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Fast Fault Identification Method of Wind Turbine Rolling Bearing based on VMD and Time Domain and Frequency Domain Feature Selection

One-third of the wind turbines faults occurred in the rolling bearings. The rapid identification of rolling bearings faults is of great significance to the safe production of wind power generation. To take full usage of the rolling bearing vibration signals, a rapid identification method based on Variational Mode Decomposition (VMD) and the time-domain and frequency-domain feature selection is developed. Firstly, the original vibration signals are processed by adaptive variational mode decomposition to obtain their components. Then, the time-domain and frequency-domain features of the original signal and components are computed through statistical methods and Fast Fourier Transform (FFT). Finally, the selected the time-frequency domain features via the random forest algorithm are given into the extreme learning machine network to construct a rapid fault identification model. Experiments demonstrate that the proposed method can identify bearing faults with a fault recognition rate of 99.7% at 0.268 seconds.