Rolling Bearing Fault Diagnosis Based on Time-Frequency Representation and Multi-Scale Feature Learning

Abstract

A rolling bearing fault diagnosis method based on multi-scale depthwise separable convolution (MDSC) and a convolutional neural network–Transformer hybrid model (CNN-Transformer) is proposed to address the non-stationarity of fault signals and the difficulty of jointly capturing local and global features. First, continuous wavelet transform (CWT) converts one-dimensional vibration signals into two-dimensional time-frequency images to enhance fault representation. Then, multi-scale convolution (MSC) and depthwise separable convolution (DSC) are introduced to extract local impulsive features and fault patterns at different scales with fewer parameters. A CNN-Transformer architecture is further developed, where convolutional neural network (CNN) captures local details and Transformer models global dependencies. In addition, pretraining-finetuning, data augmentation, label smoothing, and normal sample optimization are adopted to improve training stability and diagnostic performance. Experimental results show accuracies of 98.80% on the Xi’an Jiaotong University bearing dataset (XJTU-SY) and 100.00% on the Case Western Reserve University bearing dataset (CWRU), demonstrating strong discriminative ability, stability, and robustness.