Monitoring and Identifying Wind Turbine Generator Bearing Faults Using Deep Belief Network and EWMA Control Charts
Wind turbines are widely installed as the new source of cleaner energy production. Dynamic and random stress imposed on the generator bearing of a wind turbine may lead to overheating and failure. In this paper, a data-driven approach for condition monitoring of generator bearings using temporal tem...
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| Published in | Frontiers in Energy Research Vol. 9 |
|---|---|
| Main Authors | , , , , |
| Format | Journal Article |
| Language | English Japanese |
| Published |
Frontiers Media SA
19.11.2021
Frontiers Media S.A |
| Subjects | |
| Online Access | Get full text |
| ISSN | 2296-598X 2296-598X |
| DOI | 10.3389/fenrg.2021.799039 |
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| Abstract | Wind turbines are widely installed as the new source of cleaner energy production. Dynamic and random stress imposed on the generator bearing of a wind turbine may lead to overheating and failure. In this paper, a data-driven approach for condition monitoring of generator bearings using temporal temperature data is presented. Four algorithms, the support vector regression machine, neural network, extreme learning machine, and the deep belief network are applied to model the bearing behavior. Comparative analysis of the models has demonstrated that the deep belief network is most accurate. It has been observed that the bearing failure is preceded by a change in the prediction error of bearing temperature. An exponentially-weighted moving average (EWMA) control chart is deployed to trend the error. Then a binary vector containing the abnormal errors and the normal residuals are generated for classifying failures. LS-SVM based classification models are developed to classify the fault bearings and the normal ones. The proposed approach has been validated with the data collected from 11 wind turbines. |
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| AbstractList | Wind turbines are widely installed as the new source of cleaner energy production. Dynamic and random stress imposed on the generator bearing of a wind turbine may lead to overheating and failure. In this paper, a data-driven approach for condition monitoring of generator bearings using temporal temperature data is presented. Four algorithms, the support vector regression machine, neural network, extreme learning machine, and the deep belief network are applied to model the bearing behavior. Comparative analysis of the models has demonstrated that the deep belief network is most accurate. It has been observed that the bearing failure is preceded by a change in the prediction error of bearing temperature. An exponentially-weighted moving average (EWMA) control chart is deployed to trend the error. Then a binary vector containing the abnormal errors and the normal residuals are generated for classifying failures. LS-SVM based classification models are developed to classify the fault bearings and the normal ones. The proposed approach has been validated with the data collected from 11 wind turbines. |
| Author | Huajin Li Jiahao Deng Peng Feng Shuang Yuan Dimuthu D. K. Arachchige |
| Author_xml | – sequence: 1 givenname: Huajin surname: Li fullname: Li, Huajin – sequence: 2 givenname: Jiahao surname: Deng fullname: Deng, Jiahao – sequence: 3 givenname: Shuang surname: Yuan fullname: Yuan, Shuang – sequence: 4 givenname: Peng surname: Feng fullname: Feng, Peng – sequence: 5 givenname: Dimuthu D. K. surname: Arachchige fullname: Arachchige, Dimuthu D. K. |
| BackLink | https://cir.nii.ac.jp/crid/1870583643136947968$$DView record in CiNii |
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| Title | Monitoring and Identifying Wind Turbine Generator Bearing Faults Using Deep Belief Network and EWMA Control Charts |
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