Detecting and diagnosing covariance matrix changes in multistage processes

Yanting Li and Fugee Tsung

IISE Transactions, 2011, vol. 43, issue 4, 259-274

Abstract: Multistage process monitoring and fault identification are currently receiving considerable attention. This article focuses on detecting common faults in a multistage process that affect the process covariance matrix. The process covariance matrix monitoring problem is formulated into a multiple hypotheses testing problem. The proposed method is an exponentially weighted moving average chart built on vectors that are transformed from sample covariance matrices of the collected observations. Extensive simulation analysis shows that, compared to alternative methods for multistage process covariance monitoring and diagnosis, the proposed method is capable of not only detecting variation changes quicker but also identifying faults with higher accuracy.

Date: 2011
References: Add references at CitEc
Citations:

Downloads: (external link)
http://hdl.handle.net/10.1080/0740817X.2010.521805 (text/html)
Access to full text is restricted to subscribers.

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:taf:uiiexx:v:43:y:2011:i:4:p:259-274

Ordering information: This journal article can be ordered from
http://www.tandfonline.com/pricing/journal/uiie20

DOI: 10.1080/0740817X.2010.521805

Access Statistics for this article

IISE Transactions is currently edited by Jianjun Shi

More articles in IISE Transactions from Taylor & Francis Journals
Bibliographic data for series maintained by Chris Longhurst ().