A Change-Point Approach for Phase-I Analysis in Multivariate Profile Monitoring and Diagnosis

Abstract

Process monitoring and fault diagnosis using profile data remains an important and challenging problem in statistical process control (SPC). Although the analysis of profile data has been extensively studied in the SPC literature, the challenges associated with monitoring and diagnosis of multichannel (multiple) nonlinear profiles are yet to be addressed. Motivated by an application in multioperation forging processes, we propose a new modeling, monitoring, and diagnosis framework for phase-I analysis of multichannel profiles. The proposed framework is developed under the assumption that different profile channels have similar structure so that we can gain strength by borrowing information from all channels. The multidimensional functional principal component analysis is incorporated into change-point models to construct monitoring statistics. Simulation results show that the proposed approach has good performance in identifying change-points in various situations compared with some existing methods. The codes for implementing the proposed procedure are available in the supplementary material.

• Functional data analysis
• Functional principal component analysis
• Multichannel profiles
• Nonlinear profile
• Statistical process control

ACKNOWLEDGMENTS

The authors thank the editor, associate editor, and two anonymous referees for their many helpful comments that have resulted in significant improvements in the article. The work of Paynabar was supported by NSF Grant CMMI-1451088. Qiu's research was supported by NSF Grant DMS-1405698. Zou's research was supported by the NNSF of China Grants 11431006, 11131002, 11371202 and the Foundation for the Author of National Excellent Doctoral Dissertation of China 201232. Zou is the corresponding author.