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Reconstruction-based multivariate contribution analysis for fault isolation: A branch and bound approach

Lookup NU author(s): Dr Jie ZhangORCiD



Identification of faulty variables is an important component of multivariate statistical process monitoring (MSPM); it provides crucial information for further analysis of the root cause of the detected fault. The main challenge is the large number of combinations of process variables under consideration, usually resulting in a combinatorial optimization problem. This paper develops a generic reconstruction based multivariate contribution analysis (RBMCA) framework to identify the variables that are the most responsible for the fault. A branch and bound (BAB) algorithm is proposed to efficiently solve the combinatorial optimization problem. The formulation of the RBMCA does not depend on a specific model, which allows it lobe applicable to any MSPM model. We demonstrate the application of the RBMCA to a specific model: the mixture of probabilistic principal component analysis (PPCA mixture) model. Finally, we illustrate the effectiveness and computational efficiency of the proposed methodology through a numerical example and the benchmark simulation of the Tennessee Eastman process. (C) 2012 Elsevier Ltd. All rights reserved.

Publication metadata

Author(s): He B, Yang XH, Chen T, Zhang J

Publication type: Article

Publication status: Published

Journal: Journal of Process Control

Year: 2012

Volume: 22

Issue: 7

Pages: 1228-1236

Print publication date: 01/08/2012

Date deposited: 05/06/2014

ISSN (print): 0959-1524

ISSN (electronic): 1873-2771

Publisher: Elsevier Ltd


DOI: 10.1016/j.jprocont.2012.05.010


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