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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.

Intelligent fault diagnosis of rolling bearings based on continuous wavelet transform-multiscale feature fusion and improved channel attention mechanism

Zhang Jiqiang, Kong Xiangwei, Cheng Liu, Qi Haochen, Yu Mingzhu

Eksploatacja i Niezawodność

2023

Vol. 25, no. 1

art. no. 16

Accurate fault diagnosis is critical to operating rotating machinery safely and efficiently. Traditional fault information description methods rely on experts to extract statistical features, which inevitably leads to the problem of information loss. As a result, this paper proposes an intelligent fault diagnosis of rolling bearings based on a continuous wavelet transform(CWT)-multiscale feature fusion and an improved channel attention mechanism. Different from traditional CNNs, CWT can convert the 1-D signals into 2-D images, and extract the wavelet power spectrum, which is conducive to model recognition. In this case, the multiscale feature fusion was implemented by the parallel 2-D convolutional neural networks to accomplish deeper feature fusion. Meanwhile, the channel attention mechanism is improved by converting from compressed to extended ways in the excitation block to better obtain the evaluation score of the channel. The proposed model has been validated using two bearing datasets, and the results show that it has excellent accuracy compared to existing methods.