APPLICATION OF GWO-SVM IN WIND TURBINE GEARBOX FAULT DIAGNOSIS
Aiming at the nonlinear and instability characteristics of the wind turbine gearbox bearing fault signal,a method based on the Fuzzy Entropy and Grey Wolf Optimizer Support Vector Machine（ GWO-SVM） for the fault diagnosis of gearbox was proposed in this paper. Firstly,EEMD was used to decompose the...
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Editorial Office of Journal of Mechanical Strength
2021-01-01
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| Series: | Jixie qiangdu |
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| Online Access: | http://www.jxqd.net.cn/thesisDetails#10.16579/j.issn.1001.9669.2021.05.002 |
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| author | HU Xuan LI Chun YE KeHua |
| author_facet | HU Xuan LI Chun YE KeHua |
| author_sort | HU Xuan |
| collection | DOAJ |
| description | Aiming at the nonlinear and instability characteristics of the wind turbine gearbox bearing fault signal,a method based on the Fuzzy Entropy and Grey Wolf Optimizer Support Vector Machine（ GWO-SVM） for the fault diagnosis of gearbox was proposed in this paper. Firstly,EEMD was used to decompose the vibration signal into the several intrinsic mode functions（ IMFs）. Secondly,calculated the IMFs’ fuzzy entropies in each state and constructed feature vectors. Finally,the vectors were adopted as the input parameters for the GWO-SVM to diagnose the fault. The results prove that the fuzzy entropy of gearbox vibration signals in different states has a certain degree of discrimination,it can be identified and classified by GWO-SVM accurately. Meanwhile,GWO-SVM is compared with PSO-SVM and GA-SVM,it has shorter time and higher accuracy,the mean accuracy can up to 92. 5%. |
| format | Article |
| id | doaj-art-ad4b75259fd34ad48f12a9aa1d0dd6f9 |
| institution | Kabale University |
| issn | 1001-9669 |
| language | zho |
| publishDate | 2021-01-01 |
| publisher | Editorial Office of Journal of Mechanical Strength |
| record_format | Article |
| series | Jixie qiangdu |
| spelling | doaj-art-ad4b75259fd34ad48f12a9aa1d0dd6f92025-01-15T02:25:18ZzhoEditorial Office of Journal of Mechanical StrengthJixie qiangdu1001-96692021-01-01431026103430611672APPLICATION OF GWO-SVM IN WIND TURBINE GEARBOX FAULT DIAGNOSISHU XuanLI ChunYE KeHuaAiming at the nonlinear and instability characteristics of the wind turbine gearbox bearing fault signal,a method based on the Fuzzy Entropy and Grey Wolf Optimizer Support Vector Machine（ GWO-SVM） for the fault diagnosis of gearbox was proposed in this paper. Firstly,EEMD was used to decompose the vibration signal into the several intrinsic mode functions（ IMFs）. Secondly,calculated the IMFs’ fuzzy entropies in each state and constructed feature vectors. Finally,the vectors were adopted as the input parameters for the GWO-SVM to diagnose the fault. The results prove that the fuzzy entropy of gearbox vibration signals in different states has a certain degree of discrimination,it can be identified and classified by GWO-SVM accurately. Meanwhile,GWO-SVM is compared with PSO-SVM and GA-SVM,it has shorter time and higher accuracy,the mean accuracy can up to 92. 5%.http://www.jxqd.net.cn/thesisDetails#10.16579/j.issn.1001.9669.2021.05.002Wind turbine gearboxFault diagnosisEnsemble empirical mode decompositionGrey wolf optimizerSupport vector machineFuzzy entropy |
| spellingShingle | HU Xuan LI Chun YE KeHua APPLICATION OF GWO-SVM IN WIND TURBINE GEARBOX FAULT DIAGNOSIS Jixie qiangdu Wind turbine gearbox Fault diagnosis Ensemble empirical mode decomposition Grey wolf optimizer Support vector machine Fuzzy entropy |
| title | APPLICATION OF GWO-SVM IN WIND TURBINE GEARBOX FAULT DIAGNOSIS |
| title_full | APPLICATION OF GWO-SVM IN WIND TURBINE GEARBOX FAULT DIAGNOSIS |
| title_fullStr | APPLICATION OF GWO-SVM IN WIND TURBINE GEARBOX FAULT DIAGNOSIS |
| title_full_unstemmed | APPLICATION OF GWO-SVM IN WIND TURBINE GEARBOX FAULT DIAGNOSIS |
| title_short | APPLICATION OF GWO-SVM IN WIND TURBINE GEARBOX FAULT DIAGNOSIS |
| title_sort | application of gwo svm in wind turbine gearbox fault diagnosis |
| topic | Wind turbine gearbox Fault diagnosis Ensemble empirical mode decomposition Grey wolf optimizer Support vector machine Fuzzy entropy |
| url | http://www.jxqd.net.cn/thesisDetails#10.16579/j.issn.1001.9669.2021.05.002 |
| work_keys_str_mv | AT huxuan applicationofgwosvminwindturbinegearboxfaultdiagnosis AT lichun applicationofgwosvminwindturbinegearboxfaultdiagnosis AT yekehua applicationofgwosvminwindturbinegearboxfaultdiagnosis |