Metric-Based Meta-Learning Relation Network for Cross-Domain Few-Shot Bearing Fault Diagnosis

Deep learning techniques have recently shown great promise in the field of bearing fault diagnosis, yet their performance is often limited by practical challenges such as insufficient fault data and varied working conditions in real-world industrial settings. Furthermore, traditional deep transfer learning approaches often require extensive parameter fine-tuning for specific tasks, thus reducing their adaptability in scenarios where rapid deployment is crucial. To address these issues, we propose a novel metric-based meta-learning relation network designed for few-shot bearing fault diagnosis across diverse domains. This method directly targets an industrial need: accurate fault detection with minimal training samples under unseen operational conditions. This capability is critical for predictive maintenance systems in resource-constrained environments. Vibration signals from various working conditions are first transformed into two-dimensional time-frequency images. These samples are then divided into meta-training and meta-testing sets, with each set further split into support and query subsets according to a meta-learning strategy. Following this division, a residual shrinkage non-local feature extraction module is introduced to extract and combine features from both subsets. A neural network with a nonlinear metric is subsequently employed to compute similarity scores between the support and query sets. The proposed method enables rapid and precise bearing fault diagnosis, even with limited data samples and under unknown working conditions, which are typical in maintenance workshops and field operations. Comparative tests on three datasets demonstrate that our approach outperforms existing methods under different working conditions and noise levels, highlighting its potential to reduce unplanned downtime and improve equipment reliability in real industrial applications. The experimental results further confirm the method’s robust generalization and rapid adaptability.