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. Initially, the DTCWT is employed to decompose the torque signal into multiple sub-bands using range entropy (RE). Subsequently, the RCMFDE is calculated for each sub-band to construct discriminative fault feature vectors. These vectors are then used to train and test the LSSVM classifier to identify different types of misalignment faults. The proposed method was validated using experimental data, and the results demonstrate its superior diagnostic performance. Compared to existing approaches, the DTCWT-RCMFDE-LSSVM model achieved the highest classification accuracy of 98.33%, outperforming other methods such as MSE-SVM (94.1%), DTCWT-EE-PSO-SVM (96%), Multi-features-t-SNE-LSSVM (96.25%) and AR model coefficients-mRMR-SOM neural network (97.22%). These findings confirm the method's high precision in detecting both parallel and angular misalignments. This research holds significant potential for industrial applications in sectors reliant on rotating machinery such as power generation, petrochemical, nuclear, and manufacturing where early and accurate fault detection is essential to minimize downtime and enhance operational reliability.