Diagnosis of misalignment faults using the DTCWT-RCMFDE and LSSVM algorithms.

Misalignment is among the most frequent mechanical faults in rotating electrical machines, often resulting in partial or complete motor failure over time. To tackle this issue, the present study proposes an innovative methodology for diagnosing misalignment faults in rotating electrical machines. The method integrates the dual-tree complex wavelet transform with a refined composite multiscale fluctuation dispersion entropy algorithm (DTCWT-RCMFDE) for feature extraction, combined with the least-squares support vector machines algorithm (LSSVM) for fault classification.