Diagnosability of Discrete Event Systems with Modular Structure

The diagnosis of unobservable faults in large and complex discrete event systems modeled by parallel composition of automata is considered. A modular approach is developed for diagnosing such systems. The notion of modular diagnosability is introduced and the corresponding necessary and sufficient conditions to ensure it are presented. The verification of modular diagnosability is performed by a new algorithm that incrementally exploits the modular structure of the system to save on computational effort. The correctness of the algorithm is proved. Online diagnosis of modularly diagnosable systems is achieved using only local diagnosers.Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/45105/1/10626_2006_Article_6177.pd

Diagnosability Verification Using Compositional Branching Bisimulation

This paper presents an efficient diagnosability verification technique, based on a general abstraction approach. More specifically, branching bisimulation including state labels with explicit divergence (BBSD) is defined. This bisimulation preserves the temporal logic property that verifies diagnosability. Based on a proposed BBSD algorithm, compositional abstraction for modular diagnosability verification is shown to offer a significant state space reduction in comparison to state-of-the-art techniques. This is illustrated by verifying non-diagnosability analytically for a set of synchronized components, where the abstracted solution is independent of the number of components and the number of observable events

Verification of diagnosability based on compositional branching bisimulation

This paper presents an efficient diagnosability verification technique, based on a general abstraction approach. We exploit branching bisimulation with explicit divergence (BBED), which preserves the temporal logic property that verifies diagnosability. Furthermore, using compositional abstraction for modular diagnosability verification offers additional state space reduction in comparison to the state-of-the-art techniques

INCREMENTAL FAULT DIAGNOSABILITY AND SECURITY/PRIVACY VERIFICATION

Dynamical systems can be classified into two groups. One group is continuoustime systems that describe the physical system behavior, and therefore are typically modeled by differential equations. The other group is discrete event systems (DES)s that represent the sequential and logical behavior of a system. DESs are therefore modeled by discrete state/event models.DESs are widely used for formal verification and enforcement of desired behaviors in embedded systems. Such systems are naturally prone to faults, and the knowledge about each single fault is crucial from safety and economical point of view. Fault diagnosability verification, which is the ability to deduce about the occurrence of all failures, is one of the problems that is investigated in this thesis. Another verification problem that is addressed in this thesis is security/privacy. The two notions currentstate opacity and current-state anonymity that lie within this category, have attracted great attention in recent years, due to the progress of communication networks and mobile devices.Usually, DESs are modular and consist of interacting subsystems. The interaction is achieved by means of synchronous composition of these components. This synchronization results in large monolithic models of the total DES. Also, the complex computations, related to each specific verification problem, add even more computational complexity, resulting in the well-known state-space explosion problem.To circumvent the state-space explosion problem, one efficient approach is to exploit the modular structure of systems and apply incremental abstraction. In this thesis, a unified abstraction method that preserves temporal logic properties and possible silent loops is presented. The abstraction method is incrementally applied on the local subsystems, and it is proved that this abstraction preserves the main characteristics of the system that needs to be verified.The existence of shared unobservable events means that ordinary incremental abstraction does not work for security/privacy verification of modular DESs. To solve this problem, a combined incremental abstraction and observer generation is proposed and analyzed. Evaluations show the great impact of the proposed incremental abstraction on diagnosability and security/privacy verification, as well as verification of generic safety and liveness properties. Thus, this incremental strategy makes formal verification of large complex systems feasible

Diagnosability of Fuzzy Discrete Event Systems

In order to more effectively cope with the real-world problems of vagueness, {\it fuzzy discrete event systems} (FDESs) were proposed recently, and the supervisory control theory of FDESs was developed. In view of the importance of failure diagnosis, in this paper, we present an approach of the failure diagnosis in the framework of FDESs. More specifically: (1) We formalize the definition of diagnosability for FDESs, in which the observable set and failure set of events are {\it fuzzy}, that is, each event has certain degree to be observable and unobservable, and, also, each event may possess different possibility of failure occurring. (2) Through the construction of observability-based diagnosers of FDESs, we investigate its some basic properties. In particular, we present a necessary and sufficient condition for diagnosability of FDESs. (3) Some examples serving to illuminate the applications of the diagnosability of FDESs are described. To conclude, some related issues are raised for further consideration.Comment: 14 pages; revisions have been mad