Journal
JOURNAL OF PROCESS CONTROL
Volume 44, Issue -, Pages 224-235Publisher
ELSEVIER SCI LTD
DOI: 10.1016/j.jprocont.2016.06.003
Keywords
Dynamic fault detection; Quality related; Linear dynamic systems; Data uncertainty; Supervised modeling
Funding
- National Natural Science Foundation of China (NSFC) [61273167]
- Project National 973 [2012CB720500]
- Alexander von Humboldt Foundation
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Dynamic and uncertainty are two main features of industrial processes data which should be paid attentions when carrying out process monitoring and fault diagnosis. As a typical dynamic Bayesian network model, linear dynamic system (LDS) can efficiently deal with both dynamic and uncertain features of the process data. However, the quality information has been ignored by the LDS model, which could serve as a supervised term for information extraction and fault detection. In this paper, a supervised form of the LDS model is developed, which can successfully incorporate the information of quality variables. With this additional data information, the new supervised LDS model can provide a quality related fault detection scheme for dynamic processes. A detailed industrial case study on the Tennessee Eastman benchmark process is carried out for performance evaluation of the developed method. (C) 2016 Elsevier Ltd. All rights reserved.
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