Article ID | Journal | Published Year | Pages | File Type |
---|---|---|---|---|
697455 | Automatica | 2011 | 8 Pages |
Abstract
Conventional Bayesian methods commonly assume that the evidences are temporally independent. This condition, however, does not hold for most engineering problems. With the evidence transition information being considered, the temporal information can be synthesized within the Bayesian framework to improve diagnosis performance. In this paper, the important evidence dependency problem is solved by a data-driven Bayesian approach with consideration of evidence transition probability. The applications in a simulated distillation column and a pilot scale process are presented to demonstrate the data dependency handling ability of the proposed control loop performance diagnosis approach.
Related Topics
Physical Sciences and Engineering
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Control and Systems Engineering
Authors
Fei Qi, Biao Huang,