| Citation: | GUO J F，TAN B H，WANG Z M. Fault diagnosis method of rolling bearing based on MDAM-GhostCNN[J]. Journal of Beijing University of Aeronautics and Astronautics，2025，51（4）：1172-1184 （in Chinese） doi: 10.13700/j.bh.1001-5965.2023.0224
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A rolling bearing fault diagnostic approach based on mixture domain attention mechanism (MDAM)-GhostCNN is developed to address the issues of inadequate feature extraction, complicated computation, and low recognition accuracy under varied working conditions in conventional fault detection methods. First of all, the Markov transfer field (MTF) is used to transform the bearing vibration signal into a two-dimensional feature graph with time correlation. Secondly, taking advantage of the simplification of Ghost convolution calculation, GhostCNN is constructed. Then, a MDAM is designed, which makes the network fully capture the feature information from the two dimensions of channel and space, and makes the network pay attention to the feature space information effectively while realizing the interdependence between the feature channels, and construct the MDAM-GhostCNN model. Finally, the MTF two-dimensional feature map is input into the MDAM-GhostCNN model for training and output diagnosis results. Experimental verification and noise processing were performed on the bearing data sets from Jiang Nan University (JNU) and Case Western Reserve University. The results show that under variable working conditions, the constructed model has higher recognition accuracy, noise immunity and generalization performance.
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