A Fault Prediction Model of Adaptive Fuzzy Neural Network for Optimal Membership Function
As an essential and challenging technology of fault prediction and health management(PHM), fault prediction technology has been a research focus in the field of fault diagnosis. However, the current model-based fault prediction technology and data-driven fault prediction technology have some limitat...
| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
IEEE
2020-01-01
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| Series: | IEEE Access |
| Subjects: | |
| Online Access: | https://ieeexplore.ieee.org/document/9099510/ |
| _version_ | 1818735619587702784 |
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| author | Baoshan Zhang Lin Zhang Bo Zhang Bofan Yang Yanchao Zhao |
| author_facet | Baoshan Zhang Lin Zhang Bo Zhang Bofan Yang Yanchao Zhao |
| author_sort | Baoshan Zhang |
| collection | DOAJ |
| description | As an essential and challenging technology of fault prediction and health management(PHM), fault prediction technology has been a research focus in the field of fault diagnosis. However, the current model-based fault prediction technology and data-driven fault prediction technology have some limitations, and it is difficult to effectively apply them in practice. Therefore, this paper combines the advantages of two kinds of fault prediction technology, sets the fault distribution function as the membership function of the adaptive fuzzy neural network based on the full analysis of the fault mechanism. The use of the fault distribution function to highly generalize the law of fault occurrence, and the strong self-learning ability of the neural network can effectively tap the potential fault information of the fault data, thereby using the fault distribution function to fit the fault data, and forming a set of membership functions by presetting a variety of membership functions, so as to expand the applicability of the proposed model in fault prediction. The experimental results show that the fault prediction model proposed in this paper has the advantages of high prediction accuracy, fast convergence speed and good applicability. |
| first_indexed | 2024-12-18T00:24:08Z |
| format | Article |
| id | doaj.art-13aa4533db2d4804982d72f537e51549 |
| institution | Directory Open Access Journal |
| issn | 2169-3536 |
| language | English |
| last_indexed | 2024-12-18T00:24:08Z |
| publishDate | 2020-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj.art-13aa4533db2d4804982d72f537e515492022-12-21T21:27:17ZengIEEEIEEE Access2169-35362020-01-01810106110106710.1109/ACCESS.2020.29973689099510A Fault Prediction Model of Adaptive Fuzzy Neural Network for Optimal Membership FunctionBaoshan Zhang0https://orcid.org/0000-0002-2172-0846Lin Zhang1Bo Zhang2Bofan Yang3Yanchao Zhao4Air and Missile Defense College, Air Force Engineering University, Xi’an, ChinaAir and Missile Defense College, Air Force Engineering University, Xi’an, ChinaAir and Missile Defense College, Air Force Engineering University, Xi’an, ChinaAir and Missile Defense College, Air Force Engineering University, Xi’an, ChinaAir and Missile Defense College, Air Force Engineering University, Xi’an, ChinaAs an essential and challenging technology of fault prediction and health management(PHM), fault prediction technology has been a research focus in the field of fault diagnosis. However, the current model-based fault prediction technology and data-driven fault prediction technology have some limitations, and it is difficult to effectively apply them in practice. Therefore, this paper combines the advantages of two kinds of fault prediction technology, sets the fault distribution function as the membership function of the adaptive fuzzy neural network based on the full analysis of the fault mechanism. The use of the fault distribution function to highly generalize the law of fault occurrence, and the strong self-learning ability of the neural network can effectively tap the potential fault information of the fault data, thereby using the fault distribution function to fit the fault data, and forming a set of membership functions by presetting a variety of membership functions, so as to expand the applicability of the proposed model in fault prediction. The experimental results show that the fault prediction model proposed in this paper has the advantages of high prediction accuracy, fast convergence speed and good applicability.https://ieeexplore.ieee.org/document/9099510/Adaptive fuzzy neural networkfault mechanismfault predictionmembership function |
| spellingShingle | Baoshan Zhang Lin Zhang Bo Zhang Bofan Yang Yanchao Zhao A Fault Prediction Model of Adaptive Fuzzy Neural Network for Optimal Membership Function IEEE Access Adaptive fuzzy neural network fault mechanism fault prediction membership function |
| title | A Fault Prediction Model of Adaptive Fuzzy Neural Network for Optimal Membership Function |
| title_full | A Fault Prediction Model of Adaptive Fuzzy Neural Network for Optimal Membership Function |
| title_fullStr | A Fault Prediction Model of Adaptive Fuzzy Neural Network for Optimal Membership Function |
| title_full_unstemmed | A Fault Prediction Model of Adaptive Fuzzy Neural Network for Optimal Membership Function |
| title_short | A Fault Prediction Model of Adaptive Fuzzy Neural Network for Optimal Membership Function |
| title_sort | fault prediction model of adaptive fuzzy neural network for optimal membership function |
| topic | Adaptive fuzzy neural network fault mechanism fault prediction membership function |
| url | https://ieeexplore.ieee.org/document/9099510/ |
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