Fault Diagnosis in Rotation Motor Bearings: Novel Interpretable Indicator and Improved Snow Ablation Optimizer

Abstract Early bearing fault diagnosis is essential for stable operation and low-cost maintenance of rotation motors. A rolling bearing fault diagnosis method for rotation motors is proposed, leveraging a novel interpretable short-time kurtosis error (SKE) indicator and the improved snow ablation optimizer (ISAO). Initially, the Cauchy mutation mechanism is employed to bolster the global search capability of ISAO, thereby optimizing parameter settings. Subsequently, ISAO is utilized to adaptively configure key parameters within the adaptive feature mode decomposition (AFMD), reducing reliance on prior knowledge. The SKE indicator is then developed to enhance the efficacy of AFMD in extracting fault features. The proposed AFMD is validated using bearing single and composite fault data from two experimental platforms. The experimental results under various fault conditions show that AFMD can adaptively perform fault feature extraction and robust to noise interference. Meanwhile, the proposed method performs better than existing methods, such as traditional FMD and dung beetle optimizer-based variational mode decomposition (IDBO-VMD). This study advances state-of-the-art bearing fault diagnosis and offers a more interpretable and adaptive rotation motor condition monitoring solution.