XG-MSTA Machine Tool Anomaly Detection Method Based Multi-Modal Signals

Computer Numerical Control (CNC) machine tools are pivotal in modern manufacturing, yet their machining performance, precision, and service life are frequently compromised by anomalies in axis motion arising from assembly quality issues or wear due to prolonged use. To address the challenge of accurate anomaly detection in CNC machine tools, this study proposes a novel Cross-Modal Gated Multi-Scale Transformer (XG-MST) model, leveraging multi-axis vibration and current signals for supervised learning. Evaluated on a self-collected dataset derived from real-world CNC machine tool operations, the model achieves a detection accuracy of over 95%. Further validation through small-batch multi-axis simulation experiments on the open-source Paderborn Bearing Dataset yields an accuracy of over 90%. These results underscore the XG-MST algorithm's superior performance and significant practical value for enhancing machine tool quality inspection and predictive maintenance in manufacturing.