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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%.
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%.
APPLICATION OF GWO-SVM IN WIND TURBINE GEARBOX FAULT DIAGNOSIS
2021
Article (Journal)
Electronic Resource
Unknown
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