By introducing Rough Set Theory and the principle of Support vector machine,a gear fault diagnosis method based on them is proposed.Firstly,diagnostic decision-making is reduced based on rough set theory,and the noise...By introducing Rough Set Theory and the principle of Support vector machine,a gear fault diagnosis method based on them is proposed.Firstly,diagnostic decision-making is reduced based on rough set theory,and the noise and redundancy in the sample are removed,then,according to the chosen reduction,a support vector machine multi-classifier is designed for gear fault diagnosis.Therefore,SVM’training data can be reduced and running speed can quicken.Test shows its accuracy and effi- ciency of gear fault diagnosis.展开更多
A robust fault diagnosis approach is developed by incorporating a set-membership identification (SMI) method. A class of systems with linear models in the form of fault related parameters is investigated, with model u...A robust fault diagnosis approach is developed by incorporating a set-membership identification (SMI) method. A class of systems with linear models in the form of fault related parameters is investigated, with model uncertainties and parameter variations taken into account explicitly and treated as bounded errors. An ellipsoid bounding set-membership identification algorithm is proposed to propagate bounded uncertainties rigorously and the guaranteed feasible set of faults parameters enveloping true parameter values is given. Faults arised from abrupt parameter variations can be detected and isolated on-line by consistency check between predicted and observed parameter sets obtained in the identification procedure. The proposed approach provides the improved robustness with its ability to distinguish real faults from model uncertainties, which comes with the inherent guaranteed robustness of the set-membership framework. Efforts are also made in this work to balance between conservativeness and computation complexity of the overall algorithm. Simulation results for the mobile robot with several slipping faults scenarios demonstrate the correctness of the proposed approach for faults detection and isolation (FDI).展开更多
There exists some discord or contradiction of information during the process of fault diagnosis for rotary machine. But the traditional methods used in fault diagnosis can not dispose of the information. A model of fa...There exists some discord or contradiction of information during the process of fault diagnosis for rotary machine. But the traditional methods used in fault diagnosis can not dispose of the information. A model of fault diagnosis for a rotary machine based on information entropy theory and rough set theory is presented in this paper. The model has clear mathematical definition and can dispose both complete unification information and complete inconsistent information of vibration faults. By using the model, decision rules of six typical vibration faults of a steam turbine and electric generating set are deduced from experiment samples. Finally, the decision rules are validated by selected samples and good identification results are acquired.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
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
Minimal cut sets （or prime implicants： minimal combinations of basic event conditions leading to system failure） are important information for reliability/safety analysis and design. To obtain minimal cut sets for ...Minimal cut sets （or prime implicants： minimal combinations of basic event conditions leading to system failure） are important information for reliability/safety analysis and design. To obtain minimal cut sets for general non-coherent fault trees, including negative basic events or multi-valued basic events, a special procedure such as the consensus rule must be applied to the results obtained by logical operations for coherent fault trees, which will require more steps and time. This paper proposes a simple method for a non-coherent fault tree, whose top event is represented as an AND combination of monotonic sub-trees. A ＂monotonic＂ sub-tree means that it does not have both positive and negative representations for each basic event. It is proven that minimal cut sets can be obtained by a conventional method for coherent fault trees. An illustrative example of a simple event tree analysis shows the detail and characteristics of the proposed method.展开更多
A novel algorithm named randomized binary gravita- tional search （RBGS） algorithm is proposed for the set covering problem （SCP）. It differs from previous SCP approaches because it does not work directly on the SC...A novel algorithm named randomized binary gravita- tional search （RBGS） algorithm is proposed for the set covering problem （SCP）. It differs from previous SCP approaches because it does not work directly on the SCP matrix. In the proposed algo- rithm, the solution of SCP is viewed as multi-dimension position of objects in the binary search space. All objects in the space attract each other by the gravity force, and this force causes a global movement of all objects towards the objects with heavier masses which correspond to good solutions. Computation results show that the proposed algorithm is very competitive. In addition, the proposed aigodthm is extended for SCP to solve the fault diagno- sis problem in graph-based systems.展开更多
As the first step of service restoration of distribution system,rapid fault diagnosis is a significant task for reducing power outage time,decreasing outage loss,and subsequently improving service reliability and safe...As the first step of service restoration of distribution system,rapid fault diagnosis is a significant task for reducing power outage time,decreasing outage loss,and subsequently improving service reliability and safety.This paper analyzes a fault diagnosis approach by using rough set theory in which how to reduce decision table of data set is a main calculation intensive task.Aiming at this reduction problem,a heuristic reduction algorithm based on attribution length and frequency is proposed.At the same time,the corresponding value reduction method is proposed in order to fulfill the reduction and diagnosis rules extraction.Meanwhile,a Euclid matching method is introduced to solve confliction problems among the extracted rules when some information is lacking.Principal of the whole algorithm is clear and diagnostic rules distilled from the reduction are concise.Moreover,it needs less calculation towards specific discernibility matrix,and thus avoids the corresponding NP hard problem.The whole process is realized by MATLAB programming.A simulation example shows that the method has a fast calculation speed,and the extracted rules can reflect the characteristic of fault with a concise form.The rule database,formed by different reduction of decision table,can diagnose single fault and multi-faults efficiently,and give satisfied results even when the existed information is incomplete.The proposed method has good error-tolerate capability and the potential for on-line fault diagnosis.展开更多
A new fault tree analysis (FTA) computation method is put forth by using modularization technique in FTA with cut sets matrix, and can reduce NP (Nondeterministic polynomial) difficulty effectively. This software can ...A new fault tree analysis (FTA) computation method is put forth by using modularization technique in FTA with cut sets matrix, and can reduce NP (Nondeterministic polynomial) difficulty effectively. This software can run in IBM PC and DOS 3.0 and up. The method provides theoretical basis and computation tool for application of FTA technique in the common engineering system展开更多
文摘By introducing Rough Set Theory and the principle of Support vector machine,a gear fault diagnosis method based on them is proposed.Firstly,diagnostic decision-making is reduced based on rough set theory,and the noise and redundancy in the sample are removed,then,according to the chosen reduction,a support vector machine multi-classifier is designed for gear fault diagnosis.Therefore,SVM’training data can be reduced and running speed can quicken.Test shows its accuracy and effi- ciency of gear fault diagnosis.
基金supported by the National Natural Science Foundation of China(616732546157310061573101)
文摘A robust fault diagnosis approach is developed by incorporating a set-membership identification (SMI) method. A class of systems with linear models in the form of fault related parameters is investigated, with model uncertainties and parameter variations taken into account explicitly and treated as bounded errors. An ellipsoid bounding set-membership identification algorithm is proposed to propagate bounded uncertainties rigorously and the guaranteed feasible set of faults parameters enveloping true parameter values is given. Faults arised from abrupt parameter variations can be detected and isolated on-line by consistency check between predicted and observed parameter sets obtained in the identification procedure. The proposed approach provides the improved robustness with its ability to distinguish real faults from model uncertainties, which comes with the inherent guaranteed robustness of the set-membership framework. Efforts are also made in this work to balance between conservativeness and computation complexity of the overall algorithm. Simulation results for the mobile robot with several slipping faults scenarios demonstrate the correctness of the proposed approach for faults detection and isolation (FDI).
基金The paper is supported by the 863 Program of China under Grant No 2006AA04A110
文摘There exists some discord or contradiction of information during the process of fault diagnosis for rotary machine. But the traditional methods used in fault diagnosis can not dispose of the information. A model of fault diagnosis for a rotary machine based on information entropy theory and rough set theory is presented in this paper. The model has clear mathematical definition and can dispose both complete unification information and complete inconsistent information of vibration faults. By using the model, decision rules of six typical vibration faults of a steam turbine and electric generating set are deduced from experiment samples. Finally, the decision rules are validated by selected samples and good identification results are acquired.
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Minimal cut sets （or prime implicants： minimal combinations of basic event conditions leading to system failure） are important information for reliability/safety analysis and design. To obtain minimal cut sets for general non-coherent fault trees, including negative basic events or multi-valued basic events, a special procedure such as the consensus rule must be applied to the results obtained by logical operations for coherent fault trees, which will require more steps and time. This paper proposes a simple method for a non-coherent fault tree, whose top event is represented as an AND combination of monotonic sub-trees. A ＂monotonic＂ sub-tree means that it does not have both positive and negative representations for each basic event. It is proven that minimal cut sets can be obtained by a conventional method for coherent fault trees. An illustrative example of a simple event tree analysis shows the detail and characteristics of the proposed method.
基金supported by the National Natural Science Foundation of China (4100605850909096)
文摘A novel algorithm named randomized binary gravita- tional search （RBGS） algorithm is proposed for the set covering problem （SCP）. It differs from previous SCP approaches because it does not work directly on the SCP matrix. In the proposed algo- rithm, the solution of SCP is viewed as multi-dimension position of objects in the binary search space. All objects in the space attract each other by the gravity force, and this force causes a global movement of all objects towards the objects with heavier masses which correspond to good solutions. Computation results show that the proposed algorithm is very competitive. In addition, the proposed aigodthm is extended for SCP to solve the fault diagno- sis problem in graph-based systems.
基金Project Supported by National Natural Science Foundation of China （50607023）, Natural Science Femdation of CQ CSTC （2006BB2189）
文摘As the first step of service restoration of distribution system,rapid fault diagnosis is a significant task for reducing power outage time,decreasing outage loss,and subsequently improving service reliability and safety.This paper analyzes a fault diagnosis approach by using rough set theory in which how to reduce decision table of data set is a main calculation intensive task.Aiming at this reduction problem,a heuristic reduction algorithm based on attribution length and frequency is proposed.At the same time,the corresponding value reduction method is proposed in order to fulfill the reduction and diagnosis rules extraction.Meanwhile,a Euclid matching method is introduced to solve confliction problems among the extracted rules when some information is lacking.Principal of the whole algorithm is clear and diagnostic rules distilled from the reduction are concise.Moreover,it needs less calculation towards specific discernibility matrix,and thus avoids the corresponding NP hard problem.The whole process is realized by MATLAB programming.A simulation example shows that the method has a fast calculation speed,and the extracted rules can reflect the characteristic of fault with a concise form.The rule database,formed by different reduction of decision table,can diagnose single fault and multi-faults efficiently,and give satisfied results even when the existed information is incomplete.The proposed method has good error-tolerate capability and the potential for on-line fault diagnosis.
文摘A new fault tree analysis (FTA) computation method is put forth by using modularization technique in FTA with cut sets matrix, and can reduce NP (Nondeterministic polynomial) difficulty effectively. This software can run in IBM PC and DOS 3.0 and up. The method provides theoretical basis and computation tool for application of FTA technique in the common engineering system
