Please use this identifier to cite or link to this item: http://hdl.handle.net/10397/34729
Title: Model-based fault diagnosis in continuous dynamic systems
Authors: Lo, CH
Wong, YK 
Rad, AB
Keywords: Artificial intelligence
Genetic algorithms
Fault detection
Fault diagnosis
Qualitative bond graph
Issue Date: 2004
Publisher: Elsevier
Source: ISA transactions, 2004, v. 43, no. 3, p. 459-475 How to cite?
Journal: ISA transactions
Abstract: Traditional fault detection and isolation methods are based on quantitative models which are sometimes difficult and costly to obtain. In this paper, qualitative bond graph (QBG) reasoning is adopted as the modeling scheme to generate a set of qualitative equations. The QBG method provides a unified approach for modeling engineering systems, in particular, mechatronic systems. An input-output qualitative equation derived from QBG formalism performs continuous system monitoring. Fault diagnosis is activated when a discrepancy is observed between measured abnormal behavior and predicted system behavior. Genetic algorithms (GA's) are then used to search for possible faulty components among a system of qualitative equations. In order to demonstrate the performance of the proposed algorithm, we have tested it on a laboratory scale servo-tank liquid process rig. Results of the proposed model-based fault detection and diagnosis algorithm for the process rig are presented and discussed.
URI: http://hdl.handle.net/10397/34729
ISSN: 0019-0578
DOI: 10.1016/S0019-0578(07)60161-X
Appears in Collections:Journal/Magazine Article

Access
View full-text via PolyU eLinks SFX Query
Show full item record

WEB OF SCIENCETM
Citations

9
Last Week
0
Last month
Citations as of Feb 22, 2017

Page view(s)

13
Last Week
0
Last month
Checked on Feb 26, 2017

Google ScholarTM

Check

Altmetric



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