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The combination of feature extraction and pattern recognition can make it possible to realize wind turbine gearboxes based on vibration signals. However, these methods need to be constantly adjusted parameters and spend time training when processing different vibration signals, which is time-consuming. Aiming at reducing the number of parameters that need to be adjusted and training time, this paper proposes a variational mode decomposition (VMD) based on atomic search optimization (ASO) and neural random forest (NRF) fault diagnosis model. The parameters of the VMD are adaptively adjusted by the ASO, which has the advantages of less adjustment parameters. After ASO-VMD decomposition, signals will be used as the input of NRF. We evaluate our method on simulation gearbox model which is established by Solidworks and Adams. Experimental results show that our method has faster training speed and higher recognition accuracy without set many parameters manually.

A novel wind turbine gearbox fault diagnosis method based on ASO-VMD and NRF