Journal
JOURNAL OF PROCESS CONTROL
Volume 28, Issue -, Pages 27-39Publisher
ELSEVIER SCI LTD
DOI: 10.1016/j.jprocont.2015.02.004
Keywords
Fault diagnosis; Feature extraction; Feature representation; Multiple faults; Unknown faults; Process monitoring; Chemometrics; Causal map; Multivariate statistics
Funding
- International Paper
- National Center for Supercomputing Applications
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Feature extraction is crucial for fault diagnosis and the use of complementary features allows for improved diagnostic performance. Most of the existing fault diagnosis methods only utilize data-driven and causal connectivity-based features of faults, whereas the important complementary feature of the propagation paths of faults is not incorporated. The propagation path-based feature is important to represent the intrinsic properties of faults and plays a significant role in fault diagnosis, particularly for the diagnosis of multiple and unknown faults. In this article, a three-step framework based on the modified distance (DI) and modified causal dependency (CD) is proposed to integrate the data-driven and causal connectivity-based features with the propagation path-based feature for diagnosing known, unknown, and multiple faults. The effectiveness of the proposed approach is demonstrated on the Tennessee Eastman process. (C) 2015 Elsevier Ltd. All rights reserved.
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