Bearing Fault Diagnosis Based on Multi-Channel WOA-VMD and Tucker Decomposition

To address the challenges that rolling bearing vibration signals are easily affected by noise and that traditional single-channel methods cannot fully exploit multi-channel information, this paper proposes a multi-channel fault diagnosis method combining Whale Optimization Algorithm-assisted Variational Mode Decomposition (WOA-VMD) with Tucker tensor decomposition. In this method, multi-channel vibration signals are first adaptively decomposed using WOA-VMD, with optimized decomposition parameters to effectively extract weak fault features. The resulting intrinsic mode functions (IMFs) are then structured into a third-order tensor to preserve inter-channel correlations. Tucker decomposition is subsequently applied to extract robust feature vectors from the tensor factor matrices, achieving dimensionality reduction, redundancy suppression, and enhanced noise mitigation. Finally, statistical features such as standard deviation, kurtosis, and waveform factor are computed from the denoised signals and fed into a Support Vector Machine (SVM) classifier for precise fault identification. Experimental results show that the proposed method outperforms traditional approaches in extracting weak fault features, effectively leveraging correlations among multi-channel signals to extract meaningful features from noise-corrupted signals, and achieving efficient and reliable fault diagnosis.