GEARBOX FAULT DIAGNOSIS MODEL TRAINING METHOD AND GEARBOX FAULT DIAGNOSIS METHOD

Disclosed in the present invention are a gearbox fault diagnosis model training method and a gearbox fault diagnosis method. The training method comprises: obtaining a motor current signal in an electromechanical system where a gearbox is located; calculating feature values representing the complexity and the mutation degree of the current signal according to the current signal; screening the feature values according to a random forest algorithm to generate a sample data set; and training a deep reinforcement learning network model according to the data set to generate a fault diagnosis model. According to the gearbox fault diagnosis model training method provided by the present invention, only the current signal is obtained, no additional sensor is needed, and the defect in the prior art that hardware is added is overcome. Feature data related to the fault is extracted by calculating and screening the feature values representing the complexity and the mutation degree of the current signal. According to the m