Bearings fault diagnosis method based on MAM and deep separable dilated convolutional neural network

Abstract

Abstract Aiming at the problems of traditional fault diagnosis methods that do not represent the time correlation between signals, low recognition accuracy under complex working conditions and noise interference and too many parameters, a bearing fault diagnosis method based on mixed attention mechanism (MAM) and deep separable dilated convolution neural network (DSDCNN) is proposed. Firstly, a Markov transfer field encoding method is used to transform the original one-dimensional vibration signal into a two-dimensional feature image with temporal correlation. Secondly, a deep separable convolution algorithm is presented by taking advantage of the low computational complexity of deep separable convolution and the ability of dilated convolution to expand the receptive field under the condition of invariable number of parameters. Then, the MAM is designed to make the model capture the feature dependency of the feature map in spatial and channel dimensions, and the MAM-DSDCNN model is constructed. Finally, the fault diagnosis performance of the proposed model is verified with two different data sets. The results show that the average recognition accuracy of MAM-DSDCNN reaches 99.63% under variable load conditions, 99.42% under variable speed conditions, 94.26% under noisy environment with the signal-to-noise of 0 dB, which prove that the model has higher recognition accuracy, stronger generalization and noise immunity performance than other deep learning algorithms.