A Modified FNN Fault Diagnosis on PCVD Microwave System
A modified FNN fault diagnosis algorithm is presented in this paper for microwave subsystem of Plasma Chemical Vapor Deposition (PCVD). The symptom variables are selected as the crisp inputs, and the corresponding membership functions are obtained from premeasured data as well as experts’ diagnostic...
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| Format: | Article |
| Language: | English |
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Wiley
2015-01-01
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| Series: | Advances in Fuzzy Systems |
| Online Access: | http://dx.doi.org/10.1155/2015/632456 |
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| _version_ | 1832567538584649728 |
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| author | Zhenyu Li Hongsheng Li |
| author_facet | Zhenyu Li Hongsheng Li |
| author_sort | Zhenyu Li |
| collection | DOAJ |
| description | A modified FNN fault diagnosis algorithm is presented in this paper for microwave subsystem of Plasma Chemical Vapor Deposition (PCVD). The symptom variables are selected as the crisp inputs, and the corresponding membership functions are obtained from premeasured data as well as experts’ diagnostic experience/knowledge. The prior probability and the restriction coefficients are combined into the FNN algorithm via matrix operator. This modified FNN algorithm is verified for PCVD fault diagnosis application and realizes the MIMO for multifault mode diagnosis. |
| format | Article |
| id | doaj-art-a9586d812965402b942a118c1c179513 |
| institution | Kabale University |
| issn | 1687-7101 1687-711X |
| language | English |
| publishDate | 2015-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | Advances in Fuzzy Systems |
| spelling | doaj-art-a9586d812965402b942a118c1c1795132025-02-03T01:01:18ZengWileyAdvances in Fuzzy Systems1687-71011687-711X2015-01-01201510.1155/2015/632456632456A Modified FNN Fault Diagnosis on PCVD Microwave SystemZhenyu Li0Hongsheng Li1Wuhan University of Technology, Wuhan 430070, ChinaWuhan University of Technology, Wuhan 430070, ChinaA modified FNN fault diagnosis algorithm is presented in this paper for microwave subsystem of Plasma Chemical Vapor Deposition (PCVD). The symptom variables are selected as the crisp inputs, and the corresponding membership functions are obtained from premeasured data as well as experts’ diagnostic experience/knowledge. The prior probability and the restriction coefficients are combined into the FNN algorithm via matrix operator. This modified FNN algorithm is verified for PCVD fault diagnosis application and realizes the MIMO for multifault mode diagnosis.http://dx.doi.org/10.1155/2015/632456 |
| spellingShingle | Zhenyu Li Hongsheng Li A Modified FNN Fault Diagnosis on PCVD Microwave System Advances in Fuzzy Systems |
| title | A Modified FNN Fault Diagnosis on PCVD Microwave System |
| title_full | A Modified FNN Fault Diagnosis on PCVD Microwave System |
| title_fullStr | A Modified FNN Fault Diagnosis on PCVD Microwave System |
| title_full_unstemmed | A Modified FNN Fault Diagnosis on PCVD Microwave System |
| title_short | A Modified FNN Fault Diagnosis on PCVD Microwave System |
| title_sort | modified fnn fault diagnosis on pcvd microwave system |
| url | http://dx.doi.org/10.1155/2015/632456 |
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