Model-based fault diagnosis in continuous dynamic systems

C. H. Lo, Y. K. Wong, A. B. Rad

Research output: Contribution to journalArticlepeer-review

10 Citations (Scopus)


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.

Original languageEnglish
Pages (from-to)459-475
Number of pages17
JournalISA Transactions
Issue number3
Publication statusPublished - Jul 2004


  • Artificial intelligence
  • Fault detection
  • Fault diagnosis
  • Genetic algorithms
  • Qualitative bond graph


Dive into the research topics of 'Model-based fault diagnosis in continuous dynamic systems'. Together they form a unique fingerprint.

Cite this