Dirichlet-weighted composite data synthesis for robust transfer learning in high-speed railway bearing fault diagnosis

Abstract

High-Speed Railway (HSR) bearing health monitoring is essential for ensuring operational safety, reducing unexpected service interruptions, and supporting condition-based maintenance in practical railway systems. In real engineering environments, however, bearing fault diagnosis remains challenging because field vibration signals are usually unlabeled, affected by changing operating conditions, and may contain superimposed fault components rather than isolated single fault patterns. To address these challenges, this study proposes a lightweight and interpretable transfer learning framework for HSR bearing fault diagnosis under limited labeled data and compound fault conditions. The proposed framework integrates data cleaning, resampling to 24 kHz according to the Nyquist sampling theorem, wavelet threshold denoising, and multi-domain feature extraction from time, frequency, and time-frequency domains. The discriminative capability of the extracted 25-dimensional feature representation was verified using Long Short-Term Memory (LSTM), Gated Recurrent Unit (GRU), Random Forest, and Light Gradient Boosting Machine (LightGBM) models, all of which achieved source domain classification accuracies above 99%. To reduce the gap between laboratory single fault data and unlabeled field compound fault data, a feature-level Dirichlet-weighted nonlinear synthesis strategy was developed to generate synthetic composite fault feature vectors from source domain samples. A lightweight fully connected regressor was then trained to estimate proportional fault contributions for target domain samples, enabling probabilistic diagnosis without target domain labels. The effectiveness of domain adaptation was confirmed by t-distributed Stochastic Neighbor Embedding (t-SNE) visualization, which showed clear alignment between source and target feature distributions. External validation on a public multi-domain compound fault dataset achieved 80.99% accuracy, with a Mean Squared Error (MSE) of 0.0799 and a Mean Absolute Error (MAE) of 0.1452. These results indicate that the proposed framework can identify bearing fault components under real compound machine fault interference and provide a practical solution for HSR bearing health monitoring, early fault warning, and maintenance decision support when labeled field data are limited.