Adaptive Spectral Partitioning and Gabor-GRU for Robust Fault Diagnosis of the Multifunction Vehicle Bus

Abstract

Reliable fault diagnosis of the Multifunction Vehicle Bus (MVB) is essential for ensuring railway operational safety. Spectral analysis shows that MVB signal magnitude is unevenly distributed and mainly concentrated below 10 MHz, suggesting that conventional uniform spectral partitioning may not align well with this characteristic. To better exploit the non-uniform spectral characteristics of MVB signals, we propose a fault diagnosis framework centered on adaptive spectral partitioning and complemented by temporal dependency modeling. Specifically, a Cumulative Magnitude Partitioning Gabor (CMP-G) method is developed to adaptively partition the spectrum into multiple bands based on the cumulative spectral magnitude distribution, on the basis of which a Multi-band Attention Gabor-GRU (MBAGGRU) model is constructed to perform band weighting and capture temporal dependencies. On a test set from an experimentally acquired dataset containing nine simulated MVB physical-layer states, the proposed method achieves 99.80% classification accuracy. To further evaluate robustness, the method is tested on three independently acquired test sets from separate acquisition sessions and under five interference types, including random single-tone frequency-domain interference, bounded additive time-domain noise, additive white Gaussian noise (AWGN), burst-transient interference, and fractional ( $1/f^{\beta } $ ) noise. Across these test scenarios, the proposed method achieves competitive diagnostic performance compared with the STFT-based and wavelet-based baselines. In addition, controlled comparisons with multiple spectral partitioning schemes and alternative backbone classifiers further validate the design. The competitive performance maintained across all tested interference conditions indicates that the proposed framework is effective for MVB physical-layer fault diagnosis and shows potential for Prognostics and Health Management (PHM) in railway systems.