A novel knowledge-based fuzzy neural network （KBFNN） for fault diagnosis is presented. Crude rules were extracted and the corresponding dependent factors and antecedent coverage factors were calculated firstly from ...A novel knowledge-based fuzzy neural network （KBFNN） for fault diagnosis is presented. Crude rules were extracted and the corresponding dependent factors and antecedent coverage factors were calculated firstly from the diagnostic sample based on rough sets theory. Then the number of rules was used to construct partially the structure of a fuzzy neural network and those factors were implemented as initial weights, with fuzzy output parameters being optimized by genetic algorithm. Such fuzzy neural network was called KBFNN. This KBFNN was utilized to identify typical faults of rotating machinery. Diagnostic results show that it has those merits of shorter training time and higher right diagnostic level compared to general fuzzy neural networks.展开更多
Objective Due to the incompleteness and complexity of fault diagnosis for power transformers,a comprehensive rough-fuzzy scheme for solving fault diagnosis problems is presented.Fuzzy set theory is used both for repre...Objective Due to the incompleteness and complexity of fault diagnosis for power transformers,a comprehensive rough-fuzzy scheme for solving fault diagnosis problems is presented.Fuzzy set theory is used both for representation of incipient faults' indications and producing a fuzzy granulation of the feature space.Rough set theory is used to obtain dependency rules that model indicative regions in the granulated feature space.The fuzzy membership functions corresponding to the indicative regions,modelled by rules,are stored as cases.Results Diagnostic conclusions are made using a similarity measure based on these membership functions.Each case involves only a reduced number of relevant features making this scheme suitable for fault diagnosis.Conclusion Superiority of this method in terms of classification accuracy and case generation is demonstrated.展开更多
Because of the stochastic property of fault occurrence and fuzziness offault phenomenon, machine fault diagnosis technique in use, such as fault tree analysis,cause consequence tree method, etc., cannot exactly descri...Because of the stochastic property of fault occurrence and fuzziness offault phenomenon, machine fault diagnosis technique in use, such as fault tree analysis,cause consequence tree method, etc., cannot exactly describe the properties of fault phe-nomenon and coherence of fault space. In this paper, based on the theory of generalPetri net, fault tree technique and theory of fuzzy set, a theory system of fuzzy Petri net(FPN) suitable for fault diagnosis is established, which is applied to an example of faultdiagnosis for FMS. This method has the properties of of rbjectivity, strong expressionability, easy inference, etc., which can solve the problems of stochastic property andfuzziness of fault.展开更多
The diesel locomotive plays an important role in the field of transport, and the engine maintenance work is the prerequisite and gnarantee for the locomotive normal working. In this paper, we first establish the fault...The diesel locomotive plays an important role in the field of transport, and the engine maintenance work is the prerequisite and gnarantee for the locomotive normal working. In this paper, we first establish the fault tree model of locomotive engine 16V240ZJ on the basis of engine non-start as the top event. Then we combines the fitzzy mathematics the- ory and fault tree analysis method for failure diagnosis of 16V240ZJ engine＇s abnormal start-up. We obtained the fuzzy probability curve and top events probability confidence interval by analyzing the fuzzy fault tree qualitatively and quantitatively. It provides a fuzzy analysis basis for solving the problem of 16V240ZJ engine＇s abnormal start-up.展开更多
This paper introduces concepts of symptom vector and fuzzy symptom vector forspacecraft condition recognition and fault diagnosis,defines an operator and suggests a fuzzy pat-tern recognition method of fault diagnosis...This paper introduces concepts of symptom vector and fuzzy symptom vector forspacecraft condition recognition and fault diagnosis,defines an operator and suggests a fuzzy pat-tern recognition method of fault diagnosis for spacecraft.This method is verified by examples andresults are checked from an expert system.展开更多
Being one of the most expensive components of an electrical power plant, the failures of a power transformer can result in serious power system issues. So fault diagnosis for power transformer is highly important to e...Being one of the most expensive components of an electrical power plant, the failures of a power transformer can result in serious power system issues. So fault diagnosis for power transformer is highly important to ensure an uninterrupted power supply. Due to information transmission mistakes as well as arisen errors while processing data in surveying and monitoring state information of transformer, uncertain and incomplete information may be produced. Based on these points, this paper presents an intelligent fault diagnosis method of power transformer using fuzzy fault tree analysis (FTA) and beta distribution for failure possibility estimation. By using the technique we proposed herein, the continuous attribute values are transformed into the fuzzy numbers to give a realistic estimate of failure possibility of a basic event in FTA. Further, it explains a new approach based on Euclidean distance between fuzzy numbers, to rank the basic events in accordance with their Fuzzy Importance Index.展开更多
Fault diagnostics is important for safe operation of nuclear power plants(NPPs). In recent years, data-driven approaches have been proposed and implemented to tackle the problem, e.g., neural networks, fuzzy and neuro...Fault diagnostics is important for safe operation of nuclear power plants(NPPs). In recent years, data-driven approaches have been proposed and implemented to tackle the problem, e.g., neural networks, fuzzy and neurofuzzy approaches, support vector machine, K-nearest neighbor classifiers and inference methodologies. Among these methods, dynamic uncertain causality graph(DUCG)has been proved effective in many practical cases. However, the causal graph construction behind the DUCG is complicate and, in many cases, results redundant on the symptoms needed to correctly classify the fault. In this paper, we propose a method to simplify causal graph construction in an automatic way. The method consists in transforming the expert knowledge-based DCUG into a fuzzy decision tree(FDT) by extracting from the DUCG a fuzzy rule base that resumes the used symptoms at the basis of the FDT. Genetic algorithm(GA) is, then, used for the optimization of the FDT, by performing a wrapper search around the FDT: the set of symptoms selected during the iterative search are taken as the best set of symptoms for the diagnosis of the faults that can occur in the system. The effectiveness of the approach is shown with respect to a DUCG model initially built to diagnose 23 faults originally using 262 symptoms of Unit-1 in the Ningde NPP of the China Guangdong Nuclear Power Corporation. The results show that the FDT, with GA-optimized symptoms and diagnosis strategy, can drive the construction of DUCG and lower the computational burden without loss of accuracy in diagnosis.展开更多
In this paper, the author develops some principles to structure a fault dictionary. The concept of a “standard fault dictionary” is raised and a framework for a fuzzy dictionary is given. Fault diagnosis aide by a f...In this paper, the author develops some principles to structure a fault dictionary. The concept of a “standard fault dictionary” is raised and a framework for a fuzzy dictionary is given. Fault diagnosis aide by a fuzzy fault dictionary is also introduced.展开更多
基金Project supported by the National Major Science and Technology Foundation of China during the 10th Five-Year Plan Period(No.2001BA204B05-KHK Z0009)
文摘A novel knowledge-based fuzzy neural network （KBFNN） for fault diagnosis is presented. Crude rules were extracted and the corresponding dependent factors and antecedent coverage factors were calculated firstly from the diagnostic sample based on rough sets theory. Then the number of rules was used to construct partially the structure of a fuzzy neural network and those factors were implemented as initial weights, with fuzzy output parameters being optimized by genetic algorithm. Such fuzzy neural network was called KBFNN. This KBFNN was utilized to identify typical faults of rotating machinery. Diagnostic results show that it has those merits of shorter training time and higher right diagnostic level compared to general fuzzy neural networks.
基金This work was supported by the National Natural Science Foundation of China(No.59637200).
文摘Objective Due to the incompleteness and complexity of fault diagnosis for power transformers,a comprehensive rough-fuzzy scheme for solving fault diagnosis problems is presented.Fuzzy set theory is used both for representation of incipient faults' indications and producing a fuzzy granulation of the feature space.Rough set theory is used to obtain dependency rules that model indicative regions in the granulated feature space.The fuzzy membership functions corresponding to the indicative regions,modelled by rules,are stored as cases.Results Diagnostic conclusions are made using a similarity measure based on these membership functions.Each case involves only a reduced number of relevant features making this scheme suitable for fault diagnosis.Conclusion Superiority of this method in terms of classification accuracy and case generation is demonstrated.
文摘Because of the stochastic property of fault occurrence and fuzziness offault phenomenon, machine fault diagnosis technique in use, such as fault tree analysis,cause consequence tree method, etc., cannot exactly describe the properties of fault phe-nomenon and coherence of fault space. In this paper, based on the theory of generalPetri net, fault tree technique and theory of fuzzy set, a theory system of fuzzy Petri net(FPN) suitable for fault diagnosis is established, which is applied to an example of faultdiagnosis for FMS. This method has the properties of of rbjectivity, strong expressionability, easy inference, etc., which can solve the problems of stochastic property andfuzziness of fault.
基金supported by Liaoning Technical University for out-standing young teachers
文摘The diesel locomotive plays an important role in the field of transport, and the engine maintenance work is the prerequisite and gnarantee for the locomotive normal working. In this paper, we first establish the fault tree model of locomotive engine 16V240ZJ on the basis of engine non-start as the top event. Then we combines the fitzzy mathematics the- ory and fault tree analysis method for failure diagnosis of 16V240ZJ engine＇s abnormal start-up. We obtained the fuzzy probability curve and top events probability confidence interval by analyzing the fuzzy fault tree qualitatively and quantitatively. It provides a fuzzy analysis basis for solving the problem of 16V240ZJ engine＇s abnormal start-up.
基金Supported by the National Natural Science Foundation of China and National Project No.863
文摘This paper introduces concepts of symptom vector and fuzzy symptom vector forspacecraft condition recognition and fault diagnosis,defines an operator and suggests a fuzzy pat-tern recognition method of fault diagnosis for spacecraft.This method is verified by examples andresults are checked from an expert system.
文摘Being one of the most expensive components of an electrical power plant, the failures of a power transformer can result in serious power system issues. So fault diagnosis for power transformer is highly important to ensure an uninterrupted power supply. Due to information transmission mistakes as well as arisen errors while processing data in surveying and monitoring state information of transformer, uncertain and incomplete information may be produced. Based on these points, this paper presents an intelligent fault diagnosis method of power transformer using fuzzy fault tree analysis (FTA) and beta distribution for failure possibility estimation. By using the technique we proposed herein, the continuous attribute values are transformed into the fuzzy numbers to give a realistic estimate of failure possibility of a basic event in FTA. Further, it explains a new approach based on Euclidean distance between fuzzy numbers, to rank the basic events in accordance with their Fuzzy Importance Index.
文摘Fault diagnostics is important for safe operation of nuclear power plants(NPPs). In recent years, data-driven approaches have been proposed and implemented to tackle the problem, e.g., neural networks, fuzzy and neurofuzzy approaches, support vector machine, K-nearest neighbor classifiers and inference methodologies. Among these methods, dynamic uncertain causality graph(DUCG)has been proved effective in many practical cases. However, the causal graph construction behind the DUCG is complicate and, in many cases, results redundant on the symptoms needed to correctly classify the fault. In this paper, we propose a method to simplify causal graph construction in an automatic way. The method consists in transforming the expert knowledge-based DCUG into a fuzzy decision tree(FDT) by extracting from the DUCG a fuzzy rule base that resumes the used symptoms at the basis of the FDT. Genetic algorithm(GA) is, then, used for the optimization of the FDT, by performing a wrapper search around the FDT: the set of symptoms selected during the iterative search are taken as the best set of symptoms for the diagnosis of the faults that can occur in the system. The effectiveness of the approach is shown with respect to a DUCG model initially built to diagnose 23 faults originally using 262 symptoms of Unit-1 in the Ningde NPP of the China Guangdong Nuclear Power Corporation. The results show that the FDT, with GA-optimized symptoms and diagnosis strategy, can drive the construction of DUCG and lower the computational burden without loss of accuracy in diagnosis.
文摘In this paper, the author develops some principles to structure a fault dictionary. The concept of a “standard fault dictionary” is raised and a framework for a fuzzy dictionary is given. Fault diagnosis aide by a fuzzy fault dictionary is also introduced.
