Skip to main content
padlock icon - secure page this page is secure

Diagnosing PN-based models with partial observable transitions

Buy Article:

$61.00 + tax (Refund Policy)

Failure diagnosis back-traces failures based on an observed system behaviour when a failure occurs. Ushio et al . (1998) first studied the diagnosability of Petri net (PN) models, a common mathematical modelling structure for CIM systems. With transitions all assumed to be unobservable, the diagnosis process of Ushio et al . relies solely on marking changes at observable places, making diagnosability analysis of most non-trivial PN non-diagnosable. However, in the context of manufacturing process, the initiation of a command and the response of a process are readily available to supervisory controller. This paper, in contrast, assumes that part of the transitions of the PN modelling is observable. Under this assumption, the first contribution of this paper is to investigate how both the label propagation function and the range function, used to construct a diagnoser , are to be revised in order to take advantage of the newly available information provided by observable transitions. The second contribution is to present a procedure to construct, for PN modelling, the associated verifier , first proposed by Yoo and Lafortune (2001) as a polynomial check mechanism on diagnosability but for finite state automata models. As shown by examples, the additional information from observed transitions in general adds diagnosability to the analysed system.
No Reference information available - sign in for access.
No Citation information available - sign in for access.
No Supplementary Data.
No Article Media
No Metrics

Keywords: Diagnosability; Petri net

Document Type: Research Article

Publication date: March 1, 2005

More about this publication?
  • Access Key
  • Free content
  • Partial Free content
  • New content
  • Open access content
  • Partial Open access content
  • Subscribed content
  • Partial Subscribed content
  • Free trial content
Cookie Policy
X
Cookie Policy
Ingenta Connect website makes use of cookies so as to keep track of data that you have filled in. I am Happy with this Find out more