A Novel Fault Prediction Method of Wind Turbine Gearbox Based on Pair-Copula Construction and BP Neural Network
Among the various maintenance technologies of wind turbines, online fault prediction technology is a kind of more cost-effective and reliable method. It may also be the most promising method for wind turbines with potential mechanical faults. SCADA data-based online condition monitoring technology h...
| 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/9091796/ |
| _version_ | 1818433197173637120 |
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| author | Zhihong Luo Changliang Liu Shuai Liu |
| author_facet | Zhihong Luo Changliang Liu Shuai Liu |
| author_sort | Zhihong Luo |
| collection | DOAJ |
| description | Among the various maintenance technologies of wind turbines, online fault prediction technology is a kind of more cost-effective and reliable method. It may also be the most promising method for wind turbines with potential mechanical faults. SCADA data-based online condition monitoring technology has become a hot spot in current researches. Therefore, a novel fault prediction method based on the Pair-Copula model is proposed in this study. First, the conditional mutual information method is introduced to screen out useful variables from a number of variables. Then aiming at the limitation that the conventional Copula model can only deal with two-dimensional variables, the Pair-Copula model is introduced. In addition, the complexity of the prediction model and the dimension of the input variables are greatly reduced by the Pair-Copula model. So, the BP neural network is selected to complete the prediction model. A combined model based on BP neural network and Pair-Copula model is proposed. In order to solve the problem that the conventional Pair-Copula model cannot process real-time data which must be required in fault prediction, a kind of improved Pair-Copula model combined with the kernel density estimation is used to calculate the real-time data. Finally, the proposed method is validated with real data from a 1.5 MW wind turbine, and the effectiveness is confirmed. |
| first_indexed | 2024-12-14T16:17:16Z |
| format | Article |
| id | doaj.art-1ee609d521244970a8a3c8e19b5488d3 |
| institution | Directory Open Access Journal |
| issn | 2169-3536 |
| language | English |
| last_indexed | 2024-12-14T16:17:16Z |
| publishDate | 2020-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj.art-1ee609d521244970a8a3c8e19b5488d32022-12-21T22:54:53ZengIEEEIEEE Access2169-35362020-01-018919249193910.1109/ACCESS.2020.29940779091796A Novel Fault Prediction Method of Wind Turbine Gearbox Based on Pair-Copula Construction and BP Neural NetworkZhihong Luo0https://orcid.org/0000-0002-5501-7833Changliang Liu1https://orcid.org/0000-0003-4653-9282Shuai Liu2https://orcid.org/0000-0001-6311-5479School of Control and Computer Engineering, North China Electric Power University, Beijing, ChinaSchool of Control and Computer Engineering, North China Electric Power University, Beijing, ChinaSchool of Control and Computer Engineering, North China Electric Power University, Beijing, ChinaAmong the various maintenance technologies of wind turbines, online fault prediction technology is a kind of more cost-effective and reliable method. It may also be the most promising method for wind turbines with potential mechanical faults. SCADA data-based online condition monitoring technology has become a hot spot in current researches. Therefore, a novel fault prediction method based on the Pair-Copula model is proposed in this study. First, the conditional mutual information method is introduced to screen out useful variables from a number of variables. Then aiming at the limitation that the conventional Copula model can only deal with two-dimensional variables, the Pair-Copula model is introduced. In addition, the complexity of the prediction model and the dimension of the input variables are greatly reduced by the Pair-Copula model. So, the BP neural network is selected to complete the prediction model. A combined model based on BP neural network and Pair-Copula model is proposed. In order to solve the problem that the conventional Pair-Copula model cannot process real-time data which must be required in fault prediction, a kind of improved Pair-Copula model combined with the kernel density estimation is used to calculate the real-time data. Finally, the proposed method is validated with real data from a 1.5 MW wind turbine, and the effectiveness is confirmed.https://ieeexplore.ieee.org/document/9091796/Wind turbinegearboxfault predictionPair-CopulaSCADA |
| spellingShingle | Zhihong Luo Changliang Liu Shuai Liu A Novel Fault Prediction Method of Wind Turbine Gearbox Based on Pair-Copula Construction and BP Neural Network IEEE Access Wind turbine gearbox fault prediction Pair-Copula SCADA |
| title | A Novel Fault Prediction Method of Wind Turbine Gearbox Based on Pair-Copula Construction and BP Neural Network |
| title_full | A Novel Fault Prediction Method of Wind Turbine Gearbox Based on Pair-Copula Construction and BP Neural Network |
| title_fullStr | A Novel Fault Prediction Method of Wind Turbine Gearbox Based on Pair-Copula Construction and BP Neural Network |
| title_full_unstemmed | A Novel Fault Prediction Method of Wind Turbine Gearbox Based on Pair-Copula Construction and BP Neural Network |
| title_short | A Novel Fault Prediction Method of Wind Turbine Gearbox Based on Pair-Copula Construction and BP Neural Network |
| title_sort | novel fault prediction method of wind turbine gearbox based on pair copula construction and bp neural network |
| topic | Wind turbine gearbox fault prediction Pair-Copula SCADA |
| url | https://ieeexplore.ieee.org/document/9091796/ |
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