A fault diagnosis method for mine rolling bearings based on deep learning

A fault diagnosis method for mine rolling bearings based on Markov transition field(MTF) and dual-channel multi-scale convolutional capsule network (DMCCN) is proposed to address the problem of traditional convolutional neural networks being unable to fully explore data features in complex environments such as coal mines. The MTF-DMCCN fault diagnosis model is constructed. After encoding the original vibration signal based on MTF and grayscale image, a dual channel input mode is used to connect the convolutional network to obtain shallow features. The method inputs the feature maps fusion into the capsule network to improve the sensitivity of the model to spatial information. The method introduces Inception modules into the network to focus on multi-scale features and enhance the network's feature extraction capabilities. Finally, vectorization processing is carried out through the capsule layer to achieve fault diagnosis and classification of rolling bearings. The results of ablation, noise resistance, and generalization experiments show that the Inception module, grayscale image input, and MTF image input all have a positive promoting effect on bearing fault diagnosis. MTF coding has the highest improvement in diagnostic precision of the model. The MTF-DMCCN model has good robustness and noise resistance. The MTF-DMCCN model has excellent adaptability to variable speed and still exhibits good generalization performance under different operating conditions. To further validate the performance of the model, image encoding methods such as Gram angle difference field (GADF), Gram angle sum field (GASF), grayscale image, and MTF are selected and combined with different networks. Comparative experiments are conducted using the University of Cincinnati intelligent maintenance system (IMS). The results show that the MTF-DMCCN model can effectively recognize the type of rolling bearing faults, with an average fault diagnosis accuracy of 99.37%.