Diagnosability Analysis of a Class of Hierarchical State Machines

Authors:
Andrea Paoli;Stéphane Lafortune
Affiliations:
Center for Research on Complex Automated Systems (CASY) "Giuseppe Evangelisti" DEIS--Department of Electronic, Computer Science and Systems, University of Bologna, Bologna, Italy 40136;Department of Electrical Engineering and Computer Science, The University of Michigan, Ann Arbor, USA 48109-2122
Venue:
Discrete Event Dynamic Systems
Year:
2008

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Abstract

This paper addresses the problem of fault detection and isolation for a particular class of discrete event dynamical systems called hierarchical finite state machines (HFSMs). A new version of the property of diagnosability for discrete event systems tailored to HFSMs is introduced. This notion, called L1-diagnosability, captures the possibility of detecting an unobservable fault event using only high level observations of the behavior of an HFSM. Algorithms for testing L1-diagnosability are presented. In addition, new methodologies are presented for studying the diagnosability properties of HFSMs that are not L1-diagnosable. These methodologies avoid the complete expansion of an HFSM into its corresponding flat automaton by focusing the expansion on problematic indeterminate cycles only in the associated extended diagnoser.