A Rolling Bearing Fault Diagnosis Method Based on the STRN-CM Model

The operational safety of rotating machinery heavily relies on the condition of its rolling bearings. However, under strong background noise and variable operating conditions, weak fault-induced impact responses are easily overwhelmed. To address these challenges, this paper proposes a dual-branch cross-modal fault diagnosis framework (STRN-CM) that integrates a Swin Transformer with a one-dimensional wide-kernel deep residual network (1D ResNet). The model develops a complementary structure of heterogeneous features. The enhanced 1D ResNet branch responds directly to the passage of volatile impulse features, which can detect early errors through raw vibrations. The Swin Transformer branch captures long-term periodic texture windows by using time–frequency images, which have an important dependence on time. Also, a Cross-Modal Attention Fusion (CMAF) scheme is introduced. Using high signal-to-noise ratio (SNR) temporal impulse features as query probes, the mechanism dynamically calibrates the response weights of time–frequency features, thereby achieving adaptive denoising and enhancement at the feature level. Experimental results demonstrate that STRN-CM achieves a diagnostic accuracy of 93.04% in harsh −6 dB noise conditions on the Case Western Reserve University (CWRU) dataset. Furthermore, it achieves a 97.99% accuracy on the Paderborn University (PU) dataset, showcasing superior generalization in cross-load and real fatigue damage transfer tasks. It also demonstrates significantly better generalization performance than single-modal networks in cross-load and real fatigue damage transfer tasks.