Uncertainty-Aware Heterogeneous Neural Blind Deconvolution Ensemble Network for Reliable System-Level Fault Diagnosis in Railway Transmission Systems

Fault diagnosis in railway transmission systems is critical for operational safety, yet existing methods often fail to address complex, system-level fault combinations, especially those unseen during training. This paper introduces the Uncertainty-Aware Heterogeneous Blind Deconvolution (UncertainHBD) ensemble network, an end-to-end framework for reliable system-level diagnosis. First, we construct an ensemble of heterogeneous neural blind deconvolution (HBD)-based submodels to extract robust component-level features from tri-axis vibration signals. Second, a novel prototype-similarity-based reliability method is proposed to distinguish known (in-distribution) and unknown (out-of-distribution) fault states. For known faults, the model provides a high-confidence system-level diagnosis. For novel unseen combinations, it integrates component-level predictions from submodels with a calibrated reliability score. This dualpath approach delivers high diagnostic accuracy for known fault conditions while robustly addressing previously unseen scenarios. Experimental evaluations on the BJTU-RAO bogie datasets confirm the proposed method's effectiveness and reliability.