A Complex Attention Transformer for Bearing Fault Diagnosis Based on Motor Current Signals

Bearing fault diagnosis is essential for ensuring the reliability and operational efficiency of rotating machinery. Traditional diagnostic methods often rely on vibration acceleration signals, which require intrusive hardware for signal acquisition and struggle to detect specific fault types, such as cage faults. Because bearings are often located deep within mechanical transmission systems, their vibration characteristics do not propagate effectively to external monitoring points, making vibration acceleration sensors less sensitive to certain faults. To address these challenges, we propose CAFormer, a complex attention transformer for bearing fault diagnosis based on motor current signals. In traction motor control, the Clarke and Park transforms are commonly used to convert three-phase AC signals into the d-q coordinate system, where D-axis and Q-axis currents are represented as orthogonal signals. In this article, we first propose a complex attention to process dual-channel orthogonal data and enhance fault feature separation. Secondly, to address the imbalance between fault and healthy samples, we propose a new loss function that assigns adaptive optimization gradients to each sample, thereby accelerating model convergence. We conducted extensive experiments on two datasets: the Paderborn University dataset and a bearing dataset generated using a mechanical transmission simulator. Our experiments demonstrate that CAFormer significantly reduces computational costs while achieving state-of-the-art performance, surpassing existing methods in both fault classification accuracy and adaptability to imbalanced datasets.