Aiming at the limitations of existing multivariate signal decomposition methods such as fast multivariate empirical mode decomposition (FMEMD) and completely adaptive projection multivariate local characteristic-scale decomposition (CAPMLCD) for gear fault diagnosis, this paper proposes a fast multivariate all-time-scale decomposition (FMATD) method. FMATD incorporates the ATD as its one-dimensional kernel within an efficient “projection-decomposition-reconstruction” framework, preserving the mode separation capability and adaptivity of ATD. Meanwhile, the efficient decomposition framework enhances computational efficiency and avoids over-decomposition. Furthermore, a fast projection strategy is designed to select the projection vectors in real time based on the signal energy distribution, thereby enhancing computational efficiency and decomposition accuracy. Applying FMATD to gear simulation signals and real vibration signals from faulty face gears demonstrates that the proposed method can effectively extract fault modes from face gear signals. Compared with FMEMD, CAPMLCD, and multivariate variational mode decomposition, FMATD yields the component with the clearest fault features in the envelope spectrum. In terms of computational efficiency, FMATD outperforms both FMEMD and CAPMLCD.
Zhengyang Cheng, Jie Zhou, Haidong Shao et al.· Structural Health Monitoring· 0 citations
The operational reliability of rotating machinery is critical for modern industrial systems. However, existing large language model (LLM)-based fault diagnosis approaches face challenges in processing high-frequency, strong-noise vibration signals, including modal misalignment, cross-modal negative fusion, and limited learning capacity for early-stage weak faults. To address these issues, this paper proposes a gate-free multi-physical field fusion LLM (GMPF-LLM) for fault diagnosis. The method constructs a parallel multi-physical feature extraction and alignment mechanism to effectively decouple steady-state harmonics, background residuals, and transient impulses. A continuous multimodal projector with a gate-free residual fusion structure is employed to mitigate information distortion and noise interference during cross-modal fusion. Furthermore, a difficulty-aware joint optimization strategy using low-rank adaptation (LoRA) and multi-class focal loss enhances recognition of early weak faults under low signal-to-noise ratio conditions. Experiments on the XJTU-SY and GDUPT bearing datasets demonstrate mean diagnostic accuracies of 99.25% and 90.55% with minimal standard deviation, outperforming mainstream methods. Ablation studies further confirm the contributions of each core module. This work provides an effective framework for integrating multi-physical field data with LLMs in industrial fault diagnosis.
Quansi Huang, Guanhua Zhu, Renhui Yu et al.· Engineering Research Express· 0 citations
