Journal
CHEMOMETRICS AND INTELLIGENT LABORATORY SYSTEMS
Volume 155, Issue -, Pages 151-159Publisher
ELSEVIER SCIENCE BV
DOI: 10.1016/j.chemolab.2016.04.012
Keywords
Distributed monitoring; Large-scale nonlinear process; Bayesian fault diagnosis; Kernel principal component analysis
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Funding
- 973 Project of China [2013CB733600]
- National Natural Science Foundation of China [21176073, 61374126]
- Program for New Century Excellent Talents in University [NCET-09-0346]
- Fundamental Research Funds for the Central Universities
- Natural Science Foundation of Shandong Province [ZR2013FM021]
- Alexander von Humboldt Foundation
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Centralized monitoring generally involves all measured variables in one model. However, the existence of variables without beneficial information may cause redundancy in the monitoring and degrade monitoring performance. This paper proposes a performance-driven distributed monitoring scheme that incorporates kernel principal analysis (KPCA) and Bayesian diagnosis system for large-scale nonlinear processes. First, a stochastic optimization method is utilized to select a subset of variables that provide the best possible performance for each fault and to decompose the process into several sub-blocks. Second, a KPCA model is established in each block to deal with nonlinearity and generate fault signature evidence. Finally, a Bayesian fault diagnosis system is established to identify the fault status of the entire process. Considering the significant calculation amount in Bayesian diagnosis, optimal evidence source selection is performed to reduce the redundancy. Case studies on the Tennessee Eastman benchmark process and a continuous stirred tank reactor process demonstrate the efficiency of the proposed scheme. (C) 2016 Elsevier B.V. All rights reserved.
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