Decentralized Fault Diagnosis Approach without a Global Model for Fault Diagnosis of Discrete Event Systems

Moamar Sayed-Mouchaweh, Edwin Lughofer

Research output: Contribution to journalArticlepeer-review

Abstract

Diagnosability property ensures that a predefined set of faults are diagnosable by a centralized diagnoser built using a global model of the system, while co-diagnosability guarantees that these faults are diagnosed in decentralized manner using a set of local diagnosers. A fault must be diagnosed by at least one local diagnoser by using its proper local observation of the system. The aim of using decentralized diagnosis approaches is to overcome the space complexity and weak robustness of centralized diagnosis approaches while at the same time preserving the diagnostic capability of a centralized diagnosis. However, co-diagnosability property is stronger than diagnosability property. If a system is co-diagnosable, then it is diagnosable, while a diagnosable system does not ensure that it is co-diagnosable. Therefore, the challenge of decentralized diagnosis approaches is to perform local diagnosis and to verify that it is equivalent to the centralized one without the need for a global model. In this paper, an approach is proposed to obtain co-diagnosable decentralized diagnosis structure of discrete event systems without the use of a global model. This approach is based on the synchronization of local diagnosis decisions in order to solve the ambiguity between local diagnosers. This synchronization allows obtaining local diagnosis equivalent to the global one without the use of a global model.
Original languageEnglish
Pages (from-to)2228-2241
Number of pages14
JournalInternational Journal of Control
Volume88
Issue number11
DOIs
Publication statusPublished - 02 Nov 2015

Fields of science

  • 101 Mathematics
  • 101013 Mathematical logic
  • 101024 Probability theory
  • 102001 Artificial intelligence
  • 102003 Image processing
  • 102019 Machine learning
  • 603109 Logic
  • 202027 Mechatronics

JKU Focus areas

  • Computation in Informatics and Mathematics
  • Mechatronics and Information Processing
  • Nano-, Bio- and Polymer-Systems: From Structure to Function

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