (2013CB733600), the National Natural Science Foundation of China (21176073), the Doctoral Fund of Ministry of Education of China (20090074110005), the Program for New Century Excellent Talents in University (NCET-09-0346), Shu Guang Project (09SG29) and the Fundamental Research Funds for the Central Universities.
文摘Fault diagnosis and monitoring are very important for complex chemical process. There are numerous methods that have been studied in this field, in which the effective visualization method is still challenging. In order to get a better visualization effect, a novel fault diagnosis method which combines self-organizing map (SOM) with Fisher discriminant analysis (FDA) is proposed. FDA can reduce the dimension of the data in terms of maximizing the separability of the classes. After feature extraction by FDA, SOM can distinguish the different states on the output map clearly and it can also be employed to monitor abnormal states. Tennessee Eastman (TE) process is employed to illustrate the fault diagnosis and monitoring performance of the proposed method. The result shows that the SOM integrated with FDA method is efficient and capable for real-time monitoring and fault diagnosis in complex chemical process.
基金Project(2013CB733605)supported by the National Basic Research Program of ChinaProject(21176073)supported by the National Natural Science Foundation of ChinaProject supported by the Fundamental Research Funds for the Central Universities,China
文摘A multivariate method for fault diagnosis and process monitoring is proposed. This technique is based on a statistical pattern(SP) framework integrated with a self-organizing map(SOM). An SP-based SOM is used as a classifier to distinguish various states on the output map, which can visually monitor abnormal states. A case study of the Tennessee Eastman(TE) process is presented to demonstrate the fault diagnosis and process monitoring performance of the proposed method. Results show that the SP-based SOM method is a visual tool for real-time monitoring and fault diagnosis that can be used in complex chemical processes.Compared with other SOM-based methods, the proposed method can more efficiently monitor and diagnose faults.
文摘All kinds of reasons are analysed in theory and a fault repository combined with local expert experiences is establishedaccording to the structure and the operation characteristic of steam generator in this paper. At the same time, Kohonen algo-rithm is used for fault diagnoses system based on fuzzy neural networks. Fuzzy arithmetic is inducted into neural networks tosolve uncertain diagnosis induced by uncertain knowledge. According to its self-association in the course of default diagnosis. thesystem is provided with non-supervise, self-organizing, self-learning, and has strong cluster ability and fast cluster velocity.
基金Supported by the Basic Research Foundation of Beijing Institute of Technology(200705422009)
文摘In order to diagnose the working status of each module on sensor node and make sure the wireless sensor networks (WSN) work properly, the components of sensor node and their working characteristics are studied. An on-line fault self-diagnosis method for sensor node is proposed. First, a flexible fault sensing circuit is designed as a state detection module on sensor node. Second, a self- diagnosis algorithm is proposed based on the hardware design and the failure analysis on sensor node. Finally, in order to ensure the WSN reliability, the voltage changes of each module working statuses can be observed using the state detection module and the faulty module will be found out timely. The experimental results show that this self-diagnosis method is suitable to sensor nodes in WSN.
基金Sponsored by the National Natural Science Foundation of China(Grant No. NSFC-60572010)
文摘To solve the fault diagnosis problem of liquid propellant rocket engine ground testing bed,a fault diagnosis approach based on self-organizing map(SOM)is proposed.The SOM projects the multidimensional ground testing bed data into a two-dimensional map.Visualization of the SOM is used to cluster the ground testing bed data.The out map of the SOM is divided to several regions.Each region is represented for one fault mode.The fault mode of testing data is determined according to the region of their labels belonged to.The method is evaluated using the testing data of a liquid-propellant rocket engine ground testing bed with sixteen fault states.The results show that it is a reliable and effective method for fault diagnosis with good visualization property.
基金the Natural Science Foundation of Fujian Province,China(2019J05024)the Education Department Foundation of Fujian Province,China(JAT170091).
文摘Up to present,the problem of the evaluation of fault diagnosability for nonlinear systems has been investigated by many researchers.However,no attempt has been done to evaluate the diagnosability of multiple faults occurring simultaneously for nonlinear systems.This paper proposes a method based on differential geometry theories to solve this problem.Then the evaluation of fault diagnosability for affine nonlinear systems with multiple faults occurring simultaneously is achieved.To deal with the effect of control laws on the evaluation results of fault diagnosability,a design scheme of the evaluation of fault diagnosability is proposed.Then the influence of uncertainties on the evaluation results of fault diagnosability for affine nonlinear systems with multiple faults occurring simultaneously is analyzed.The numerical simulation results are obtained to show the effectiveness of the proposed evaluation scheme of fault diagnosability.
文摘Aiming at the problem that ICA can only be confined to the condition that the number of observed signals is larger than the number of source signals;a single channel blind source separation method combining EEMD, PCA and RobustICA is proposed. Through the eemd decomposition of the single-channel mechanical vibration observation signal the multidimensional IMF components are obtained, and the principal component analysis (PCA) is performed on the matrix of these IMF components. The number of principal components is determined and a new matrix is generated to satisfy the overdetermined blind source separation conditions, the new matrix input RobustICA, to achieve the separation of the source signal. Finally, the isolated signals are respectively analyzed by the envelope spectrum, the fault frequency is extracted, and the fault type is judged according to the prior knowledge. The experiment was carried out by using the simulation signal and the mechanical signal. The results show that the algorithm is effective and can accurately diagnose the location of mechanical fault.
文摘After research on a 2000t/h subcritical forced-circulation balanced ventilation were applied boilerand the structure and operation d its auxillary system builds up thls heat transfer model of a superheater's pipe wall and analyze the effect of primary factors on the overtemperature of the pipe wall. Fault tree structure was used to uncover the multiplayer logic between the overternpemture of the superheater' s pipe wall and the faults.
基金Supported by the National Natural Science Foundation of China(No.61862003,61761006)the Natural Science Foundation of Guangxi of China(No.2018GXNSFDA281052)
文摘In this paper, a t/(t+1)-diagnosable system is studied, which can locate a set S with |S|≤t+1 containing all faulty units only if the system has at most t faulty units. On the basis of the characterization of the t/(t+1)-diagnosable system, a necessary and sufficient condition is presented to judge whether a system is t/(t+1)-diagnosable. Meanwhile, this paper exposes some new and important properties of the t/(t+1)-diagnosable system to present the t/(t+1)-diagnosability of some networks. Furthermore, the following results for the t/(t+1)-diagnosability of some special networks are obtained: a hypercube network of n -dimensions is (3n-5)/(3n-4)-diagnosable, a star network of n -dimensions is (3n-5)/(3n-4)-diagnosable (n≥5) and a 2D-mesh (3D-mesh) with n 2(n 3) units is 8/9-diagnosable (11/12-diagnosable). This paper shows that in general, the t/(t+1)-diagnosability of a system is not only larger than its t/t -diagnosability , but also its classic diagnosability, specially the t/(t+1)-diagnosability of the hypercube network of n -dimensions is about 3 times as large as its classic t -diagnosability and about 1.5 times as large as its t/t -diagnosability.
文摘Fault diagnose of the roller overrunning clutch is a headache problem in engineering at home and abroad. This paper introduces a new method to solve the problem by using the wavelet transform to separate fault si gnal and further analyzing the impact frequency. The signal local singularities under the wavelet transform are studied. According to the propagation features of modulus maximums of the fault signal and the noise under the wavelet transfor m different on the scales, and by use of the signal wavelet decomposition-recon struction algorithm, the clutch shell vibration acceleration signal is decompose d, denoised, and reconstructed.The signal-to-noise of the monitored signal imp roved greatly.The fault characteristic signal on time domain is positioned.The f ault characteristic frequency is picked up. Experiment shows that it is quite effective.
文摘In order to increase the efficiency and decrease the cost of machinerydiagnosis, a hybrid system of computational intelligence methods is presented. Firstly, thecontinuous attributes in diagnosis decision system are discretized with the self-organizing map(SOM) neural network. Then, dynamic reducts are computed based on rough set method, and the keyconditions for diagnosis are found according to the maximum cluster ratio. Lastly, according to theoptimal reduct, the adaptive neuro-fuzzy inference system (ANFIS) is designed for faultidentification. The diagnosis of a diesel verifies the feasibility of engineering applications.
基金Supported by the Beijing Education Committee Cooperation Building Foundation(XK100070532)
文摘Petri net model is applied to diagnose the permanent fault of hydraulic system within the framework of interpreted Petri net. The permanent fault is described as redundant structure of the model. A definition and a theorem are proposed to determine the diagnosability of the hydraulic system. The relations bwtween the diagnosability and other structure properties are also discussed. An example of actual hydraulic system is presented and its permanent fault can be diagnosed by the proposed method efficiently.
基金supported in part by the NSC under Grand No.NSC102-2221-E-468-018
文摘System-level fault identification is a key subject for maintaining the reliability of multiprocessor interconnected systems. This task requires fast and accurate inferences based on big volume of data, and the problem of fault identification in an unstructured graph has been proved to be NP-hard (non-deterministic polynomial-time hard). In this paper, we adopt the PMC diagnostic model (first proposed by Preparata, Metze, and Chien) as the foundation of point-to-point probing technology, and a system contains only restricted-faults if every of its fault-free units has at least one fault-free neighbor. Under this condition we propose an efficient method of identifying restricted-faults in the folded hypercube, which is a promising alternative to the popular hypercube topology.
文摘This paper describes, by means of a Voronoi hypersphere, the nearest neighbor relations of all the feature submatrices in the fault classification space and analyses the deviation disturbance angles between fault feature submatrices and a k-dimension unitary matrix of the measured voltage change matrix. With the above two concepts, this paper discusses the diagnos-ability in the fault classification approach. The paper also classifies and defines the fault diagnosis problems. Finally, the paper derives the corresponding necessary and sufficient conditions for correct location of faults.
基金This project was supported by the National Natural Science Foundation of China and the Natural Science Foundation of Hubei Prov
文摘The prerequisite for the existing protocols' correctness is that protocols can be normally operated under the normal conditions, rather than dealing with abnormal conditions. In other words, protocols with the fault-tolerance can not be provided when some fault occurs. This paper discusses the self fault-tolerance of protocols. It describes some concepts and methods for achieving self fault-tolerance of protocols. Meanwhile, it provides a case study, investigates a typical protocol that does not satisfy the self fault-tolerance, and gives a new redesign version of this existing protocol using the proposed approach.
文摘In order to increase robustness of the AERS (Aero-engine Rotor System) and to solve the problem of lacking fault samples in fault diagnosis and the difficulty in identifying early weak fault, we proposed a new method that it not only can identify the early fault of AERS but also it can do self-recovery monitoring of fault. Our method is based on the analysis of the early fault features on AERS, and it combined the SVM (Support Vector Machine) with the stochastic resonance theory and the wavelet packet decomposition and fault self-recovery. First, we zoom the early fault feature signals by using the stochastic resonance theory. Second, we extract the feature vectors of early fault using the multi-resolution analysis of the wavelet packet. Third, we input the feature vectors to a fault classifier, which can be used to identify the early fault of AERS and carry out self-recovery monitoring of fault. In this paper, features of early fault on AERS, the zoom of early fault characteristics, the extraction method of early fault characteristics, the construction of multi-fault classifier and way of fault self-recovery monitoring are studied. Results show that our method can effectively identify the early fault of AERS, especially for identifying of fault with small samples, and it can carry on self-recovery monitoring of fault.
文摘Dynamically reconfigurable Field Programmable Gate Array(dr-FPGA) based electronic systems on board mission-critical systems are highly susceptible to radiation induced hazards that may lead to faults in the logic or in the configuration memory. The aim of our research is to characterize self-test and repair processes in Fault Tolerant(FT) dr-FPGA systems in the presence of environmental faults and explore their interrelationships. We develop a Continuous Time Markov Chain(CTMC) model that captures the high level fail-repair processes on a dr-FPGA with periodic online Built-In Self-Test(BIST) and scrubbing to detect and repair faults with minimum latency. Simulation results reveal that given an average fault interval of 36 s, an optimum self-test interval of 48.3 s drives the system to spend 13% of its time in self-tests, remain in safe working states for 76% of its time and face risky fault-prone states for only 7% of its time. Further, we demonstrate that a well-tuned repair strategy boosts overall system availability, minimizes the occurrence of unsafe states, and accommodates a larger range of fault rates within which the system availability remains stable within 10% of its maximum level.
文摘The article describes an approach to building a self-learning diagnostic algorithm. The self-learning algorithm creates models of the object under consideration. The models are formed periodically through a certain time period. The model includes a set of functions that can describe whole object, or a part of the object, or a specified functionality of the object. Thus, information about fault location can be obtained. During operation of the object the algorithm collects data received from sensors. Then the algorithm creates samples related to steady state operation. Clustering of those samples is used for the functions definition. Values of the functions in the centers of clusters are stored in the computer’s memory. To illustrate the considered approach, its application to the diagnosis of turbomachines is described.