Abstract
The non-stationarity of high-speed train axle-box bearing vibration signals, combined with distribution discrepancies between laboratory and field data, makes it challenging to directly apply fault diagnosis methods trained on experimental data to actual operating conditions. This paper proposes a four-branch multi-modal unsupervised domain-adaptive fault diagnosis framework, termed CRG-DA Net, based on ConvNeXt and ResNet1D, aimed at enabling cross-condition fault diagnosis under unlabeled target data. Multi-scale and multi-domain fault features are extracted from raw vibration signals using envelope spectrum analysis, short-time Fourier transform, and wavelet transform. A four-branch parallel network is then constructed, employing ConvNeXt-Tiny for modeling the time-frequency representations and ResNet1D for learning the time-domain characteristics of raw signals, with each branch generating high-dimensional feature representations and corresponding fault prediction logits. To account for the varying discriminative contributions of different modalities, a gated dynamic fusion mechanism is introduced, which computes sample-specific fusion weights from the concatenated branch features and integrates the individual branch predictions into a final fused output. In addition, adversarial domain adaptation combined with pseudo-label self-training is employed to align the source and target domain feature distributions, while target samples are classified following the same gated fusion and prediction procedure. Extensive experiments on multi-condition laboratory datasets and real-world operating data demonstrate that the proposed CRG-DA Net achieves outstanding fault diagnosis performance, meeting the expected experimental performance and exhibiting strong generalization across different operating conditions.