The bearings of the wind turbine are subjected to alternating stresses and shock loads during operation,leading to nonlinear,non-stationary,and noisy vibration signals,thus rendering conventional feature extraction insufficient.Aiming at the inherent defects in the wind turbine bearings failure message processing and feature extraction for wind turbine bearing fault diagnosis,a novel method has been pro-posed on the basis of the optimal Variational Modal Decomposition combined with a random forest algo-rithm.Firstly,The technique utilizes the sooty tern optimization algorithm to conduct a search optimiza-tion of the values in the variables for the variational modal decomposition.Subsequently,the method with optimized parameters is employed to decompose the vibration signal of rolling bearing signals to obtain mo-dal components.Finally,the peak value,kurtosis,and envelope entropy are applied to construct the fu-sion feature training set and input them into the random forest classifier to realize fault recognition.The results of the case analysis demonstrate the efficacy of the proposed methodology to identify faults in achie-ving a fault recognition accuracy of up to 100％for wind turbine bearing faults,facilitating accurate fault discrimination.
维普
万方数据