Optimized diagnosability of distributed discrete event systems through abstraction. (Diagnosticabilité Optimisée des Systèmes Distribués à Evénements Discrets par Abstraction)

Over the latest decades, much research work has been done on automatic fault diagnosis. However, it is imperative to analyze at system design stage how correctness and efficiency and diagnosis algorithm can achieve. Thus many studies were interested in analyzing and characterizing the properties of diagnosability of a system. Diagnosability is the property of a system ensuring that it generates observations for detecting and discriminating faults in finite time after their occurrence.In this thesis, we investigate how to optimize distributed diagnosability analysis by abstracting necessary and sufficient information from local objects to decide global diagnosability decision. The algorithm efficiency can be greatly improved by synchronization of abstracted local objects compared to that of non abstracted local ones.Then we extend the distributed diagnosability algorithm from fault event first to simple pattern and then to general pattern, where pattern can describe more general objects in the diagnosis problem, e.g., multiple faults, multiple occurrences of the same fault, ordered occurrences of significant events, etc. In the distributed framework, the pattern recognition is first incrementally performed normally in a subsystem and then pattern diagnosability can be determined by adjusting abstracted method used in fault event case. We prove the correctness and efficiency of our proposed algorithm both in theory through proof and in practice through implementation.Finally we study joint diagnosability problem in systems with autonomous components, i.e., observable information is distributed instead of centralized. In other words, each component can only observe its own observable events. We give joint diagnosability definition. And then we discuss the undecidability of joint diagnosability in the general case, i.e., communication events are not observable, before proposing an algorithm to test its sufficient condition. In addition, we also get a decidability result and algorithm when communications are observable.

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