A CBA-KELM-Based Recognition Method for Fault Diagnosis of Wind Turbines with Time-Domain Analysis and Multisensor Data Fusion
Fault diagnosis technology (FDT) is an effective tool to ensure stability and reliable operation in wind turbines. In this paper, a novel fault diagnosis methodology based on a cloud bat algorithm (CBA)-kernel extreme learning machines (KELM) approach for wind turbines is proposed via combination of...
Saved in:
| Main Authors: | , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Wiley
2019-01-01
|
| Series: | Shock and Vibration |
| Online Access: | http://dx.doi.org/10.1155/2019/7490750 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1832545738274373632 |
|---|---|
| author | Xiafei Long Ping Yang Hongxia Guo Zhuoli Zhao Xiwen Wu |
| author_facet | Xiafei Long Ping Yang Hongxia Guo Zhuoli Zhao Xiwen Wu |
| author_sort | Xiafei Long |
| collection | DOAJ |
| description | Fault diagnosis technology (FDT) is an effective tool to ensure stability and reliable operation in wind turbines. In this paper, a novel fault diagnosis methodology based on a cloud bat algorithm (CBA)-kernel extreme learning machines (KELM) approach for wind turbines is proposed via combination of the multisensor data fusion technique and time-domain analysis. First, the derived method calculates the time-domain indices of raw signals, and the fused time-domain indexes dataset are obtained by the multisensor data fusion. Then, the CBA-based KELM recognition model that can identify fault patterns of a wind turbine gearbox (WTB) is automatically established with the fused dataset. The dataset includes a large number of samples involving 6 fault types under different operational conditions by 5 accelerometers. The effectiveness and feasibility of this proposed method are proved by adopting the datasets originated from the test rig, and it achieves a diagnostic accuracy of 96.25%. Finally, compared with the other peer-to-peer methods, the experimental classification results show that the proposed CBA-KELM technique has the best performances. |
| format | Article |
| id | doaj-art-c4dc687f3b99428eb999b2ec05558a42 |
| institution | Kabale University |
| issn | 1070-9622 1875-9203 |
| language | English |
| publishDate | 2019-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | Shock and Vibration |
| spelling | doaj-art-c4dc687f3b99428eb999b2ec05558a422025-02-03T07:24:55ZengWileyShock and Vibration1070-96221875-92032019-01-01201910.1155/2019/74907507490750A CBA-KELM-Based Recognition Method for Fault Diagnosis of Wind Turbines with Time-Domain Analysis and Multisensor Data FusionXiafei Long0Ping Yang1Hongxia Guo2Zhuoli Zhao3Xiwen Wu4School of Electric Power, South China University of Technology, Guangzhou 510640, ChinaGuangdong Key Laboratory of Clean Energy Technology, South China University of Technology, Guangzhou 510640, ChinaSchool of Electric Power, South China University of Technology, Guangzhou 510640, ChinaSchool of Automation, Guangdong University of Technology, Guangzhou 510006, ChinaHunan New Energy Development Co.,Ltd., Guodian Power, Changsha 410016, ChinaFault diagnosis technology (FDT) is an effective tool to ensure stability and reliable operation in wind turbines. In this paper, a novel fault diagnosis methodology based on a cloud bat algorithm (CBA)-kernel extreme learning machines (KELM) approach for wind turbines is proposed via combination of the multisensor data fusion technique and time-domain analysis. First, the derived method calculates the time-domain indices of raw signals, and the fused time-domain indexes dataset are obtained by the multisensor data fusion. Then, the CBA-based KELM recognition model that can identify fault patterns of a wind turbine gearbox (WTB) is automatically established with the fused dataset. The dataset includes a large number of samples involving 6 fault types under different operational conditions by 5 accelerometers. The effectiveness and feasibility of this proposed method are proved by adopting the datasets originated from the test rig, and it achieves a diagnostic accuracy of 96.25%. Finally, compared with the other peer-to-peer methods, the experimental classification results show that the proposed CBA-KELM technique has the best performances.http://dx.doi.org/10.1155/2019/7490750 |
| spellingShingle | Xiafei Long Ping Yang Hongxia Guo Zhuoli Zhao Xiwen Wu A CBA-KELM-Based Recognition Method for Fault Diagnosis of Wind Turbines with Time-Domain Analysis and Multisensor Data Fusion Shock and Vibration |
| title | A CBA-KELM-Based Recognition Method for Fault Diagnosis of Wind Turbines with Time-Domain Analysis and Multisensor Data Fusion |
| title_full | A CBA-KELM-Based Recognition Method for Fault Diagnosis of Wind Turbines with Time-Domain Analysis and Multisensor Data Fusion |
| title_fullStr | A CBA-KELM-Based Recognition Method for Fault Diagnosis of Wind Turbines with Time-Domain Analysis and Multisensor Data Fusion |
| title_full_unstemmed | A CBA-KELM-Based Recognition Method for Fault Diagnosis of Wind Turbines with Time-Domain Analysis and Multisensor Data Fusion |
| title_short | A CBA-KELM-Based Recognition Method for Fault Diagnosis of Wind Turbines with Time-Domain Analysis and Multisensor Data Fusion |
| title_sort | cba kelm based recognition method for fault diagnosis of wind turbines with time domain analysis and multisensor data fusion |
| url | http://dx.doi.org/10.1155/2019/7490750 |
| work_keys_str_mv | AT xiafeilong acbakelmbasedrecognitionmethodforfaultdiagnosisofwindturbineswithtimedomainanalysisandmultisensordatafusion AT pingyang acbakelmbasedrecognitionmethodforfaultdiagnosisofwindturbineswithtimedomainanalysisandmultisensordatafusion AT hongxiaguo acbakelmbasedrecognitionmethodforfaultdiagnosisofwindturbineswithtimedomainanalysisandmultisensordatafusion AT zhuolizhao acbakelmbasedrecognitionmethodforfaultdiagnosisofwindturbineswithtimedomainanalysisandmultisensordatafusion AT xiwenwu acbakelmbasedrecognitionmethodforfaultdiagnosisofwindturbineswithtimedomainanalysisandmultisensordatafusion AT xiafeilong cbakelmbasedrecognitionmethodforfaultdiagnosisofwindturbineswithtimedomainanalysisandmultisensordatafusion AT pingyang cbakelmbasedrecognitionmethodforfaultdiagnosisofwindturbineswithtimedomainanalysisandmultisensordatafusion AT hongxiaguo cbakelmbasedrecognitionmethodforfaultdiagnosisofwindturbineswithtimedomainanalysisandmultisensordatafusion AT zhuolizhao cbakelmbasedrecognitionmethodforfaultdiagnosisofwindturbineswithtimedomainanalysisandmultisensordatafusion AT xiwenwu cbakelmbasedrecognitionmethodforfaultdiagnosisofwindturbineswithtimedomainanalysisandmultisensordatafusion |