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

Abstract

AbstractIn 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.