Condition Monitoring for the Roller Bearings of Wind Turbines under Variable Working Conditions Based on the Fisher Score and Permutation Entropy
Condition monitoring is used to assess the reliability and equipment efficiency of wind turbines. Feature extraction is an essential preprocessing step to achieve a high level of performance in condition monitoring. However, the fluctuating conditions of wind turbines usually cause sudden variations...
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| Format: | Article |
| Language: | English |
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MDPI AG
2019-08-01
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| Series: | Energies |
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| Online Access: | https://www.mdpi.com/1996-1073/12/16/3085 |
| _version_ | 1828350157584859136 |
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| author | Lei Fu Tiantian Zhu Kai Zhu Yiling Yang |
| author_facet | Lei Fu Tiantian Zhu Kai Zhu Yiling Yang |
| author_sort | Lei Fu |
| collection | DOAJ |
| description | Condition monitoring is used to assess the reliability and equipment efficiency of wind turbines. Feature extraction is an essential preprocessing step to achieve a high level of performance in condition monitoring. However, the fluctuating conditions of wind turbines usually cause sudden variations in the monitored features, which may lead to an inaccurate prediction and maintenance schedule. In this scenario, this article proposed a novel methodology to detect the multiple levels of faults of rolling bearings in variable operating conditions. First, signal decomposition was carried out by variational mode decomposition (VMD). Second, the statistical features were calculated and extracted in the time domain. Meanwhile, a permutation entropy analysis was conducted to estimate the complexity of the vibrational signal in the time series. Next, feature selection techniques were applied to achieve improved identification accuracy and reduce the computational burden. Finally, the ranked feature vectors were fed into machine learning algorithms for the classification of the bearing defect status. In particular, the proposed method was performed over a wide range of working regions to simulate the operational conditions of wind turbines. Comprehensive experimental investigations were employed to evaluate the performance and effectiveness of the proposed method. |
| first_indexed | 2024-04-14T01:21:00Z |
| format | Article |
| id | doaj.art-c329b0576bd04f6387598ff1af34d92f |
| institution | Directory Open Access Journal |
| issn | 1996-1073 |
| language | English |
| last_indexed | 2024-04-14T01:21:00Z |
| publishDate | 2019-08-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Energies |
| spelling | doaj.art-c329b0576bd04f6387598ff1af34d92f2022-12-22T02:20:39ZengMDPI AGEnergies1996-10732019-08-011216308510.3390/en12163085en12163085Condition Monitoring for the Roller Bearings of Wind Turbines under Variable Working Conditions Based on the Fisher Score and Permutation EntropyLei Fu0Tiantian Zhu1Kai Zhu2Yiling Yang3College of Mechanical Engineering, Zhejiang University of Technology, Hangzhou 310023, ChinaCollege of Computer Science & Technology, Zhejiang University of Technology, Hangzhou 310023, ChinaCollege of Mechanical Engineering, Zhejiang University of Technology, Hangzhou 310023, ChinaFaculty of Mechanical Engineering and Mechanics, Ningbo University, Ningbo 315211, ChinaCondition monitoring is used to assess the reliability and equipment efficiency of wind turbines. Feature extraction is an essential preprocessing step to achieve a high level of performance in condition monitoring. However, the fluctuating conditions of wind turbines usually cause sudden variations in the monitored features, which may lead to an inaccurate prediction and maintenance schedule. In this scenario, this article proposed a novel methodology to detect the multiple levels of faults of rolling bearings in variable operating conditions. First, signal decomposition was carried out by variational mode decomposition (VMD). Second, the statistical features were calculated and extracted in the time domain. Meanwhile, a permutation entropy analysis was conducted to estimate the complexity of the vibrational signal in the time series. Next, feature selection techniques were applied to achieve improved identification accuracy and reduce the computational burden. Finally, the ranked feature vectors were fed into machine learning algorithms for the classification of the bearing defect status. In particular, the proposed method was performed over a wide range of working regions to simulate the operational conditions of wind turbines. Comprehensive experimental investigations were employed to evaluate the performance and effectiveness of the proposed method.https://www.mdpi.com/1996-1073/12/16/3085condition monitoringwind turbinevariational mode decompositionfisher scorepermutation entropyvariable operational condition |
| spellingShingle | Lei Fu Tiantian Zhu Kai Zhu Yiling Yang Condition Monitoring for the Roller Bearings of Wind Turbines under Variable Working Conditions Based on the Fisher Score and Permutation Entropy Energies condition monitoring wind turbine variational mode decomposition fisher score permutation entropy variable operational condition |
| title | Condition Monitoring for the Roller Bearings of Wind Turbines under Variable Working Conditions Based on the Fisher Score and Permutation Entropy |
| title_full | Condition Monitoring for the Roller Bearings of Wind Turbines under Variable Working Conditions Based on the Fisher Score and Permutation Entropy |
| title_fullStr | Condition Monitoring for the Roller Bearings of Wind Turbines under Variable Working Conditions Based on the Fisher Score and Permutation Entropy |
| title_full_unstemmed | Condition Monitoring for the Roller Bearings of Wind Turbines under Variable Working Conditions Based on the Fisher Score and Permutation Entropy |
| title_short | Condition Monitoring for the Roller Bearings of Wind Turbines under Variable Working Conditions Based on the Fisher Score and Permutation Entropy |
| title_sort | condition monitoring for the roller bearings of wind turbines under variable working conditions based on the fisher score and permutation entropy |
| topic | condition monitoring wind turbine variational mode decomposition fisher score permutation entropy variable operational condition |
| url | https://www.mdpi.com/1996-1073/12/16/3085 |
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