A Causal-Transformer Based Meta-Learning Method for Few-Shot Fault Diagnosis in CNC Machine Tool Bearings

In intelligent manufacturing processes such as aerospace production, computer numerical control (CNC) machine tools require real-time optimization of process parameters to meet precision machining demands. These dynamic operating conditions increase the risk of fatigue damage in CNC machine tool bearings, highlighting the urgent demand for rapid and accurate fault diagnosis methods that can maintain production efficiency and extend equipment uptime. However, varying conditions induce feature distribution shifts, and scarce fault samples limit model generalization. Therefore, this paper proposes a causal-Transformer-based meta-learning (CTML) method for bearing fault diagnosis in CNC machine tools, comprising three core modules: (1) the original bearing signal is transformed into a multi-scale time-frequency feature space using continuous wavelet transform; (2) a causal-Transformer architecture is designed to achieve feature extraction and fault classification based on the physical causal law of fault propagation; (3) the above mechanisms are integrated into a model-agnostic meta-learning (MAML) framework to achieve rapid cross-condition adaptation through an adaptive gradient pruning strategy. Experimental results using the multiple bearing dataset show that under few-shot cross-condition scenarios (3-way 1-shot and 3-way 5-shot), the proposed CTML outperforms benchmark models (e.g., Transformer, domain adversarial neural networks (DANN), and MAML) in terms of classification accuracy and sensitivity to operating conditions, while maintaining a moderate level of model complexity.