Causality assignment and model approximation for hybrid bond graph: Fault diagnosis perspectives

Chang Boon Low, Danwei Wang, Shai Arogeti, Jing Bing Zhang

Research output: Contribution to journalArticlepeer-review

75 Scopus citations

Abstract

Bond graph (BG) is an effective tool for modeling complex systems and it has been proven useful for fault detection and isolation (FDI) for continuous systems. BG provides the causal relations between system's variables which allow FDI algorithms to be developed systematically from the graph. In the same spirit, Hybrid bond graph (HBG) is a BG-based modeling approach which provides an avenue to model complex hybrid systems. However, due to mode-varying causality properties of HBG, HBG has not been efficiently-exploited for fault diagnosis. In this work, a comprehensive study on the HBG from FDI viewpoints is presented. Some properties pertaining to the HBG are gained in the study. Based on these findings, a causality assignment procedure and a model approximation technique are developed to achieve a HBG with a desirable causality assignment that leads a unified description of system's behavior. These results lay a foundation for quantitative FDI design for complex hybrid systems.

Original languageEnglish
Article number5262981
Pages (from-to)570-580
Number of pages11
JournalIEEE Transactions on Automation Science and Engineering
Volume7
Issue number3
DOIs
StatePublished - 1 Jul 2010

Keywords

  • Causality assignment
  • fault diagnosis perspective
  • hybrid bond graph (HBG)
  • hybrid systems

ASJC Scopus subject areas

  • Control and Systems Engineering
  • Electrical and Electronic Engineering

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