Decentralized state estimation and diagnosis of p-time labeled Petri nets systems
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Decentralized state estimation and diagnosis of p-time labeled Petri nets systems Patrice Bonhomme1 Received: 21 July 2019 / Accepted: 2 September 2020 / © Springer Science+Business Media, LLC, part of Springer Nature 2020
Abstract This paper proposes a state estimation technique in a decentralized context for time dependent systems. The plant of the studied system is modeled by P-time labeled Petri nets (P-TLPN) and the set of events is partitioned into a set of observable and unobservable ones, leading to a partial observation configuration. Indeed, the observation is distributed over a set of distinct sites which have their own local vision of the system. Moreover, some event are indistinguishable as the same label can be associated with the same transition adding another source of non-determinism. Thus, thanks to a global coordinator helped by the consideration of the timing factor, the local information transmitted via the different sites will be exploited to assess the set of states consistent with the current considered observation. The developed technique is an iterative procedure coupled with a time feasibility analysis (i.e., schedulability) conducted for particular firing sequences allowing to explain the considered observation, called time explanations. A diagnosis procedure aiming at evaluating the occurrence of particular faults for each behavior is also provided. Keywords Petri nets · Partial observation · Decentralized estimation · Diagnosis
1 Introduction The continuous expanding complexity of man-made systems such as manufacturing, communication networks, transportation and embedded systems goes hand in hand with the demand for the development of new efficient control and diagnosis techniques. Thus, collecting reliable state information becomes a major concern as it is necessary to interact efficiently (generally in real time) with the studied system in order to make the right decision in a timely and accurate manner. Faced with this situation, it is well known that, in practice it becomes impossible to associate to each state variable of interest a dedicated sensor to track the evolution of its current state. This particular prevalent situation is known as partial observation and is often due to limited sensing capabilities. The limited sensing Patrice Bonhomme
[email protected]; [email protected] 1
INSA CVL, LIFAT EA 6300. CNRS, ROOT ERL CNRS 7002, 3 rue de la Chocolaterie, CS 23410, 41034 Blois, France
Discrete Event Dynamic Systems
capabilities can be attributed to many different factors as accessibility, economical, environmental or the breakdown of existing sensors. Consequently, the development of methods allowing to estimate or reconstruct the system state even if complete state information cannot be directly obtained is crucial. Indeed, the decision-maker will have to make its decision thanks to the available data obtained from the existing sensors but in a situation of partial knowledge of the global system state. So, only incomplete information is traced back bu
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