To address low reliability of fault diagnosis in ship motor rolling bearings, caused by weak fault signals and the difficulty of extracting fault features, this paper proposed a feature extraction method that integrates variational mode decomposition (VMD) with wavelet packet fuzzy entropy (WPFE). Furthermore, a support vector machine model optimized by the black-winged kite algorithm (BKA-SVM) is introduced to enhance diagnostic accuracy.

First, collected motor bearing vibration signals are decomposed via VMD, and optimal IMF components are selected by minimum envelope entropy. Next, selected IMFs undergo wavelet packet decomposition to compute fuzzy entropy. Finally, a BKA-SVM model is constructed for fault diagnosis and classification with extracted feature data.

Simulation experiments with SVM optimized by different algorithms, combined with validation on a self-constructed experimental platform, show that the diagnostic accuracy of the BKA-SVM model for three different sample sets reaches 98.33%–100%. Compared with SVM models optimized by the particle swarm optimization (PSO) algorithm, sparrow search algorithm (SSA) and newton-raphson-based optimizer (NRBO), the BKA-SVM demonstrates superior classification performance and higher accuracy in the extraction and diagnosis of rolling bearing faults.

The findings of this study provide a valuable reference for the fault diagnosis of ship motor bearings.