RMA-CNN: A Residual Mixed-Domain Attention CNN for Bearings Fault Diagnosis and its Time-Frequency Domain Interpretability

Abstract

Early fault diagnosis of bearings is crucial for ensuring safe and reliable operations. Convolutional Neural Networks (CNNs) have achieved significant breakthroughs in machinery fault diagnosis. However, complex and varying working conditions can lead to inter-class similarity and intra-class variability in datasets, making it more challenging for CNNs to learn discriminative features. Furthermore, CNNs are often considered "black boxes" and lack sufficient interpretability in the fault diagnosis field. To address these issues, this paper introduces a Residual Mixed-Domain AttentionCNN method, referred to as RMA-CNN. This method comprises multiple ResidualMixed Domain Attention Modules (RMAMs), each employing one attention mechanism to emphasize meaningful features in both time and channel domains. This significantly enhances the network's ability to learn fault-related features. Moreover, we conduct an in-depth analysis of the inherent feature learning mechanism of the attention module RMAM to improve the interpretability of CNNs in fault diagnosis applications. Experiments conducted on two datasets—a high-speed aeronautical bearing dataset and a motor bearing dataset—demonstrate that the RMA-CNN achieves remarkable results in diagnostic tasks.