Fault diagnosis method of small sample rolling bearings under variable working conditions based on MTF-SPCNN

In order to solve the problem of low fault diagnosis accuracy caused by complex operating conditions and insufficient samples of rolling bearings, a fault diagnosis method based on the Markov transition field (MTF) and the stripe pooling convolutional neural network (SPCNN) for small sample rolling bearings under variable working conditions was proposed. Firstly, one-dimensional bearing signals were transformed into two-dimensional images with time correlation by using MTF. Then, the stripe pooling module (SPM) was presented and introduced into the network, which could not only enhance the ability of the model to capture information in the long-distance direction but also effectively extract remote spatial features. Secondly, the channel attention mechanism, namely SE was added before the max pooling layer to increase the weight of useful information and improve the training speed of the model. The MTF-SPCNN model was thus constructed. Finally, the MTF images were input into the MTF-SPCNN for training, and fault classification results were obtained. The data sets of Case Western Reserve University and laboratory rolling bearings MFS were used to verify the validity of the proposed method in small samples with variable load and variable speed, and the MFS data sets were processed with noise added and compared with other intelligent algorithms. Experimental results show that the proposed method has higher fault recognition accuracy and stronger generalization performance and anti-interference performance. © 2024 Beijing University of Aeronautics and Astronautics (BUAA). All rights reserved.