Vision to Detection: Physics-Guided Data Augmentation and Weighted Random Forests for Anomaly Detection in Electromagnetic Needle Selection

The motion anomalies of needle-selection blades in electromagnetic needle selectors are subtle and rare. Limited by insufficient observation, feature extraction difficulty, and severe class imbalance due to scarce fault samples, data-driven models suffer in training and generalization. To address this, we propose a physics-guided anomaly detection method based on visually captured trajectory data. A system dynamics model generates physically consistent virtual anomaly samples to mitigate data imbalance, while normal-data pre-training enhances rare-fault recognition. Building on physics-informed data augmentation, the proposed enhanced weighted random forest achieves 97% in WAP, WAR, and WAF, with F1-scores of 0.84 for two rare anomaly classes and recall improved to 92% and 80%, outperforming mainstream data augmentation and classification methods, enabling efficient rare-fault detection under small-sample conditions.