Enhancement of Bearing Fault Diagnosis Using Optimized Variational Decomposition, Entropy-Based Modal Reconstruction, and Evolutionary Bidirectional Fusion Network

Rolling bearing vibration signals often exhibit strong nonstationarity and are susceptible to noise interference, which makes fault feature extraction and accurate diagnosis challenging under complex operating conditions. To address these issues, this paper proposes a fault diagnosis pipeline that sequentially combines an improved snow ablation optimizer (ISAO), variational generalized nonlinear mode decomposition (VGNMD), and a bidirectional temporal sequence fusion network (BiTSF-Net). Firstly, ISAO is used to optimize the key parameters of VGNMD, including the bandwidth penalty parameter and smoothing constraint parameter, with minimum envelope entropy as the fitness function. Secondly, the optimized VGNMD decomposes raw vibration signals into modal components, and the modal component with the minimum envelope entropy is selected to highlight fault-related impulsive characteristics. Thirdly, 11-dimensional time-domain statistical features are extracted from the selected optimal modal component to characterize bearing health states. Finally, these extracted features are used as the input to BiTSF-Net, which combines bidirectional temporal convolutional networks and bidirectional long short-term memory networks in a parallel structure to learn local transient features and temporal dependencies for fault classification. Experimental validation is conducted on the Case Western Reserve University dataset. Comparative results with convolutional neural networks, gated recurrent units, and long short-term memory networks demonstrate that the proposed pipeline achieves superior diagnostic performance, with an average accuracy of 99.63% and a maximum accuracy of 100%. These results confirm the effectiveness and robustness of the proposed ISAO-VGNMD feature extraction and BiTSF-Net classification pipeline for bearing fault diagnosis under complex nonstationary conditions.