Abstract The health condition of rolling bearings is crucial for the safe and stable operation of rotating machinery. However, bearing vibration signals acquired in industrial settings typically exhibit non-stationarity, strong nonlinearity, and extremely weak early fault characteristics, which significantly limit the accuracy and practicality of traditional diagnostic methods. To address these core challenges, this study proposes a novel end-to-end fault diagnosis model named LNFormer. It enhances the modeling of raw vibration signal sequences through an input transposition operation, eliminating the need for additional positional encoding. Furthermore, by removing the Softmax function from the self-attention mechanism, the model's computational complexity is substantially reduced, improving processing efficiency for long, high-sampling-rate vibration signals common in industry and supporting real-time or near-real-time monitoring. An innovative Multi-Head Layer Normalization (MHLN) structure is also designed as a nonlinear operator to effectively capture complex fault patterns while suppressing overfitting, thereby enhancing the model's robustness and generalization capability under noisy and varying operational conditions. Extensive experiments on eight public bearing datasets demonstrate that LNFormer achieves superior diagnostic accuracy and F1-score compared to existing advanced methods, along with excellent generalization and computational efficiency. A practical case study further confirms its significant potential for direct industrial application without complex data preprocessing.
Ren et al. (2026) studied this question.