Multivariate process monitoring and fault identification using multiple decision tree classifiers

Abstract

Machine learning based algorithms, such as a decision tree (DT) classifier, have been applied to automated process monitoring and fault identification in manufacturing processes, however the current DT-based process control models employ a single DT classifier for both mean shift detection and fault identification. As many manufacturing processes use automated data collection for multiple process parameters, a DT classifier would have to handle a large number of classes. Previous research shows that a large number of classes can degrade the accuracy of a DT multiclass classifier. In this study we propose a new process monitoring model using multiple DT classifiers with each handling a small number of classes. Moreover, we not only detect mean shifts but also identify process variability levels that may cause out-of-control signals. Experimental results show that our proposed model achieves satisfactory performance in process monitoring and fault identification with various parameter settings. It achieves better ARL performance compared with the baseline method based on a single DT classifier.

Keywords:

• multivariate analysis
• process monitoring
• fault identification
• decision tree
• statistical process control

Acknowledgement

The authors would like to thank the editor and referees for their helpful comments that improved the final version of this paper significantly. This research was supported by Natural Science Foundation of China with Grants 71002105, 70931004, and 70802043.