Fault diagnosis method for rolling bearing on shearer arm based on deep transfer learning

Aiming at the problems of less typical fault data, insufficient labeled data and poor training effect of fault diagnosis model of mining equipment in long-term normal service, an intelligent fault diagnosis method of shearer rocker arm transmission system based on deep transfer learning is proposed. Using this method, the fault diagnosis model parameters obtained after the training of the simulation platform fault data are migrated to the intelligent fault diagnosis model of the shearer, so as to transfer learning between different equipment, and realize the intelligent fault diagnosis of the shearer rocker arm based on small sample data. By constructing the pre-trained convolutional neural network, the image data set converted into two-dimensional time-frequency distribution is used as the input of the pre-trained model, and the network parameters of the pre-trained model are migrated to the fault diagnosis model of the shearer rocker arm transmission system. By ensuring the generalization ability of the low-level network unchanged, the data set containing labels is used as the training data set of the intelligent fault diagnosis model of the shearer rocker arm transmission system to train the model. By fine-tuning the high-level network parameters to optimize the model and update the weights, the migration fault diagnosis model of the shearer rocker arm transmission system is obtained, which improves the feature extraction ability of the model and reduces the error. In order to verify the effectiveness of the method, the rolling bearing of the transmission system is taken as the research object, and the bearing data of the University of Western Reserve are used as the training set. The DDS transmission system platform is used to simulate the working condition of the transmission system of the rocker arm of the underground shearer to obtain the monitoring data of the rolling bearing, which is used as the test set for experimental verification. The experimental results show that the average fault recognition accuracy of rolling bearing is 99.59%. Compared with the traditional intelligent fault diagnosis method, the intelligent fault diagnosis method proposed in this paper has fast convergence speed and high diagnostic accuracy. It can realize high precision equipment state recognition and classification based on laboratory fault diagnosis knowledge. © 2022 Meitan Kexue Jishu/Coal Science and Technology (Peking). All rights reserved.