Simultaneous fault and mode switching identification for hybrid systems based on particle swarm optimization

Ming Yu, Ming Luo, Danwei Wang, Shai Arogeti, Xinzheng Zhang

Research output: Contribution to journalArticlepeer-review

16 Scopus citations


This paper describes a methodology for simultaneous identification of fault parameters and mode switching events for hybrid systems. The method is developed based on the notion of Global Analytical Redundancy Relations (GARRs) from the bond graph model of the hybrid system. A unified formula with mode change time sequence and initial mode coefficients (IMC) is derived to represent the mode switching. Due to the discontinuous characteristic of the mode switching, an adaptive hybrid particle swarm optimization (AHPSO) employing the combination of real valued PSO (RPSO) and binary valued PSO (BPSO) is proposed to optimize different parts of solution simultaneously, a novel individual level adaptive method using fuzzy system is developed to dynamically adjust the algorithm parameters. GARRs are used as a fitness index of the AHPSO. Case studies of different energy domains are carried out to illustrate the efficiency of the proposed algorithm. Crown

Original languageEnglish
Pages (from-to)3000-3012
Number of pages13
JournalExpert Systems with Applications
Issue number4
StatePublished - 1 Apr 2010
Externally publishedYes


  • Bond graph
  • Fault parameter
  • Global analytical redundancy relation
  • Hybrid system
  • Mode switching time stamps
  • Particle swarm optimization

ASJC Scopus subject areas

  • General Engineering
  • Computer Science Applications
  • Artificial Intelligence


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