Quantitative hybrid bond graph-based fault detection and isolation

Chang Boon Low, Danwei Wang, Shai Arogeti, Ming Luo

Research output: Contribution to journalArticlepeer-review

69 Scopus citations

Abstract

This research result consists of two parts: one is general theory on causality assignment for hybrid bond graph (HBG) and another is application of this concept to the quantitative fault diagnosis. From Low et al., 2008, a foundation for quantitative bond graph-based fault detection and isolation (FDI) design using HBG is laid. Useful causality properties pertaining to the HBG from FDI perspectives, and the concept of diagnostic hybrid bond graph (DHBG) which is advantageous for efficient and effective FDI applications are proposed. This paper is a continuation of our previous paper (Low et al., 2008). Here, the DHBG is exploited to analyze the hybrid system's fault detectability and fault isolability. Additionally, a quantitative FDI framework for effective fault diagnosis for hybrid systems is proposed. Simulation and experimental results are presented to validate some key concepts of the quantitative hybrid bond graph-based FDI framework.

Original languageEnglish
Article number5204121
Pages (from-to)558-569
Number of pages12
JournalIEEE Transactions on Automation Science and Engineering
Volume7
Issue number3
DOIs
StatePublished - 1 Jul 2010

Keywords

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

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