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

Boussif, Abderraouf; Ghazel, Mohamed; Basilio, João Carlos

doi:10.1007/s10626-020-00324-y

Cited by 22 publications

(13 citation statements)

References 99 publications

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“…Some referenced papers also include the identification of further fault characteristics like impact, location, or time of occurrence and the actions taken to avoid further damage in the process of fault detection [ 6 , 7 , 29 ], whereas others state that this is a specific part of fault isolation [ 28 ]. In this paper, we consider methods that detect whether a fault occurred or not, despite a possible subdivision into the fault categories [ 20 ]: abrupt (e.g., [ 30 ]), incipient (e.g., [ 31 ]), or intermittent (e.g., [ 32 ]). Methods of fault classification will handle any further identification.…”

Section: Methodsmentioning

confidence: 99%

Fault Handling in Industry 4.0: Definition, Process and Applications

Webert

Döß

Kaupp

et al. 2022

Sensors

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The increase of productivity and decrease of production loss is an important goal for modern industry to stay economically competitive. For that, efficient fault management and quick amendment of faults in production lines are needed. The prioritization of faults accelerates the fault amendment process but depends on preceding fault detection and classification. Data-driven methods can support fault management. The increasing usage of sensors to monitor machine health status in production lines leads to large amounts of data and high complexity. Machine Learning methods exploit this data to support fault management. This paper reviews literature that presents methods for several steps of fault management and provides an overview of requirements for fault handling and methods for fault detection, fault classification, and fault prioritization, as well as their prerequisites. The paper shows that fault prioritization lacks research about available learning methods and underlines that expert opinions are needed.

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Section: Methodsmentioning

confidence: 99%

Fault Handling in Industry 4.0: Definition, Process and Applications

Webert

Döß

Kaupp

et al. 2022

Sensors

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“…In the context of DES, robust fault diagnosis for systems subject to unreliable sensors (or subject to sensor failures) has been studied by many researchers. The reader is referred to the very recent comprehensive survey papers [8], [12] for more details on this topic. For example, in [10], the authors investigated the verification of diagnosability for systems subject to intermittent sensor failures.…”

Section: B Literature Review On Diagnosis With Unreliable Sensorsmentioning

confidence: 99%

A Uniform Framework for Diagnosis of Discrete-Event Systems with Unreliable Sensors using Linear Temporal Logic

Dong¹,

Yin²,

Li³

2022

Preprint

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In this paper, we investigate the diagnosability verification problem of partially-observed discrete-event systems (DES) subject to unreliable sensors. In this setting, upon the occurrence of each event, the sensor reading may be nondeterministic due to measurement noises or possible sensor failures. Existing works on this topic mainly consider specific types of unreliable sensors such as the cases of intermittent sensors failures, permanent sensor failures or their combinations. In this work, we propose a novel uniform framework for diagnosability of DES subject to, not only sensor failures, but also a very general class of unreliable sensors. Our approach is to use linear temporal logic (LTL) with semantics on infinite traces to describe the possible behaviors of the sensors. A new notion of ϕ-diagnosability is proposed as the necessary and sufficient condition for the existence of a diagnoser when the behaviors of sensors satisfy the LTL formula ϕ. Effective approach is provided to verify this notion. We show that, our new notion of ϕ-diagnosability subsumes all existing notions of robust diagnosability of DES subject to sensor failures. Furthermore, the proposed framework is user-friendly and flexible since it supports an arbitrary user-defined unreliable sensor type based on the specific scenario of the application. As examples, we provide two new notions of diagnosability, which have never been investigated in the literature, using our uniform framework.

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“…In practice, however, due to measurement noises, sensor failures or malicious attacks, the sensor readings can be unreliable or non-deterministic. Such non-deterministic observation issue was initially addressed in the context of intermittent loss of observations [3], [4], [11], where it is assumed that some observable events are unreliable in the sense that their occurrences may be observed or be lost non-deterministically. In [17], Takai and Ushio investigated the issue of nondeterministic observation using Mealy automata.…”

Section: Introductionmentioning

confidence: 99%

Fault Diagnosis of Discrete-Event Systems under Non-Deterministic Observations with Output Fairness

Dong¹,

Gao²,

Yin³

et al. 2022

Preprint

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In this paper, we revisit the fault diagnosis problem of discrete-event systems (DES) under non-deterministic observations. Non-deterministic observation is a general observation model that includes the case of intermittent loss of observations. In this setting, upon the occurrence of an event, the sensor reading may be non-deterministic such that a set of output symbols are all possible. Existing works on fault diagnosis under non-deterministic observations require to consider all possible observation realizations. However, this approach includes the case where some possible outputs are permanently disabled. In this work, we introduce the concept of output fairness by requiring that, for any output symbols, if it has infinite chances to be generated, then it will indeed be generated infinite number of times. We use an assumeguarantee type of linear temporal logic formulas to formally describe this assumption. A new notion called output-fair diagnosability (OF-diagnosability) is proposed. An effective approach is provided for the verification of OF-diagnosability. We show that the proposed notion of OF-diagnosability is weaker than the standard definition of diagnosability under non-deterministic observations, and it better captures the physical scenario of observation non-determinism or intermittent loss of observations.

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

Cited by 22 publications

References 99 publications

Fault Handling in Industry 4.0: Definition, Process and Applications

Fault Handling in Industry 4.0: Definition, Process and Applications

A Uniform Framework for Diagnosis of Discrete-Event Systems with Unreliable Sensors using Linear Temporal Logic

Fault Diagnosis of Discrete-Event Systems under Non-Deterministic Observations with Output Fairness

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