Research on fault prediction optimization of CNC machine tools based on ensemble learning

Li Yao; Yuwen Wan; Srdjan Damjanovic; Zhengfei Xin

doi:10.1117/12.3052979

3 January 2025 Research on fault prediction optimization of CNC machine tools based on ensemble learning

Li Yao, Yuwen Wan, Srdjan Damjanovic, Zhengfei Xin

Author Affiliations +

Proceedings Volume 13442, Fifth International Conference on Signal Processing and Computer Science (SPCS 2024); 1344220 (2025) https://doi.org/10.1117/12.3052979
Event: Fifth International Conference on Signal Processing and Computer Science (SPCS 2024), 2024, Kaifeng, China

Abstract

As the manufacturing industry develops towards high precision and intelligence, CNC machine tools play an important role in production. The occurrence of failures not only reduces production efficiency but also increases costs. In order to improve the accuracy and efficiency of fault prediction, this paper establishes an integrated learning model for CNC machine tool fault prediction by stacking ensemble learning algorithms and combining decision trees, support vector machine (SVM), random forests and other algorithms. The fault-related features are optimized through data preprocessing and feature engineering, and the results are finally obtained. The experimental results show that the ensemble learning model is superior to the single model in result indicators, especially recall rate and F1 score, reaching 0.6393 and 0.7027. This verifies that the ensemble learning model proposed in this paper has better performance in improving the performance of CNC machine tool fault prediction and can better solve the problems related to prediction in CNC machine tool faults.

(2025) Published by SPIE. Downloading of the abstract is permitted for personal use only.

Citation Download Citation

Li Yao, Yuwen Wan, Srdjan Damjanovic, and Zhengfei Xin "Research on fault prediction optimization of CNC machine tools based on ensemble learning", Proc. SPIE 13442, Fifth International Conference on Signal Processing and Computer Science (SPCS 2024), 1344220 (3 January 2025); https://doi.org/10.1117/12.3052979

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