Fault diagnosis of train bearings is crucial for railway safety, yet models trained on a single operating condition often experience significant performance degradation when subjected to variations in speed or load. This paper proposes a new single-source domain generalization (SDG) model named adaptive mask flow adversarial network (AMFAN), aiming to enhance generalization capability by effective cross-domain simulation based on learnable perturbations in feature space. An adaptive mask mechanism is designed to determine domain-sensitive feature elements. And a feature perturbation strategy is conducted to the determined feature elements via a pre-trained flow model. Experiments on two train bearing datasets verify the superior performance over state-of-the-art methods. The results prove that the controlled feature expansion provides a viable and robust pathway for SDG, showing strong potential for real-world train bearing fault diagnosis when confronting unknown operating conditions.
Jun Wang, Bo Yu, He Ren et al.· ISA transactions· 0 citations
Spiking neural network (SNN) has drawn substantial research focus due to its high biological interpretability, low energy consumption and effectiveness in time-dependent data processing. However, existing methods fail to fully integrate the biological interpretability of SNN with the physical interpretability of time-frequency transform methods in machinery fault diagnosis. To this end, we propose a spiking time-frequency patching (STFP) spiking neural network model, in which a STFP module is designed for time-frequency feature extraction inside the network, and a spiking temporal–spatial attention module is designed to focus on important features of temporal–spatial dimensions. In addition, the spiking residual network used in the model is also improved in neurons. Extensive experimental results on two datasets of rotating machinery parts demonstrate that, the proposed model achieves superior diagnostic performance benefiting from the designed modules, furnishing an end-to-end fault diagnosis method excelling in accuracy, stability, and interpretability.
Shilong Zhu, Jun Wang, Weiguo Huang et al.· IEEE Transactions on Reliabi...· 0 citations