Cost-Sensitive LightGBM-Based Online Fault Detection Method for Wind Turbine Gearboxes
In practice, faulty samples of wind turbine (WT) gearboxes are far smaller than normal samples during operation, and most of the existing fault diagnosis methods for WT gearboxes only focus on the improvement of classification accuracy and ignore the decrease of missed alarms and the reduction of th...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
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Frontiers Media S.A.
2021-08-01
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| Series: | Frontiers in Energy Research |
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| Online Access: | https://www.frontiersin.org/articles/10.3389/fenrg.2021.701574/full |
| _version_ | 1818639924905115648 |
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| author | Mingzhu Tang Qi Zhao Huawei Wu Zimin Wang |
| author_facet | Mingzhu Tang Qi Zhao Huawei Wu Zimin Wang |
| author_sort | Mingzhu Tang |
| collection | DOAJ |
| description | In practice, faulty samples of wind turbine (WT) gearboxes are far smaller than normal samples during operation, and most of the existing fault diagnosis methods for WT gearboxes only focus on the improvement of classification accuracy and ignore the decrease of missed alarms and the reduction of the average cost. To this end, a new framework is proposed through combining the Spearman rank correlation feature extraction and cost-sensitive LightGBM algorithm for WT gearbox’s fault detection. In this article, features from wind turbine supervisory control and data acquisition (SCADA) systems are firstly extracted. Then, the feature selection is employed by using the expert experience and Spearman rank correlation coefficient to analyze the correlation between the big data of WT gearboxes. Moreover, the cost-sensitive LightGBM fault detection framework is established by optimizing the misclassification cost. The false alarm rate and the missed detection rate of the WT gearbox under different working conditions are finally obtained. Experiments have verified that the proposed method can significantly improve the fault detection accuracy. Meanwhile, the proposed method can consistently outperform traditional classifiers such as AdaCost, cost-sensitive GBDT, and cost-sensitive XGBoost in terms of low false alarm rate and missed detection rate. Owing to its high Matthews correlation coefficient scores and low average misclassification cost, the cost-sensitive LightGBM (CS LightGBM) method is preferred for imbalanced WT gearbox fault detection in practice. |
| first_indexed | 2024-12-16T23:03:07Z |
| format | Article |
| id | doaj.art-74e7ce04a4b5404582788259394be4ed |
| institution | Directory Open Access Journal |
| issn | 2296-598X |
| language | English |
| last_indexed | 2024-12-16T23:03:07Z |
| publishDate | 2021-08-01 |
| publisher | Frontiers Media S.A. |
| record_format | Article |
| series | Frontiers in Energy Research |
| spelling | doaj.art-74e7ce04a4b5404582788259394be4ed2022-12-21T22:12:39ZengFrontiers Media S.A.Frontiers in Energy Research2296-598X2021-08-01910.3389/fenrg.2021.701574701574Cost-Sensitive LightGBM-Based Online Fault Detection Method for Wind Turbine GearboxesMingzhu Tang0Qi Zhao1Huawei Wu2Zimin Wang3School of Energy and Power Engineering, Changsha University of Science and Technology, Changsha, ChinaSchool of Energy and Power Engineering, Changsha University of Science and Technology, Changsha, ChinaHubei Key Laboratory of Power System Design and Test for Electrical Vehicle, Hubei University of Arts and Science, Xiangyang, ChinaSchool of Computer Science and Information Security, Guilin University of Electronic Technology, Guilin, ChinaIn practice, faulty samples of wind turbine (WT) gearboxes are far smaller than normal samples during operation, and most of the existing fault diagnosis methods for WT gearboxes only focus on the improvement of classification accuracy and ignore the decrease of missed alarms and the reduction of the average cost. To this end, a new framework is proposed through combining the Spearman rank correlation feature extraction and cost-sensitive LightGBM algorithm for WT gearbox’s fault detection. In this article, features from wind turbine supervisory control and data acquisition (SCADA) systems are firstly extracted. Then, the feature selection is employed by using the expert experience and Spearman rank correlation coefficient to analyze the correlation between the big data of WT gearboxes. Moreover, the cost-sensitive LightGBM fault detection framework is established by optimizing the misclassification cost. The false alarm rate and the missed detection rate of the WT gearbox under different working conditions are finally obtained. Experiments have verified that the proposed method can significantly improve the fault detection accuracy. Meanwhile, the proposed method can consistently outperform traditional classifiers such as AdaCost, cost-sensitive GBDT, and cost-sensitive XGBoost in terms of low false alarm rate and missed detection rate. Owing to its high Matthews correlation coefficient scores and low average misclassification cost, the cost-sensitive LightGBM (CS LightGBM) method is preferred for imbalanced WT gearbox fault detection in practice.https://www.frontiersin.org/articles/10.3389/fenrg.2021.701574/fullfault detectionSpearman rank correlationcost-sensitive classificationlightGBMwind turbine |
| spellingShingle | Mingzhu Tang Qi Zhao Huawei Wu Zimin Wang Cost-Sensitive LightGBM-Based Online Fault Detection Method for Wind Turbine Gearboxes Frontiers in Energy Research fault detection Spearman rank correlation cost-sensitive classification lightGBM wind turbine |
| title | Cost-Sensitive LightGBM-Based Online Fault Detection Method for Wind Turbine Gearboxes |
| title_full | Cost-Sensitive LightGBM-Based Online Fault Detection Method for Wind Turbine Gearboxes |
| title_fullStr | Cost-Sensitive LightGBM-Based Online Fault Detection Method for Wind Turbine Gearboxes |
| title_full_unstemmed | Cost-Sensitive LightGBM-Based Online Fault Detection Method for Wind Turbine Gearboxes |
| title_short | Cost-Sensitive LightGBM-Based Online Fault Detection Method for Wind Turbine Gearboxes |
| title_sort | cost sensitive lightgbm based online fault detection method for wind turbine gearboxes |
| topic | fault detection Spearman rank correlation cost-sensitive classification lightGBM wind turbine |
| url | https://www.frontiersin.org/articles/10.3389/fenrg.2021.701574/full |
| work_keys_str_mv | AT mingzhutang costsensitivelightgbmbasedonlinefaultdetectionmethodforwindturbinegearboxes AT qizhao costsensitivelightgbmbasedonlinefaultdetectionmethodforwindturbinegearboxes AT huaweiwu costsensitivelightgbmbasedonlinefaultdetectionmethodforwindturbinegearboxes AT ziminwang costsensitivelightgbmbasedonlinefaultdetectionmethodforwindturbinegearboxes |