Asymmetric Diagnosability Analysis of Discrete-Event Systems

16 pages•Published: January 6, 2018

Abstract

The twin plant method is central in every research whose focus is checking the diagnosability of discrete-event systems (DESs). Although the property of diagnosability has been extended over time, and several proposals have been advanced to perform a distributed analysis, diagnosability checking still relies on the exploitation of the twin plant method. However, the twin plant structure is redundant, which is a drawback, above all if the considered DES observation is uncertain: in such a case, several distinct twin plants have to be built in order to check the diagnosability for increasing levels of uncertainty. A higher uncertainty level requires a twin plant of larger size. The paper first gives some preliminary thoughts to the reduction of the twin plant size. Next, on the ground that no contribution in the literature has altered the original state-based representation of the twin plant, the paper shows how to transform such a representation into a transition-based one. Finally, it reports some investigations aimed at reducing the effort needed to produce each twin plant: a twin plant inherent to a higher uncertainty level can be produced by incrementing the twin plant relevant to the lower level.

Keyphrases: diagnosability, diagnosability analysis, discrete event systems, twin plant, uncertain observations

In: Marina Zanella, Ingo Pill and Alessandro Cimatti (editors). 28th International Workshop on Principles of Diagnosis (DX'17), vol 4, pages 78-93.

Links:	https://easychair.org/publications/paper/7kVk
	https://doi.org/10.29007/6lc2