A twin-plant based approach for diagnosability analysis of intermittent failures

Boussif, Abderraouf; Liu, Baisi; Ghazel, Mohamed

doi:10.1109/wodes.2016.7497854

Cited by 7 publications

(8 citation statements)

References 16 publications

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“…The reset events in such a context are called normalization events and, thus, the change from faulty to normal status is called normalization. Such a modeling strategy has been used in Biswas (2012), Jiang et al (2003), Boussif et al (2016) and Boussif and Ghazel (2019).…”

Section: Normalization Settingmentioning

confidence: 99%

“…Then, some restrictive versions of these definitions, which characterize the ability to identify the status of the system after either the occurrence of an intermittent fault or its recovery, will be discussed; they are usually referred to as "strong diagnosability". All of these notions were firstly introduced by Contant et al (2004), Contant (2005), and Contant et al (2002), and then revisited in Boussif and Ghazel (2016a), Boussif andGhazel (2017), andCarvalho et al (2017), in an event-based scheme within a recovery setting, and in Biswas ( 2012), Boussif et al (2016) and Boussif and Ghazel (2019), in a state-based scheme within a normalization setting.…”

Section: Fault Detection-based Definitions Of Intermittent Fault Diagnosabilitymentioning

confidence: 99%

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Intermittent fault diagnosability of discrete event systems: an overview of automaton-based approaches

Boussif

Ghazel²,

Basilio

2020

Discrete Event Dyn Syst

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Real life experience has shown that intermittent faults are among the most challenging kinds of faults to detect and isolate, being present in the majority of production systems. Such a concern has made intermittent fault an active area of research in both discrete event and continuous-variable dynamic systems. In this paper, we present a review of the state-of-the art of intermittent fault diagnosis of discrete event systems modeled by finite state automata. To this end, we revisit the main definitions of diagnosability of intermittent faults, and present comparisons between them, consider verification and analysis techniques, and discuss available complexity results. Examples are used throughout the paper to illustrate the reviewed concepts and verification algorithms. We also look ahead, by suggesting some perspectives for future research.

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Section: Normalization Settingmentioning

confidence: 99%

Section: Fault Detection-based Definitions Of Intermittent Fault Diagnosabilitymentioning

confidence: 99%

Intermittent fault diagnosability of discrete event systems: an overview of automaton-based approaches

Boussif

Ghazel²,

Basilio

2020

Discrete Event Dyn Syst

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“…DES is usually modelled as a Finite state machine(FSM) (Refs. [1,2]) or a Petri-net (Refs. [3][4][5]).…”

Section: Introductionmentioning

confidence: 99%

“…Some researchers separated the global model into components to reduce the number of states (Refs. [1,13,14]). Some researchers proposed different kinds of models to diagnose the systems (Refs.…”

Section: Introductionmentioning

confidence: 99%

Verifying Diagnosability of Discrete Event System with Logical Formula

2020

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“…Moreover, while both the original notion of DES diagnosability and the traditional twin plant method assume that faults are permanent, a new definition of diagnosability relevant to intermittent faults and a consequent adaptation of the twin plant method can be found in [9]. Another research line is aimed at performing distributed diagnosability analysis [10,11], based on a distributed modeling of the considered DES.…”

Section: Introductionmentioning

confidence: 99%

Asymmetric Diagnosability Analysis of Discrete-Event Systems

Zanella¹

Kalpa Publications in Computing

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The twin plant method is central in every research whose focus is checking the diagnosability of discrete-event systems (DESs). Although the property of diagnosability has been extended over time, and several proposals have been advanced to perform a distributed analysis, diagnosability checking still relies on the exploitation of the twin plant method. However, the twin plant structure is redundant, which is a drawback, above all if the considered DES observation is uncertain: in such a case, several distinct twin plants have to be built in order to check the diagnosability for increasing levels of uncertainty. A higher uncertainty level requires a twin plant of larger size. The paper first gives some preliminary thoughts to the reduction of the twin plant size. Next, on the ground that no contribution in the literature has altered the original state-based representation of the twin plant, the paper shows how to transform such a representation into a transition-based one. Finally, it reports some investigations aimed at reducing the effort needed to produce each twin plant: a twin plant inherent to a higher uncertainty level can be produced by incrementing the twin plant relevant to the lower level.

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A twin-plant based approach for diagnosability analysis of intermittent failures

Cited by 7 publications

References 16 publications

Intermittent fault diagnosability of discrete event systems: an overview of automaton-based approaches

Intermittent fault diagnosability of discrete event systems: an overview of automaton-based approaches

Verifying Diagnosability of Discrete Event System with Logical Formula

Asymmetric Diagnosability Analysis of Discrete-Event Systems

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