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Xudong Song

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Open access 2026

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

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.

Xudong Song, Qi-Peng Zhao, Yang Liu · 0 citations
Open access Aug 2026

A Remaining Useful Life Prediction Method for Aero-Engines Based on Degradation-Aware Masked Augmentation and a CNN–Transformer Hybrid Network

Accurate remaining useful life (RUL) prediction is essential for condition-based maintenance and safe aero-engine operation. To address non-stationary monitoring data, limited informative degradation samples, and the difficulty of jointly modeling local degradation patterns and temporal dependencies, this study proposes a degradation-aware dynamic masking augmentation method combined with a multiscale CNN–Transformer network. The 21 sensor variables in the NASA C-MAPSS dataset are first grouped by physical meaning and reconstructed into four-dimensional state features. A degradation-state score integrating local variance and trend slope is then used to adapt temporal masking probabilities across degradation stages, while feature masking probabilities are assigned according to feature importance. Masked positions are filled with adjacent unmasked observations, and invalid augmented samples are removed through trend consistency verification. A multiscale CNN with channel attention extracts local degradation features, and a Transformer encoder captures temporal dependencies. Bayesian optimization is used to determine key hyperparameters. On FD001, the proposed method achieves an MSE of 348.3970, an MAE of 8.3908, and an R2 of 0.9227, reducing MSE and MAE by 4.27% and 28.42%, respectively, compared with ML-RFR. It also achieves the highest R2 of 0.9369 on FD003. Cross-dataset and multi-seed experiments further confirm its applicability and stability.

Xudong Song, Guohua Wu, Meng-Dan Wang et al. · 0 citations