Physics-guided multi-view hybrid depth-wise separable convolution and temporal LSTM network for fault diagnosis of rotor-bearing systems

Introduction Rolling-element bearings are critical components of rotating machinery, and their failures can affect equipment safety, availability, and service life. However, non-stationary vibration signals and background noise make reliable automatic fault diagnosis challenging. This study proposes a physics-guided Hybrid Depth-wise Separable Spatial Convolution and Temporal Long Short-Term Memory network (HDSSC-TLST) for rotor-bearing fault diagnosis. Methods The proposed approach derives the minimum sample length from the characteristic defect frequencies of the inner race, outer race, and rolling elements. A leakage-free chronological multi-view sliding-window scheme generates large windows to capture impact periodicity and centered small windows to resolve high-frequency transients. Both views are converted into continuous wavelet transform (CWT) scalograms using a complex Morlet wavelet. Depth-wise separable convolution blocks extract spatial time-frequency features, while a stacked LSTM models their temporal evolution to classify four conditions: normal, inner-race fault, outer-race fault, and ball fault. Results Experiments using the Case Western Reserve University (CWRU) 48 kHz drive-end dataset show that HDSSC-TLST achieves 94% accuracy at a signal-to-noise ratio (SNR) of 19 dB and outperforms the hybrid adaptive fusion network (HAFDL) and nonlinear auto-regressive model with exogenous inputs (NARX) at every tested SNR from 1 to 19 dB. Averaged over ten data sizes, the model achieves precision, recall, and F1-score values of 0.90, compared with 0.79, 0.78, and 0.79 for HAFDL and 0.75, 0.74, and 0.74 for NARX, respectively. It also reduces training time by 14.5% relative to HAFDL and 18.8% relative to NARX. On a leakage-free chronological split of noise-free 1 hp data, it achieves 96.0 ± 0.8% accuracy and a macro-F1 score of 0.960 ± 0.009. Its mean accuracy on unseen 0, 2, and 3 hp loads is 88.1% without retraining. Discussion The results indicate that combining physics-guided sample selection, multi-view time-frequency representation, depth-wise separable convolution, and temporal modeling can provide an effective and computationally efficient approach to bearing fault diagnosis under noisy conditions and changing operating loads.