Please use this identifier to cite or link to this item: https://doi.org/10.1016/j.engappai.2010.01.026
Title: Multi-agent based collaborative fault detection and identification in chemical processes
Authors: Seng Ng, Y. 
Srinivasan, R. 
Keywords: Bayesian combination
Decision fusion
Fault diagnosis
Multi-agent system
Voting
Issue Date: Sep-2010
Citation: Seng Ng, Y., Srinivasan, R. (2010-09). Multi-agent based collaborative fault detection and identification in chemical processes. Engineering Applications of Artificial Intelligence 23 (6) : 934-949. ScholarBank@NUS Repository. https://doi.org/10.1016/j.engappai.2010.01.026
Abstract: Fault detection and identification (FDI) has received significant attention in literature. Popular methods for FDI include principal component analysis, neural-networks, and signal processing methods. However, each of these methods inherit certain strengths and shortcomings. A method that works well under one circumstance might not work well under another when different features of the underlying process come to the fore. In this paper, we show that a collaborative FDI approach that combines the strengths of various heterogeneous FDI methods is able to maximize diagnostic performance. A multi-agent framework is proposed to realize such collaboration in practice where different FDI methods, i.e: principal component analysis, self-organizing maps, non-parametric approaches, or neural-networks are combined. Since the results produced by different FDI agents might be in conflict, we use decision fusion methods to combine FDI results. Two different methods - voting-based fusion and Bayesian probability fusion are studied here. Most monitoring and fault diagnosis algorithms are computationally complex, but their results are often needed in real-time. One advantage of the multi-agent framework is that it provides an efficient means for speeding up the execution time of the various FDI methods through seamless deployment in a large-scale grid. The proposed multi-agent approach is illustrated through fault diagnosis of the startup of a lab-scale distillation unit and the Tennessee Eastman Challenge problem. Extensive testing of the proposed method shows that combining diagnostic classifiers of different types can significantly improve diagnostic performance. © 2010 Elsevier Ltd. All rights reserved.
Source Title: Engineering Applications of Artificial Intelligence
URI: http://scholarbank.nus.edu.sg/handle/10635/89509
ISSN: 09521976
DOI: 10.1016/j.engappai.2010.01.026
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