A Change-Point Approach for Phase-I Analysis in Multivariate Profile Monitoring and Diagnosis. Issue 2 (2nd April 2016)

• Record Type: Journal Article
• Authors: Paynabar, Kamran; Zou, Changliang; Qiu, Peihua
• Is Part Of: Technometrics. Volume 58:Issue 2(2016)
• Page Start: 191
• Page End: 204
• Publication Date: 2016-04-02
• Subjects: Functional data analysis -- Functional principal component analysis -- Multichannel profiles -- Nonlinear profile -- Statistical process control; Statistical physics -- Periodicals; Chemistry -- Statistical methods -- Periodicals; Engineering -- Statistical methods -- Periodicals; 519.5
• DOI: 10.1080/00401706.2015.1042168
• Languages: English
• ISSNs: 0040-1706

Abstract

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.