Fault Diagnosis Method of Wind Turbine Gearbox Based on Deep Belief Network and Vibration Signal

With the development of wind power, the fault of wind turbine is increasing year by year. The gearbox is the fault multiple component of the wind turbine. The monitoring system gets massive data, and the health monitoring of the fan has also entered the era of "big data". The new problem in the field of health monitoring of mechanical equipment is to excavate information from these large data and to identify the health status of equipment efficiently and accurately. Deep learning is an effective tool for large data processing. Based on the strong perception and self-learning ability of deep learning theory, a fault diagnosis method of wind turbine gearbox based on deep belief network and vibration signal is proposed in this paper. The original fault sample vibration signal is input to the deep belief network to get the deep learning sample model. At the same time, Batch Normalization is added to reduce the overfitting probability and improve the convergence speed of the network. Then the trained deep neural network model is applied to the fault diagnosis of the planetary gear box of the wind turbine gearbox, and the results show that the method is more accurate than the DBN, the BPNN algorithm and the traditional fault diagnosis method.