A Novel Bearing Fault Diagnosis Framework with a Multi-Scale Feature Extraction Module and Efficient Content-Guided Attention Mechanism

Rolling bearing faults originate from complex tribodynamic interactions among rolling elements, raceways, and the cage, yielding nonlinear, non-stationary vibration signals that are highly susceptible to noise and operating-condition variations, which compromises the reliability of diagnosis. To address this issue, this paper proposes the RConvNeXt–ECGA framework. The main contributions are twofold: (1) RConvNeXt is a convolutional module based on ConvNeXt, which achieves efficient multi-scale feature extraction through grouped parallel convolutions with multiple receptive fields; (2) Efficient Content-Guided Attention (ECGA) is a novel pixel-level attention mechanism, which adaptively reweights feature maps to highlight informative regions and suppress irrelevant interference. The proposed method achieves an average accuracy of 99.8% on bearing datasets from Case Western Reserve University and Huazhong University of Science and Technology, and 94.33% under cross-operating-condition tests, demonstrating superior robustness and generalization over representative deep learning-based baseline models.