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About this paper
Wen Zhao, Jiawen Ding, Xueting Huang, Yibo Zhang
Researchers at Chongqing University of Posts and Telecommunications and collaborators developed an interpretable predictive maintenance framework for industrial milling machines, leveraging machine learning and SHAP analysis. The framework, based on a six-step experimental process, benchmarks various algorithms to accurately predict machine failures and provides actionable insights into critical operational features.
Key insights
- Ensemble learning models, particularly Gradient Boosting, XGBoost, and Random Forest, demonstrated superior predictive accuracy and generalization capability for milling machine fault prediction.
- SHAP analysis effectively identified and quantified the influence of key operational parameters, such as processing temperature, torque, speed, and tool wear time, on failure predictions.
- Strong positive correlations exist between overall machine failure and specific fault types like heat dissipation fault (HDF), power fault (PWF), and overload fault (OSF).
Summary by alphaXiv.
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