Causal Temporal Signature from diagnoser model for online diagnosis of Discrete Event Systems

Abstract

In Discrete Events System (DES), there are two basic approaches to diagnosis: the first approach is the diagnosers and the second approach is Causal Temporal Signature (CTS) and chronicles. The first approach has limitations including the issue of combinatorial explosion. On the other side, it offers tools to study the diagnosability of the models constructed. CTS are easier to write but pose the problem of the guarantee of the completeness of a given base. This means that there is at least one CTS in the set of CTS for all the faults in the monitored system. This study aims to propose a method to garantee the completeness of a set of CTS. The method is based on a translation of formalism and model of a diagnoser into CTS.From these CTS, a recognition algorithm based on the concept of "world" is used. A "world" is defined as a set of coherent hypotheses of assignment of the event received by the diagnostic task.

Machine learning-based approach for online fault Diagnosis of Discrete Event System

“…They are efficient and able to validate the consistency and completeness of the faults to be diagnosed. However, to work properly, these approaches require accurate and deep analytical models of the domain and the major difficulty is the high cost of implementing the models (Saddem and Philippot, 2014), (De Souza et al, 2020), (Moreira and Lesage, 2019). Indeed, the temporal complexity of implementing most models is exponential.…”

Failure detection and localization for timed event graphs in $(\max \limits ,+)$-algebra

“…The first attempts to deal with time in an event-based system is to model observable time patterns that are relevant to symptomatic abnormal behaviours without a complete model of the underlying system to supervise. Among these pattern-based techniques, there are temporal causal signatures [24], [20] that directly map faults to observable effects defined as timed event patterns. A closer formalism is also the one of chronicles [9] that provides an effective and automated way to recognize such patterns on-line from an observable flow of timed events.…”

CBR-ODAF: A Case-Based Reasoning for the Online Diagnosis of All internal Faults in Automated Production Systems

“…We chose to use CTS for the following reasons: (a) it is a formalism that is expressive enough since it is able to formally describe all the desired behaviors of the system to be diagnosed. This is done thanks to the temporal constraints and conjunction operators [8]; (b) CTS is understandable and readable by human supervisory operators to facilitate rules inspection, correlations discovery and relationships between them and (c) it has high efficiency in knowledge view of the symptoms of faults diagnosis. However, among the difficulties to overcome for the diagnosis using this formalism, we find the acquisition and the updating of a representative and rich knowledge base (that is to say a database of CTS) [8].…”

“…This is done thanks to the temporal constraints and conjunction operators [8]; (b) CTS is understandable and readable by human supervisory operators to facilitate rules inspection, correlations discovery and relationships between them and (c) it has high efficiency in knowledge view of the symptoms of faults diagnosis. However, among the difficulties to overcome for the diagnosis using this formalism, we find the acquisition and the updating of a representative and rich knowledge base (that is to say a database of CTS) [8]. Indeed, a poor and unrepresentative base can lead to mediocre diagnosis system [3].…”