Bearing fault diagnosis network based on adaptive dimension-increasing and convolutional self-attention

Deep learning networks are an end-to-end black box model, and network interpretable analysis can provide a deeper understanding of network internal operating mechanism to reasonably optimize network structure and adjust parameters. At present, the bearing fault diagnosis model based on Transformer network must use methods of time-frequency analysis, etc. to do dimension increasing for time-domain signal and convert it into a 2-D image of time-frequency map, etc. for interpretable analysis. This method has drawbacks of fixed parameters, large number of network parameters, and poor network interpretability in dimension increasing process. Here, aiming at above problems, a convolutional self- attention adaptive dimension-increasing network (CSADI-Net) integrating adaptive dimension increasing method in network, convolutional self-attention module and mid layer class activation map (ML-CAM) was proposed. The convolutional self-attention module could use convolutional layers to obtain query (Q), key (K) and value (V) of feature map to greatly reduce the number of trainable parameters. The adaptive dimension increasing method in network could integrate dimension increasing process with network training through internal feature maps splicing, etc. to make it have the ability to adaptively adjust parameters. ML-CAM could display attention levels of various parts on 2-D feature map obtained with the network dimension increasing method in the form of a heatmap, it could be an intuitive, visual and interpretable analysis method. In addition, CSADI-Net and ML-CAM were tested. It was shown that the accuracy of CSADI-Net on the bearing dataset of Case Western Reserve University can reach 97. 32 ± 0. 12%, it can fully classify the actually measured bearing fault dataset of Dalian University; at the same time, ML-CAM is used to draw class activation heatmaps for CSADI-Net on various samples of the two datasets to interpret the network operation mechanism, and confirm CSADI-Net having advantages of high accuracy, high anti-noise ability and strong interpretability. © 2024 Chinese Vibration Engineering Society. All rights reserved.