Youlong Lyu1,2,*, Ying Chu3, Qingpeng Qiu3, Jie Zhang1,2, Jutao Guo4
CMC-Computers, Materials & Continua, Vol.85, No.2, pp. 3393-3418, 2025, DOI:10.32604/cmc.2025.068157
- 23 September 2025
Abstract 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:… More >
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