| Citation: | LEI C L，SHI J S，MA S Z，et al. Rolling bearing fault diagnosis method based on MSDCNN in strong noise environment[J]. Journal of Beijing University of Aeronautics and Astronautics，2025，51（9）：2906-2915 （in Chinese） doi: 10.13700/j.bh.1001-5965.2023.0456
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To address the poor anti-noise performance, high computational complexity, and insufficient generalization performance of traditional bearing fault diagnosis methods based on deep learning, this paper proposed a rolling bearing fault diagnosis method based on multi-scale dynamic convolutional neural network (MSDCNN). Firstly, the one-dimensional vibration signal of the rolling bearing was transformed into frequency domain by Fourier transform, and the features werefurther extracted by wide convolution kernel. Secondly, a multi-scale dynamic convolution structure was presented, and an improved channel attention mechanism wasutilized to assign different weights to the feature information extracted by convolution kernels of different sizes. Then, a self-calibrating spatial attention mechanism (SCSAM) was designed to capture the importance of different regions by inputting the extracted feature information into the spatial attention mechanism. Finally, the features were further extracted by the small convolution kernel, and the Softmax classifier was used to classify faults. Two different data sets were used to verify the fault diagnosis performance of the proposed model. The experimental results show that the proposed model has higher classification accuracy, better generalization ability, and stronger robustness under strong noise background than other intelligent modelssuch as multi-scale deep convolutional neural network (MSD-CNN) and wide convolutional kernel convolutional neural network (WKCNN).
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