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Philippe Dague

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12 papers
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12

AAAI Conference 2017 Conference Paper

Diagnosability Planning for Controllable Discrete Event Systems

  • Hassan Ibrahim
  • Philippe Dague
  • Alban Grastien
  • Lina Ye
  • Laurent Simon

In this paper, we propose an approach to ensure the diagnosability of a partially controllable system. Given a model of correct and faulty behaviors of a partially observable discrete event system, equipped with a set of elementary actions that do not intertwine with autonomous events, we search a diagnosability plan, i. e. , a sequence of applicable actions that leads the system from an initial belief state (a set of potentially current states) to a diagnosable belief state, in which the system is then left to run freely. This helps in reducing the diagnosis interaction with running systems and can be applied, e. g. , on the output of a repair plan, like in power networks. The two successive stages of this approach keep diagnosability planning, including diagnosability tests, in PSPACE in comparison to the EXPTIME test for the more complex active diagnosability used usually in such cases. For this, we propose to construct incrementally the twin plant structure of the given system and to exploit its parts already constructed while testing the candidate plans and constructing its next parts. This helps in pruning the twin plant constructions and many non-diagnosability plan tests. We have created a special benchmark and tested three proposed methods, according to the recycling level of twin plants construction, with one cost function used for plan optimality and an optional heuristics.

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ECAI Conference 2016 Conference Paper

Fault Manifestability Verification for Discrete Event Systems

  • Lina Ye
  • Philippe Dague
  • Delphine Longuet
  • Laura Brandán Briones
  • Agnes Madalinski

Fault diagnosis is a crucial and challenging task in the automatic control of complex systems, whose efficiency depends on the diagnosability property of a system. Diagnosability describes the system ability to determine whether a given fault has effectively occurred based on the observations. However, this is a very strong property that requires generally high number of sensors to be satisfied. Consequently, it is not rare that developing a diagnosable system is too expensive. To solve this problem, in this paper, we first define a new system property called manifestability that represents the weakest requirement on faults and observations for having a chance to identify on line fault occurrences and can be verified at design stage. Then, we propose an algorithm with PSPACE complexity to automatically verify it.

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ECAI Conference 2010 Conference Paper

Diagnosability Analysis of Discrete Event Systems with Autonomous Components

  • Lina Ye
  • Philippe Dague

Diagnosability is the property of a given partially observable system model to always exhibit unambiguously a failure behavior from its only available observations in finite time after the fault occurrence, which is the basic question that underlies diagnosis taking into account its requirements at design stage. However, for the sake of simplicity, the previous works on diagnosability analysis of discrete event systems (DESs) have the same assumption that any observable event can be globally observed, which is at the price of privacy. In this paper, we first briefly describe cooperative diagnosis architecture for DESs with autonomous components, where any component can only observe its own observable events and thus keeps its internal structure private. And then a new definition of cooperative diagnosability is consequently proposed. At the same time, we present a formal framework for cooperative diagnosability checking, where global consistency of local diagnosability analysis can be achieved by analyzing communication compatibility between local twin plants without any synchronization. The formal algorithm with its discussion is provided as well.

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ECAI Conference 2008 Conference Paper

A probabilistic analysis of diagnosability in discrete event systems

  • Farid Nouioua
  • Philippe Dague

This paper shows that we can take advantage of information about the probabilities of the occurrences of events, when this information is available, to refine the classical results of diagnosability: instead of giving a binary answer, the approach we propose allows one to quantify, in particular, the degree of non-diagnosability in case of negative answer. The dynamics of the system is modelled by a reducible Markov chain. A state of this chain contains information about whether it is faulty (resp. ambiguous) or not. The useful refinements of the decision about diagnosability are then obtained from the asymptotic analysis of this Markov chain. This analysis may be very useful in practice since it may lead to take the decision of tolerating some non-diagnosable systems, if their non-diagnosability is not critical, and thus allows one saving the cost of additional sensors necessary to make these systems diagnosableThis work is part of DIAFORE project supported by ANR under grant ANR-05-PDIT-016-05.

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LPAR Conference 2008 Conference Paper

Distributed Consistency-Based Diagnosis

  • Vincent Armant
  • Philippe Dague
  • Laurent Simon

Abstract A lot of methods exist to prevent errors and incorrect behaviors in a distributed framework, where all peers work together for the same purpose, under the same protocol. For instance, one may limit them by replication of data and processes among the network. However, with the emergence of web services, the willing for privacy, and the constant growth of data size, such a solution may not be applicable. For some problems, failure of a peer has to be detected and located by the whole system. In this paper, we propose an approach to diagnose abnormal behaviors of the whole system by extending the well known consistency-based diagnosis framework to a fully distributed inference system, where each peer only knows the existence of its neighbors. Contrasting with previous works on model-based diagnosis, our approach computes all minimal diagnoses in an incremental way, without needs to get any conflict first.

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AAAI Conference 1987 Conference Paper

Troubleshooting: When Modeling Is the Trouble

  • Philippe Dague

This paper shows how order of magnitude reasoning has been successfully used for troubleshooting complex analog circuits. The originality of this approach was to be able to remove the gap between the information required to apply a general theory of diagnosis and the limited information actually available. The expert’s ability to detect a defect by reasoning about the significant changes in behavior it induces is extensively exploited here: as a kind of reasoning that justifies the qualitative modeling, as a heuristic that defines a strategy and as a working hypothesis that makes clear the scope of this approach.

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