Diagnoser design strategy for discrete event system: Case study of neutralization system

Abstract

In fault diagnosis, we study model-based approaches to diagnose the discrete event system that efficiently represents the determined ambiguity, unpredictability, and the observation and judgment of several real-life problems. Diagnosability is a crucial task for system reliability. This article presents a strategy for verifying the diagnosability of discrete event systems (DESs) using conjunctive normal form (CNF). This strategy introduces CNF-based finite state machine (FSM). First, the scheme considers the model of the system for diagnosis, and CNF represents all transitions of the DES. CNF-based FSM constructs a diagnoser known as CNF-based diagnoser. Diagnoser tests whether faulty events can be detected or not in a given system model, that is, DES. The diagnoser verifies the diagnosability of the given DES-based FSM using the resolve rule. The construction of diagnoser and diagnosability verification with respect to a real-world industrial system is illustrated. The complexity of the diagnoser construction and diagnosability verification are shown to be efficient.