Probabilistic contribution analysis for statistical process monitoring: A missing variable approach

Chen, T and Sun, Y (2009) Probabilistic contribution analysis for statistical process monitoring: A missing variable approach Control Engineering Practice, 17 (4). pp. 469-477.

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Abstract

Probabilistic models, including probabilistic principal component analysis (PPCA) and PPCA mixture models, have been successfully applied to statisticalprocess monitoring. This paper reviews these two models and discusses some implementation issues that provide alternative perspective on their application to process monitoring. Then aprobabilisticcontributionanalysis method, based on the concept of missingvariable, is proposed to facilitate the diagnosis of the source behind the detected process faults. The contributionanalysis technique is demonstrated through its application to both PPCA and PPCA mixture models for the monitoring of two industrial processes. The results suggest that the proposed method in conjunction with PPCA model can reduce the ambiguity with regard to identifying the processvariables that contribute to process faults. More importantly it provides a fault identification approach for PPCA mixture model where conventional contributionanalysis is not applicable.

Item Type:	Article
Divisions :	Faculty of Engineering and Physical Sciences > Chemical and Process Engineering
Authors :	Authors Email ORCID Chen, T UNSPECIFIED UNSPECIFIED Sun, Y UNSPECIFIED UNSPECIFIED
Date :	2009
Identification Number :	10.1016/j.conengprac.2008.09.005
Additional Information :	NOTICE: this is the author’s version of a work that was accepted for publication in Control Engineering Practice. Changes resulting from the publishing process, such as peer review, editing, corrections, structural formatting, and other quality control mechanisms may not be reflected in this document. Changes may have been made to this work since it was submitted for publication. A definitive version was subsequently published in Control Engineering Practice, 17 (4), April 2009, DOI 10.1016/j.conengprac.2008.09.005.
Depositing User :	Symplectic Elements
Date Deposited :	11 Jun 2012 16:06
Last Modified :	23 Sep 2013 19:28
URI:	http://epubs.surrey.ac.uk/id/eprint/534405

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