Diagnosability analysis and fault diagnosis of P-time labelled Petri nets
by Patrice Bonhomme
International Journal of Critical Computer-Based Systems (IJCCBS), Vol. 8, No. 1, 2018

Abstract: This paper addresses the problem of analysing the diagnosability of a P-time labelled Petri net with partial information. Indeed, the set of transitions is partitioned into those labelled with the empty string ε called silent (as their firing cannot be detected) including the faulty transitions and the observable ones. The diagnosability can be defined as the ability to detect the type of a failure within a finite number of steps after its occurrence - the system is then said to be diagnosable. The proposed approach is based on the synthesis of a modified state observer where the fault transitions are considered as observable allowing the construction of a sampath-like diagnoser. The novelty of the developed approach resides in the fact that, although the time factor is considered as intervals, the diagnoser is computed thanks to the underlying untimed Petri net structure of the P-time labelled model considered. Furthermore, the method relies on linear programming techniques and the schedulability analysis of particular firing sequences exhibited by the analysis of the obtained diagnoser and does not require the building of the state class graph.

Online publication date: Fri, 18-May-2018

The full text of this article is only available to individual subscribers or to users at subscribing institutions.

 
Existing subscribers:
Go to Inderscience Online Journals to access the Full Text of this article.

Pay per view:
If you are not a subscriber and you just want to read the full contents of this article, buy online access here.

Complimentary Subscribers, Editors or Members of the Editorial Board of the International Journal of Critical Computer-Based Systems (IJCCBS):
Login with your Inderscience username and password:

Username:        Password:          Forgotten your password?

Want to subscribe?
A subscription gives you complete access to all articles in the current issue, as well as to all articles in the previous three years (where applicable). See our Orders page to subscribe.

If you still need assistance, please email subs@inderscience.com