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Journal of Control Science and Engineering
Volume 2017 (2017), Article ID 9517385, 10 pages
Research Article

A Bayesian Approach to Control Loop Performance Diagnosis Incorporating Background Knowledge of Response Information

Department of Automation, Xiamen University, Xiamen 361005, China

Correspondence should be addressed to Sun Zhou

Received 20 June 2017; Accepted 3 August 2017; Published 28 September 2017

Academic Editor: Chunhui Zhao

Copyright © 2017 Sun Zhou and Yiming Wang. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.


To isolate the problem source degrading the control loop performance, this work focuses on how to incorporate background knowledge into Bayesian inference. In an effort to reduce dependence on the amount of historical data available, we consider a general kind of background knowledge which appears in many applications. The knowledge, known as response information, is about what faults can possibly affect each of the monitors. We show how this knowledge can be translated to constraints on the underlying probability distributions and introduced in the Bayesian diagnosis. In this way, the dimensionality of the observation space is reduced and thus the diagnosis can be more reliable. Furthermore, for the judgments to be consistent, the set of posterior probabilities of each possible abnormality that are computed from different observation subspaces is synthesized to obtain the partially ordered posteriors. The eigenvalue formulation is used on the pairwise comparison matrix. The proposed approach is applied to a diagnosis problem on an oil sand solids handling system, where it is shown how the combination of background knowledge and data enhances the control performance diagnosis even when the abnormality data are sparse in the historical database.