Research on Fault Diagnosis for Pumping Station Based on T-S Fuzzy Fault Tree and Bayesian Network
According to the characteristics of fault diagnosis for pumping station, such as the complex structure, multiple mappings, and numerous uncertainties, a new approach combining T-S fuzzy gate fault tree and Bayesian network (BN) is proposed. On the one hand, traditional fault tree method needs the lo...
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| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
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Wiley
2017-01-01
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| Series: | Journal of Electrical and Computer Engineering |
| Online Access: | http://dx.doi.org/10.1155/2017/6175429 |
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| _version_ | 1832561455938928640 |
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| author | Zhuqing Bi Chenming Li Xujie Li Hongmin Gao |
| author_facet | Zhuqing Bi Chenming Li Xujie Li Hongmin Gao |
| author_sort | Zhuqing Bi |
| collection | DOAJ |
| description | According to the characteristics of fault diagnosis for pumping station, such as the complex structure, multiple mappings, and numerous uncertainties, a new approach combining T-S fuzzy gate fault tree and Bayesian network (BN) is proposed. On the one hand, traditional fault tree method needs the logical relationship between events and probability value of events and can only represent the events with two states. T-S fuzzy gate fault tree method can solve these disadvantages but still has weaknesses in complex reasoning and only one-way reasoning. On the other hand, the BN is suitable for fault diagnosis of pumping station because of its powerful ability to deal with uncertain information. However, it is difficult to determine the structure and conditional probability tables of the BN. Therefore, the proposed method integrates the advantages of the two methods. Finally, the feasibility of the method is verified through a fault diagnosis model of the rotor in the pumping unit, the accuracy of the method is verified by comparing with the methods based on traditional Bayesian network and BP neural network, respectively, when the historical data is sufficient, and the results are more superior to the above two when the historical data is insufficient. |
| format | Article |
| id | doaj-art-c0ef7da244f54e83833bfa7c970386c5 |
| institution | Kabale University |
| issn | 2090-0147 2090-0155 |
| language | English |
| publishDate | 2017-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | Journal of Electrical and Computer Engineering |
| spelling | doaj-art-c0ef7da244f54e83833bfa7c970386c52025-02-03T01:24:59ZengWileyJournal of Electrical and Computer Engineering2090-01472090-01552017-01-01201710.1155/2017/61754296175429Research on Fault Diagnosis for Pumping Station Based on T-S Fuzzy Fault Tree and Bayesian NetworkZhuqing Bi0Chenming Li1Xujie Li2Hongmin Gao3College of Computer and Information Engineering, Hohai University, Nanjing 211100, ChinaCollege of Computer and Information Engineering, Hohai University, Nanjing 211100, ChinaCollege of Computer and Information Engineering, Hohai University, Nanjing 211100, ChinaCollege of Computer and Information Engineering, Hohai University, Nanjing 211100, ChinaAccording to the characteristics of fault diagnosis for pumping station, such as the complex structure, multiple mappings, and numerous uncertainties, a new approach combining T-S fuzzy gate fault tree and Bayesian network (BN) is proposed. On the one hand, traditional fault tree method needs the logical relationship between events and probability value of events and can only represent the events with two states. T-S fuzzy gate fault tree method can solve these disadvantages but still has weaknesses in complex reasoning and only one-way reasoning. On the other hand, the BN is suitable for fault diagnosis of pumping station because of its powerful ability to deal with uncertain information. However, it is difficult to determine the structure and conditional probability tables of the BN. Therefore, the proposed method integrates the advantages of the two methods. Finally, the feasibility of the method is verified through a fault diagnosis model of the rotor in the pumping unit, the accuracy of the method is verified by comparing with the methods based on traditional Bayesian network and BP neural network, respectively, when the historical data is sufficient, and the results are more superior to the above two when the historical data is insufficient.http://dx.doi.org/10.1155/2017/6175429 |
| spellingShingle | Zhuqing Bi Chenming Li Xujie Li Hongmin Gao Research on Fault Diagnosis for Pumping Station Based on T-S Fuzzy Fault Tree and Bayesian Network Journal of Electrical and Computer Engineering |
| title | Research on Fault Diagnosis for Pumping Station Based on T-S Fuzzy Fault Tree and Bayesian Network |
| title_full | Research on Fault Diagnosis for Pumping Station Based on T-S Fuzzy Fault Tree and Bayesian Network |
| title_fullStr | Research on Fault Diagnosis for Pumping Station Based on T-S Fuzzy Fault Tree and Bayesian Network |
| title_full_unstemmed | Research on Fault Diagnosis for Pumping Station Based on T-S Fuzzy Fault Tree and Bayesian Network |
| title_short | Research on Fault Diagnosis for Pumping Station Based on T-S Fuzzy Fault Tree and Bayesian Network |
| title_sort | research on fault diagnosis for pumping station based on t s fuzzy fault tree and bayesian network |
| url | http://dx.doi.org/10.1155/2017/6175429 |
| work_keys_str_mv | AT zhuqingbi researchonfaultdiagnosisforpumpingstationbasedontsfuzzyfaulttreeandbayesiannetwork AT chenmingli researchonfaultdiagnosisforpumpingstationbasedontsfuzzyfaulttreeandbayesiannetwork AT xujieli researchonfaultdiagnosisforpumpingstationbasedontsfuzzyfaulttreeandbayesiannetwork AT hongmingao researchonfaultdiagnosisforpumpingstationbasedontsfuzzyfaulttreeandbayesiannetwork |