A Bearing Fault Diagnosis Method for Variable Operating Conditions Based on MMDSC-CBAM-BiLSTM

Abstract

Bearing fault diagnosis under variable operating conditions is challenging because changes in rotational speed and load alter the vibration response and cause substantial distribution shifts between operating domains. To improve cross-condition fault recognition, this study proposes an MMDSC-CBAM-BiLSTM model that combines multi-scale depthwise separable convolution (MMDSC), convolutional block attention (CBAM), and bidirectional long short-term memory (BiLSTM). The vibration signals are first transformed into time–frequency representations using a wavelet transform. MMDSC then extracts complementary fault features at multiple spatial scales with reduced convolutional cost, CBAM adaptively reweights informative channel and spatial responses, and BiLSTM models bidirectional temporal dependencies before feature fusion and Softmax classification. The physical interpretation of the diagnosis is linked to the characteristic vibration responses generated by localized defects on the inner race, outer race, and rolling element, while the network itself learns discriminative representations rather than explicitly reconstructing defect morphology. Cross-condition experiments on the CWRU and Jiangnan University bearing datasets yield average accuracies of 98.22% and 93.26%, respectively, demonstrating improved robustness and generalization under varying operating conditions. The results indicate that the proposed architecture provides an effective data-driven solution for variable-condition bearing fault diagnosis.