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<p>Early detection of abnormal process behavior is critical in smart manufacturing, as it contributes to reduced downtime, improved process reliability, and prevention of quality-related losses. However, many anomaly-detection studies in manufacturing still rely on isolated sensor datasets or single-machine setups, limiting their extensibility across different machines and production environments. This leaves a gap in leveraging standardized MTConnect-derived data for anomaly detection in a more interoperable multi-machine setting. To address this gap, this study develops an AI/ML-based anomaly-detection framework using MTConnect-derived data from multiple milling machine groups. Anomaly labels are assigned at the experiment level using documented run remarks, and statistical features are extracted from machine, spindle, load, vibration, energy, and runtime variables. The study first examines a single-machine baseline and then expands to a combined dataset built from two feature-compatible machine groups, imi_vm20i and imi_vmx30ui. Logistic Regression, Random Forest, and XGBoost are used for anomaly classification. In 5-fold stratified cross-validation on the combined dataset, Random Forest achieved the highest mean accuracy (0.9455 ± 0.0727), Logistic Regression achieved the highest recall (0.9000 ± 0.2000) and F1-score (0.8000 ± 0.2667), and XGBoost achieved the highest ROC-AUC (0.9222 ± 0.1556). Feature-importance analysis showed that vibration-related variables were the most influential in the full model, while an ablation study showed that Power/Energy/Runtime features performed best as a standalone group. A separate robustness experiment on the structurally different tmf_vf10 machine group showed a clear drop in performance, highlighting the importance of feature compatibility for interoperable anomaly detection. Overall, the results show that MTConnect-derived data can support anomaly detection across compatible machine groups, while also revealing the limits of transferability when machine-data structure changes.</p>

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AI/ML-based interoperable anomaly detection in advanced manufacturing using MTconnect-derived multi-machine data

  • 期刊论文

  • 发表日期：

    2026年7月6日

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  • Harshkumar K. Parmar,
  • Shivakumar Raman

摘要

Early detection of abnormal process behavior is critical in smart manufacturing, as it contributes to reduced downtime, improved process reliability, and prevention of quality-related losses. However, many anomaly-detection studies in manufacturing still rely on isolated sensor datasets or single-machine setups, limiting their extensibility across different machines and production environments. This leaves a gap in leveraging standardized MTConnect-derived data for anomaly detection in a more interoperable multi-machine setting. To address this gap, this study develops an AI/ML-based anomaly-detection framework using MTConnect-derived data from multiple milling machine groups. Anomaly labels are assigned at the experiment level using documented run remarks, and statistical features are extracted from machine, spindle, load, vibration, energy, and runtime variables. The study first examines a single-machine baseline and then expands to a combined dataset built from two feature-compatible machine groups, imi_vm20i and imi_vmx30ui. Logistic Regression, Random Forest, and XGBoost are used for anomaly classification. In 5-fold stratified cross-validation on the combined dataset, Random Forest achieved the highest mean accuracy (0.9455 ± 0.0727), Logistic Regression achieved the highest recall (0.9000 ± 0.2000) and F1-score (0.8000 ± 0.2667), and XGBoost achieved the highest ROC-AUC (0.9222 ± 0.1556). Feature-importance analysis showed that vibration-related variables were the most influential in the full model, while an ablation study showed that Power/Energy/Runtime features performed best as a standalone group. A separate robustness experiment on the structurally different tmf_vf10 machine group showed a clear drop in performance, highlighting the importance of feature compatibility for interoperable anomaly detection. Overall, the results show that MTConnect-derived data can support anomaly detection across compatible machine groups, while also revealing the limits of transferability when machine-data structure changes.