Please use this identifier to cite or link to this item: https://doi.org/10.1016/j.compchemeng.2010.05.004
Title: Evaluation of decision fusion strategies for effective collaboration among heterogeneous fault diagnostic methods
Authors: Ghosh, K.
Ng, Y.S.
Srinivasan, R. 
Keywords: Bayesian probability
Classifier
Process monitoring
Supervision
Issue Date: 9-Feb-2011
Source: Ghosh, K., Ng, Y.S., Srinivasan, R. (2011-02-09). Evaluation of decision fusion strategies for effective collaboration among heterogeneous fault diagnostic methods. Computers and Chemical Engineering 35 (2) : 342-355. ScholarBank@NUS Repository. https://doi.org/10.1016/j.compchemeng.2010.05.004
Abstract: Numerous methodologies for fault detection and identification (FDI) in chemical processes have been proposed in literature. However, it is extremely difficult to design a perfect FDI method to efficiently monitor an industrial-scale process. In this work, we seek to overcome this difficulty by using multiple heterogeneous FDI methods and fusing their results so that the strengths of the individual FDI methods are combined and their shortcomings overcome. Several decision fusion strategies can be used for this purpose. In this paper, we study the relative benefits of utility-based and evidence-based decision fusion strategies. Our results from a lab-scale distillation column and the popular Tennessee Eastman challenge problem show that in situations where no single FDI method offers adequate performance, evidence-based fusion strategies such as weighted voting, Bayesian, and Dempster-Shafer based fusion can provide (i) complete fault coverage, (ii) more than 40% increase in overall fault recognition rate, (iii) significant improvement in monitoring performance, and (iv) reduction in fault detection and diagnosis delays. © 2010 Elsevier Ltd.
Source Title: Computers and Chemical Engineering
URI: http://scholarbank.nus.edu.sg/handle/10635/63873
ISSN: 00981354
DOI: 10.1016/j.compchemeng.2010.05.004
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