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Research on a Lightweight Multi-Scale Feature Fusion and its Fault Diagnosis Method for Rolling Bearing with Limited Labeled Samples

Zhang Jiqiang, Kong Xiangwei, Han Taorui, Cheng Liu, Li Xueyi, Liu Zhitong

Eksploatacja i Niezawodność

2025

Vol. 27, no. 1

art. no. 192235

Convolutional neural networks(CNNs) show significant potential for bearing fault diagnosis. However, traditional CNNs face challenges such as poor noise resistance, high computational complexity, reliance on extensive samples, and limited generalizability. As a result, this paper proposes WDSC-Net, a lightweight, multiscale feature fusion method, focusing on limited labeled fault samples. Initially, a wide kernel convolutional is employed, aiming to reduce parameters and computational complexity. Next, features are fed into a 1×1 convolutional layer reduces feature dimensionality. Subsequently, leveraging the benefits of depth-separable convolution (DSC) allows the separation of spatial and channel features, constructing four convolutional layers of varying scales to amplify the nonlinear fault representation. Finally, an improved feature soft-threshold denoising module is introduced for global feature denoising. Validation on CWRU and MCDS datasets shows that the WDSC-Net method exhibits superior generalizability and noise resistance compared to typical deep-learning fault methods.