Diagnosability Analysis of a Class of Hierarchical State Machines

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.