Rolling Bearing Fault Diagnosis Model Based on Multi-Scale Depthwise Separable Convolutional Neural Network Integrated with Spatial Attention Mechanism

• Published in: Sensors (Basel, Switzerland) Vol. 25; no. 13; p. 4064
• Main Authors: Jin, Zhixin, Hu, Xudong, Wang, Hongli, Guan, Shengyu, Liu, Kaiman, Fang, Zhiwen, Wang, Hongwei, Wang, Xuesong, Wang, Lijie, Zhang, Qun
• Format: Journal Article
• Language: English
• Published: Switzerland MDPI AG 30.06.2025; MDPI
• Subjects: Accuracy; Adaptation; Bearings; Datasets; Deep learning; Fault diagnosis; Fourier transforms; Gramian angular difference field; multi-scale depthwise separable convolutional neural network; Neural networks; rolling bearing; Time series; transfer learning; Wavelet transforms; Gramian angular difference field; transfer learning; rolling bearing; fault diagnosis; multi-scale depthwise separable convolutional neural network
• ISSN: 1424-8220; 1424-8220
• DOI: 10.3390/s25134064

In response to the challenges posed by complex and variable operating conditions of rolling bearings and the limited availability of labeled data, both of which hinder the effective extraction of key fault features and reduce diagnostic accuracy, this study introduces a model that combines a spatial attention (SA) mechanism with a multi-scale depthwise separable convolution module. The proposed approach first employs the Gramian angular difference field (GADF) to convert raw signals. This conversion maps the temporal characteristics of the signal into an image format that intrinsically preserves both temporal dynamics and phase relationships. Subsequently, the model architecture incorporates a spatial attention mechanism and a multi-scale depthwise separable convolutional module. Guided by the attention mechanism to concentrate on discriminative feature regions and to suppress noise, the convolutional component efficiently extracts hierarchical features in parallel through the multi-scale receptive fields. Furthermore, the trained model serves as a pre-trained network and is transferred to novel variable-condition environments to enhance diagnostic accuracy in few-shot scenarios. The effectiveness of the proposed model was evaluated using bearing datasets and field-collected industrial data. Experimental results confirm that the proposed model offers outstanding fault recognition performance and generalization capability across diverse working conditions, small-sample scenarios, and real industrial environments.