Practical Model-Based Diagnosis with Qualitative Possibilistic Uncertainty

An approach to fault isolation that exploits vastly incomplete models is presented. It relies on separate descriptions of each component behavior, together with the links between them, which enables focusing of the reasoning to the relevant part of the system. As normal observations do not need explanation, the behavior of the components is limited to anomaly propagation. Diagnostic solutions are disorders (fault modes or abnormal signatures) that are consistent with the observations, as well as abductive explanations. An ordinal representation of uncertainty based on possibility theory provides a simple exception-tolerant description of the component behaviors. We can for instance distinguish between effects that are more or less certainly present (or absent) and effects that are more or less certainly present (or absent) when a given anomaly is present. A realistic example illustrates the benefits of this approach.Comment: Appears in Proceedings of the Eleventh Conference on Uncertainty in Artificial Intelligence (UAI1995

Practical model-based diagnosis with qualitative possibilistic uncertainty

International audienceAn approach to fault isolation that exploits vastly incomplete models is presented. It relies on separate descriptions of each component behavior, together with the links between them, which enables focusing of the reasoning to the relevant part of the system. As normal observations do not need explanation, the behavior of the components is limited to anomaly propagation. Diagnostic solutions are disorders (fault modes or abnormal signatures) that are consistent with the observations, as well as abductive explanations. An ordinal representation of uncertainty based on possibility theory provides a simple exception-tolerant description of the component behaviors. We can for instance distinguish between effects that are more or less certainly present (or absent) and effects that are more or less certainly present (or absent) when a given anomaly is present. A realistic example illustrates the benefits of this approach

Possibilistic handling of uncertainty in fault diagnosis

eBook Published 17 July 2019, ISBN 978-0429142741, https://doi.org/10.1201/9780429142741International audienceFMECAs (failure mode effects and criticality analyses) form a very important category of knowledge, compiled during the design phase of a complex system, and used for diagnosis activities. This section proposes their extension, allowing a finer representation of the available knowledge, at approximately the same cost, through the introduction of an appropriate representation of uncertainty and incompleteness based on possibility theory. Efficient discrimination techniques exploiting uncertain observations are introduced, and a satellite industry example illustrates the computations involved in this approach

Une méthode pratique de diagnostic de pannes ''basée sur les modèles'' avec prise en compte qualitative de l'incertitude

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Handling Uncertainty with Possibility Theory and Fuzzy Sets in a Satellite Fault Diagnosis Application

The Fault Mode Effects and Criticality Analyses (FMECA), describe the impact of identified faults. They form an important category of knowledge gathered during the design phase of a satellite, and are used also for diagnosis activities. This paper proposes their extension, allowing a finer representation of the available knowledge, at approximately the same cost, through the introduction of an appropriate representation of uncertainty and incompleteness based on Zadeh's possibility theory and fuzzy sets. The main benefit of the approach is to provide a qualitative treatment of uncertainty where we can for instance distinguish manifestations which are more or less certainly present (or absent) and manifestations which are more or less possibly present (or absent) when a given fault is present. In a second step, the proposed approach is extended to handle fault impacts expressed as event chronologies. Efficient, real-time compatible discrimination techniques exploiting uncertain observat..

Possibilistic handling of uncertainty in practical fault isolation

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Qualitative and logical handling of uncertainty in a relational model for operational fault diagnosis

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Possibility theory in "Fault mode effect analyses". A satellite fault diagnosis application

International audienceMatra Marconi Space (MMS) has been developing spacecraft diagnostic support systems for eight years. Keeping in mind the operational constraints of scale and efficiency that guided the design of the first systems, the reasoning paradigms are now refined, in order to improve the solutions. The FMECA, Fault Mode Effects and Criticality Analyses, form a very important category of knowledge compiled during the design phase of a satellite, and used also for diagnosis activities. This paper proposes their extension, allowing a finer representation of the available knowledge, at approximately the same cost, through the introduction of an appropriate representation of uncertainty and incompleteness based on possibility theory. Efficient discriminization techniques exploiting uncertain observations are introduced, and an example illustrates the mechanisms involved in this approach

Explaining away in a possibilistic setting

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Relational approach to fault diagnosis based on a functional model

International audienceMatra Marconi Space has been developing operational expert systems for spacecraft fault diagnosis using hybrid approaches, including techniques related to model based approach and fault trees. Keeping in mind the operational constraints of exhaustivity and efficiency that guided the initial choices, the reasoning paradigms developed are now refined, to improve the solutions reached. The paper describes some of the techniques implemented so far, and proposes extensions to the relational approach to automated diagnosis, in order to include more of the available knowledge to refine the representation of influences, by distinguishing certain and possible influence