Research on Extraction of Compound Fault Characteristics for Rolling Bearings in Wind Turbines
Wind turbines work in strong background noise, and multiple faults often occur where features are mixed together and are easily misjudged. To extract composite fault of rolling bearings from wind turbines, a new hybrid approach was proposed based on multi-point optimal minimum entropy deconvolution...
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| Format: | Article |
| Language: | English |
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MDPI AG
2020-06-01
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| Series: | Entropy |
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| Online Access: | https://www.mdpi.com/1099-4300/22/6/682 |
| _version_ | 1827714729762619392 |
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| author | Ling Xiang Hao Su Ying Li |
| author_facet | Ling Xiang Hao Su Ying Li |
| author_sort | Ling Xiang |
| collection | DOAJ |
| description | Wind turbines work in strong background noise, and multiple faults often occur where features are mixed together and are easily misjudged. To extract composite fault of rolling bearings from wind turbines, a new hybrid approach was proposed based on multi-point optimal minimum entropy deconvolution adjusted (MOMEDA) and the 1.5-dimensional Teager kurtosis spectrum. The composite fault signal was deconvoluted using the MOMEDA method. The deconvoluted signal was analyzed by applying the 1.5-dimensional Teager kurtosis spectrum. Finally, the frequency characteristics were extracted for the bearing fault. A bearing composite fault signal with strong background noise was utilized to prove the validity of the method. Two actual cases on bearing fault detection were analyzed with wind turbines. The results show that the method is suitable for the diagnosis of wind turbine compound faults and can be applied to research on the health behavior of wind turbines. |
| first_indexed | 2024-03-10T19:03:40Z |
| format | Article |
| id | doaj.art-dfba254255ad4b4e8814e374abdf481a |
| institution | Directory Open Access Journal |
| issn | 1099-4300 |
| language | English |
| last_indexed | 2024-03-10T19:03:40Z |
| publishDate | 2020-06-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Entropy |
| spelling | doaj.art-dfba254255ad4b4e8814e374abdf481a2023-11-20T04:17:41ZengMDPI AGEntropy1099-43002020-06-0122668210.3390/e22060682Research on Extraction of Compound Fault Characteristics for Rolling Bearings in Wind TurbinesLing Xiang0Hao Su1Ying Li2School of Mechanical Engineering, North China Electric Power University, Baoding 071003, ChinaSchool of Mechanical Engineering, North China Electric Power University, Baoding 071003, ChinaSchool of Mechanical Engineering, North China Electric Power University, Baoding 071003, ChinaWind turbines work in strong background noise, and multiple faults often occur where features are mixed together and are easily misjudged. To extract composite fault of rolling bearings from wind turbines, a new hybrid approach was proposed based on multi-point optimal minimum entropy deconvolution adjusted (MOMEDA) and the 1.5-dimensional Teager kurtosis spectrum. The composite fault signal was deconvoluted using the MOMEDA method. The deconvoluted signal was analyzed by applying the 1.5-dimensional Teager kurtosis spectrum. Finally, the frequency characteristics were extracted for the bearing fault. A bearing composite fault signal with strong background noise was utilized to prove the validity of the method. Two actual cases on bearing fault detection were analyzed with wind turbines. The results show that the method is suitable for the diagnosis of wind turbine compound faults and can be applied to research on the health behavior of wind turbines.https://www.mdpi.com/1099-4300/22/6/682rolling bearingfault detectionmulti-point optimal minimum entropy deconvolution adjusted (MOMEDA)1.5-dimensional Teager kurtosis spectrumwind turbine |
| spellingShingle | Ling Xiang Hao Su Ying Li Research on Extraction of Compound Fault Characteristics for Rolling Bearings in Wind Turbines Entropy rolling bearing fault detection multi-point optimal minimum entropy deconvolution adjusted (MOMEDA) 1.5-dimensional Teager kurtosis spectrum wind turbine |
| title | Research on Extraction of Compound Fault Characteristics for Rolling Bearings in Wind Turbines |
| title_full | Research on Extraction of Compound Fault Characteristics for Rolling Bearings in Wind Turbines |
| title_fullStr | Research on Extraction of Compound Fault Characteristics for Rolling Bearings in Wind Turbines |
| title_full_unstemmed | Research on Extraction of Compound Fault Characteristics for Rolling Bearings in Wind Turbines |
| title_short | Research on Extraction of Compound Fault Characteristics for Rolling Bearings in Wind Turbines |
| title_sort | research on extraction of compound fault characteristics for rolling bearings in wind turbines |
| topic | rolling bearing fault detection multi-point optimal minimum entropy deconvolution adjusted (MOMEDA) 1.5-dimensional Teager kurtosis spectrum wind turbine |
| url | https://www.mdpi.com/1099-4300/22/6/682 |
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