An Early Fault Detection Method for Wind Turbine Main Bearings Based on Self-Attention GRU Network and Binary Segmentation Changepoint Detection Algorithm
The condition monitoring and potential anomaly detection of wind turbines have gained significant attention because of the benefits of reducing the operating and maintenance costs and enhancing the reliability of wind turbines. However, the complex and dynamic operation states of wind turbines still...
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| Format: | Article |
| Language: | English |
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MDPI AG
2023-05-01
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| Series: | Energies |
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| Online Access: | https://www.mdpi.com/1996-1073/16/10/4123 |
| _version_ | 1797600219593965568 |
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| author | Junshuai Yan Yongqian Liu Xiaoying Ren |
| author_facet | Junshuai Yan Yongqian Liu Xiaoying Ren |
| author_sort | Junshuai Yan |
| collection | DOAJ |
| description | The condition monitoring and potential anomaly detection of wind turbines have gained significant attention because of the benefits of reducing the operating and maintenance costs and enhancing the reliability of wind turbines. However, the complex and dynamic operation states of wind turbines still pose tremendous challenges for reliable and timely fault detection. To address such challenges, in this study, a condition monitoring approach was designed to detect early faults of wind turbines. Specifically, based on a GRU network with a self-attention mechanism, a SAGRU normal behavior model for wind turbines was constructed, which can learn temporal features and mine complicated nonlinear correlations within different status parameters. Additionally, based on the residual sequence obtained using a well-trained SAGRU, a binary segmentation changepoint detection algorithm (BinSegCPD) was introduced to automatically identify deterioration conditions in a wind turbine. A case study of a main bearing fault collected from a 50 MW windfarm in southern China was employed to evaluate the proposed method, which validated its effectiveness and superiority. The results showed that the introduction of a self-attention mechanism significantly enhanced the model performance, and the adoption of a changepoint detection algorithm improved detection accuracy. Compared to the actual fault time, the proposed approach could automatically identify the deterioration conditions of main bearings 72.47 h in advance. |
| first_indexed | 2024-03-11T03:46:20Z |
| format | Article |
| id | doaj.art-06fcd345a9484f25b26efdc439c61892 |
| institution | Directory Open Access Journal |
| issn | 1996-1073 |
| language | English |
| last_indexed | 2024-03-11T03:46:20Z |
| publishDate | 2023-05-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Energies |
| spelling | doaj.art-06fcd345a9484f25b26efdc439c618922023-11-18T01:13:14ZengMDPI AGEnergies1996-10732023-05-011610412310.3390/en16104123An Early Fault Detection Method for Wind Turbine Main Bearings Based on Self-Attention GRU Network and Binary Segmentation Changepoint Detection AlgorithmJunshuai Yan0Yongqian Liu1Xiaoying Ren2School of New Energy, North China Electric Power University, Beijing 102206, ChinaSchool of New Energy, North China Electric Power University, Beijing 102206, ChinaSchool of New Energy, North China Electric Power University, Beijing 102206, ChinaThe condition monitoring and potential anomaly detection of wind turbines have gained significant attention because of the benefits of reducing the operating and maintenance costs and enhancing the reliability of wind turbines. However, the complex and dynamic operation states of wind turbines still pose tremendous challenges for reliable and timely fault detection. To address such challenges, in this study, a condition monitoring approach was designed to detect early faults of wind turbines. Specifically, based on a GRU network with a self-attention mechanism, a SAGRU normal behavior model for wind turbines was constructed, which can learn temporal features and mine complicated nonlinear correlations within different status parameters. Additionally, based on the residual sequence obtained using a well-trained SAGRU, a binary segmentation changepoint detection algorithm (BinSegCPD) was introduced to automatically identify deterioration conditions in a wind turbine. A case study of a main bearing fault collected from a 50 MW windfarm in southern China was employed to evaluate the proposed method, which validated its effectiveness and superiority. The results showed that the introduction of a self-attention mechanism significantly enhanced the model performance, and the adoption of a changepoint detection algorithm improved detection accuracy. Compared to the actual fault time, the proposed approach could automatically identify the deterioration conditions of main bearings 72.47 h in advance.https://www.mdpi.com/1996-1073/16/10/4123wind turbinefault detectionself-attentiongated recurrent unitchangepoint detection |
| spellingShingle | Junshuai Yan Yongqian Liu Xiaoying Ren An Early Fault Detection Method for Wind Turbine Main Bearings Based on Self-Attention GRU Network and Binary Segmentation Changepoint Detection Algorithm Energies wind turbine fault detection self-attention gated recurrent unit changepoint detection |
| title | An Early Fault Detection Method for Wind Turbine Main Bearings Based on Self-Attention GRU Network and Binary Segmentation Changepoint Detection Algorithm |
| title_full | An Early Fault Detection Method for Wind Turbine Main Bearings Based on Self-Attention GRU Network and Binary Segmentation Changepoint Detection Algorithm |
| title_fullStr | An Early Fault Detection Method for Wind Turbine Main Bearings Based on Self-Attention GRU Network and Binary Segmentation Changepoint Detection Algorithm |
| title_full_unstemmed | An Early Fault Detection Method for Wind Turbine Main Bearings Based on Self-Attention GRU Network and Binary Segmentation Changepoint Detection Algorithm |
| title_short | An Early Fault Detection Method for Wind Turbine Main Bearings Based on Self-Attention GRU Network and Binary Segmentation Changepoint Detection Algorithm |
| title_sort | early fault detection method for wind turbine main bearings based on self attention gru network and binary segmentation changepoint detection algorithm |
| topic | wind turbine fault detection self-attention gated recurrent unit changepoint detection |
| url | https://www.mdpi.com/1996-1073/16/10/4123 |
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