WT-STCA-ViT: Window-transform synergistic spectro-temporal channel attention Vision Transformer for robust bearing fault diagnosis

This is the authors' abstract. We don't add key points for this paper.

Reliable bearing fault diagnosis under variable operating conditions and severe noise remains challenging because empirically selected time-frequency front ends may produce degraded representations, while generic attention mechanisms lack explicit sensitivity to fault-related spectral structures. To address these limitations, this study proposes a robust cascaded framework termed window-transform synergistic spectro-temporal channel attention Vision Transformer (WT-STCA-ViT), which integrates a window-enhanced Fourier-based synchrosqueezing transform (FSST) with a spectro-temporal channel attention Vision Transformer (STCA-ViT). Specifically, a parameterized α - exponential Kaiser - Blackman (α - EKB) window family and a task-constrained parameter screening strategy are established to optimize spectral concentration and transient localization, mitigating energy spreading across noisy scenarios. The resulting high-resolution time-frequency maps are encoded using a pretrained Vision Transformer (ViT) backbone and refined via the proposed frequency-axis-structure-aware spectro-temporal channel attention (STCA) mechanism, which generates frequency-dependent channel weights to calibrate band-level features while suppressing noise-dominated and irrelevant feature responses. Furthermore, a lightweight post-Transformer encoder and LoRA adapters are coupled to achieve global token-level refinement and parameter-efficient optimization. Extensive benchmarks on the public CWRU dataset and a self-built cylindrical roller bearing dataset demonstrate competitive diagnostic accuracy, improved weak-fault recognition, and stable feature separability, with computational cost analysis further confirming a favorable trade-off among diagnostic robustness, trainable-parameter efficiency, and potential for low-latency online deployment.