Please use this identifier to cite or link to this item: https://doi.org/10.1016/j.engappai.2010.01.026
DC FieldValue
dc.titleMulti-agent based collaborative fault detection and identification in chemical processes
dc.contributor.authorSeng Ng, Y.
dc.contributor.authorSrinivasan, R.
dc.date.accessioned2014-10-09T06:54:34Z
dc.date.available2014-10-09T06:54:34Z
dc.date.issued2010-09
dc.identifier.citationSeng 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
dc.identifier.issn09521976
dc.identifier.urihttp://scholarbank.nus.edu.sg/handle/10635/89509
dc.description.abstractFault 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.
dc.description.urihttp://libproxy1.nus.edu.sg/login?url=http://dx.doi.org/10.1016/j.engappai.2010.01.026
dc.sourceScopus
dc.subjectBayesian combination
dc.subjectDecision fusion
dc.subjectFault diagnosis
dc.subjectMulti-agent system
dc.subjectVoting
dc.typeArticle
dc.contributor.departmentCHEMICAL & BIOMOLECULAR ENGINEERING
dc.description.doi10.1016/j.engappai.2010.01.026
dc.description.sourcetitleEngineering Applications of Artificial Intelligence
dc.description.volume23
dc.description.issue6
dc.description.page934-949
dc.description.codenEAAIE
dc.identifier.isiut000280258100008
Appears in Collections:Staff Publications

Show simple item record
Files in This Item:
There are no files associated with this item.

SCOPUSTM   
Citations

54
checked on Jan 30, 2023

WEB OF SCIENCETM
Citations

40
checked on Jan 30, 2023

Page view(s)

160
checked on Feb 2, 2023

Google ScholarTM

Check

Altmetric


Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.