Undecidable Case and Decidable Case of Joint Diagnosability in Distributed Discrete Event Systems (original) (raw)
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HAL (Le Centre pour la Communication Scientifique Directe), 2012
Diagnosability is an important property that determines at design stage how accurate any diagnosis algorithm can be on a partially observable system. Most existing approaches assumed that each observable event in the system is globally observed. Considering the cases where there is no global information, a recent work has proposed a new framework to check diagnosability in a system where each component can only observe its own observable events to keep the internal structure private in terms of observations. However, the authors implicitly assume that the local paths in each component can be exhaustively enumerated, which is not true in a general case where there are embedded cycles. In this paper, we get some new results about diagnosability in such a system, i.e., what we call joint diagnosability in a self-observed distributed system. First we prove its undecidability with unobservable communication events by reducing the Post's Correspondence Problem (PCP) to an observation problem, inspired from an existing work. Then we propose an algorithm to check a sufficient but not necessary condition of joint diagnosability. Finally we briefly discuss about the decidable case where communication events are all observable.
Diagnosability Analysis for Self-observed Distributed Discrete Event Systems
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Diagnosability is a crucial property that determines at design stage how accurate any diagnosis algorithm can be on a partially observable system and thus has a significant impact on the performance and reliability of complex systems. Most existing approaches assumed that observable events in the system are globally observed. But sometimes it is not possible to obtain global information. Thus a recent work has proposed a new framework to check diagnosability in a system where each component can only observe its own observable events to keep the internal structure private in terms of observations. However, the authors implicitly assume that local paths in components can be exhaustively enumerated, which is not true in a general case where there are embedded cycles. In this paper, we get some new results about diagnosability in such a system, i.e., what we call joint diagnosability in a self-observed distributed system. First we prove the undecidability of joint diagnosability with un...
Diagnosability Analysis of Discrete Event Systems with Autonomous Components
European Conference on Artificial Intelligence, 2010
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. 2 PRELIMINARIES In this section, we first describe how to model DESs with autonomous components and then give some important concepts before proposing cooperative diagnosis architecture for such systems. 2.1 System model We consider a distributed DES composed of a set of autonomous components {G 1 , G 2 ,..., G n } that communicate with each other by communication events. Moreover, any component can only observe its own observable events and thus can keep its internal structure private. This kind of system is modeled by a set of FSMs with each one representing the local model of one component.
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2012 IEEE 24th International Conference on Tools with Artificial Intelligence, 2012
Diagnosability is an important system property that determines at design stage how accurate any diagnostic reasoning can be on a partially observed system. A fault in a discrete-event system is diagnosable iff its occurrence can always be deduced from enough observations. It is well known that centralized diagnosability approaches lead to combinatorial explosion of the search space since they assume the existence of a monolithic model of the system. This is why very recently the distributed approaches for diagnosability began to be investigated, relying on local objects. On the other hand, diagnosis objectives are generalized from fault event to fault pattern that can represent multiple faults, repeating fault, sequences of significant events, repair of faults, etc. For pattern case, most existing approaches are centralized. In this paper, we propose a new distributed framework for pattern diagnosability. We first show how to recognize patterns by incrementally constructing local pattern recognizers through extended subsystems. Then we propose a structure called regional pattern verifier that is constructed from the subsystem where the pattern is completely recognized before showing how to abstract just the necessary and sufficient diagnosability information to further save the search space. Then the global consistency checking is based on another local structure called abstracted local twin checker to analyze pattern diagnosability. In this way, we avoid constructing global objects both for pattern recognition and for pattern diagnosability. The correctness of our distributed algorithm is theoretically proved and its efficiency experimentally demonstrated by the results of the implementation.
Polynomial Time Verification of Decentralized Diagnosability of Discrete Event Systems
IEEE Transactions on Automatic Control, 2011
The first step in the diagnosis of failure occurrences in discrete event systems is the verification of the system diagnosability. Several works have addressed this problem using either diagnosers or verifiers for both centralized and decentralized architectures. In this technical note, we propose a new algorithm to verify decentralized diagnosability of discrete event systems. The proposed algorithm requires polynomial time in the number of states and events of the system and has lower computational complexity than all other methods found in the literature. In addition, it can also be applied to the centralized case.
New results on decentralized diagnosis of discrete-event systems
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The past decade has witnessed the development of a body of theory, with associated applications, for fault diagnosis of dynamic systems that can be modeled in a discrete event systems framework. This paper presents several new notions of diagnosability, together with on-line diagnosis decision rules, in the context of a general decentralized architecture that allows for the use of "conditional decisions" by local diagnosers. The properties of these new notions of diagnosability are presented and their relationship with existing work discussed. Verification algorithms and local diagnoser synthesis methods are briefly outlined.
An Incremental Approach for Pattern Diagnosability in Distributed Discrete Event Systems
2009 21st IEEE International Conference on Tools with Artificial Intelligence, 2009
Diagnosability is a crucial property that determines at design stage how accurate any diagnosis algorithm can be on a partially observable system. Recent work on diagnosability has generalized fault event case to pattern case, which can describe more general objectives for diagnosis problem, but based on global model and global twin plant construction. In this paper, we propose an original framework to solve pattern diagnosability in a distributed way to avoid calculating global objects. We first show how to incrementally accomplish pattern recognition without building global model by propagating only diagnosability relative information between components. Then an efficient way to construct pattern verifier is proposed, which is inspired from the classical twin plant method but with smaller state space, to search for partial critical paths, whose global consistency is subsequently checked. Meanwhile we prove that the result obtained from our distributed approach is on an equality with that from the centralized one but the evaluation result shows that our search state space exploited is only a small subpart of the global twin plant, whose construction is unavoidable in the centralized approach.
Decentralized modular diagnosis of concurrent discrete event systems
2008 9th International Workshop on Discrete Event Systems, 2008
The problem of decentralized modular fault diagnosis of concurrent discrete event systems, that is composed of a set of component modules, is formulated and studied. In the proposed decentralized modular framework, diagnosis is performed by the local diagnosers, located at the component sites, using their own local observations. This is to ensure the scalability of the approach with respect to the number of component modules, and we require that the local diagnosers be "modularly computable", i.e., their computation should be based on the local models, and not the global models. It is also required that there are no missed-detections (every fault is detected within a bounded number of transitions) and no false-alarms (a fault detection report is issued only when a fault has occurred). We formally define the decentralized modular diagnosis problem and introduce the notion of modular diagnosability as a key property for the existence of desired decentralized modular diagnosers. We show that under this property, the complexity for constructing the local diagnosers is polynomial in the number of local modules. We present a method for testing the modular diagnosability property by reducing it to an instance of a certain codiagnosability property for which known verification techniques exist.
Decentralized Failure Diagnosis of Discrete Event Systems
IEEE Transactions on Systems, Man, and Cybernetics, 2006
By decentralized diagnosis we mean diagnosis using multiple diagnosers, each possessing its own set of sensors, without involving any communication among diagnosers or to any coordinators. The notion of decentralized diagnosis is formalized by introducing the notion of codiagnosability that requires that a failure be detected by one of the diagnosers within a bounded delay. Algorithms of complexity polynomial in the size of the system and the nonfault specification are provided for: 1) testing codiagnosability, 2) computing the bound in delay of diagnosis, 3) offline synthesis of individual diagnosers, and 4) online diagnosis using them. The notion of codiagnosability and the above algorithms are initially presented in a setting of a specification language (violation of which represents a fault) and are later specialized to the case where faults are modeled as the occurrences of certain events. The notion of strong codiagnosability is also introduced to capture the ability of being certain about both the failure as well as the nonfailure conditions in a system within a bounded delay.
12th International Workshop on Discrete Event Systems (2014), 2014
In order to diagnose the occurrence of a fault event, it is first necessary to verify if the language of the system is diagnosable with respect to an observable event set and a fault event set. This verification can be carried out, in polynomial time, by using a verifier automaton. In some cases, the language of the system remains diagnosable even if some events of the observable event set become unobservable, i.e., the language of the system is diagnosable with respect to a subset of the observable event set. This leads to a reduction in the number of sensors used in the diagnosis, therefore reducing the cost of the system. Another possibility is to exploit the redundancy of some sensors in order to obtain a more reliable and robust diagnosis. In this work, we propose an algorithm to find, in a systematic way, all minimal subsets of the observable event set that ensure the diagnosability of the DES (minimal diagnosis bases). The method is based on the construction of verifiers and has lower computational complexity than other methods recently presented in the literature.