Research on Bearing Fault Diagnosis Method Based on Multi-Scale Convolution and Attention Mechanism in Strong Noise Environment

Aiming to address the suboptimal diagnostic efficacy of conventional approaches in rolling bearing fault diagnosis under complex operational scenarios—particularly when subjected to intense noise contamination—this study introduces a novel fault diagnosis framework leveraging the synergistic attention mechanisms of MSCNN and GRU.The workflow begins with conditioning vibration signals sourced from the CRUW bearing dataset, followed by robust extraction of multi-scale fault features through a multi-scale convolutional neural network (MSCNN). To enhance temporal modeling, a gated recurrent unit (GRU) is integrated to extract temporal dependencies from the signals. Further improvements are achieved by enhancing the base model with Transformer layers and a convolutional attention module (CBAM), respectively. Experimental validation is conducted to compare the diagnostic performance of three configurations MSCNN+GRU, MSCNN+GRU+Transformer, and MSCNN+GRU+CBAM—under varying noise conditions. Outcomes demonstrate that the MSCNN+GRU+CBAM model exhibits superior fault detection accuracy and optimal generalization capacity across different load and rotational speed settings, confirming its notable advantage in rolling bearing fault diagnosis.