Effective Verification of Weak Diagnosability (original) (raw)
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Undecidable Case and Decidable Case of Joint Diagnosability in Distributed Discrete Event Systems
2013
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, one of our recent work 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, we assumed that the local paths in each component can be exhaustively enumerated, which is not suitable in a general case where there are embedded cycles. In this paper, we get some new results about diagnosability in such a system in a general case, i.e., what we call joint diagnosability in a self-observed distributed system. First, we prove the undecidability of joint diagnosability with unobservable communication events by reducing the Post's Correspondence Problem to joint diagnosability problem. We also propose an algorithm to check a sufficient but not necessary condition of joint diagnosability, which is then adapted when the assumption of all communication events being unobservable is relaxed, i.e., communication events could be either observable or unobservable. Then, we discuss about the decidable case where communication events are all observable and develop a new efficient algorithm to test it. Finally, we also provide an important property of joint diagnosability after analyzing its relationship with classical diagnosability.
2015
Diagnosability is a procedure whose goal is to determine whether any failure – or a class of failures – can be determined in finite time after its occurrence. Earlier works on diagnosability of discrete event systems (DES) establish some intermediary models from the analyzed model and then call some procedures to check diagnosablity based on these models, while recent works try to give a diagnosability formulation as a model-checking problem. However, there still lacks a single framework able to handle both of the diagnosability issues: how to model the problem? and how to decide it? In this paper, we build on some existing works which have formally established necessary and sufficient conditions for diagnosability of DES and we propose a generic operative formulation of diagnosability using the -calculus logic, which allows resolving the diagnosability issue within a single formalism. Diagnosis, Diagnosability, Monitoring, Discrete event system, -calculus 1.
Distributed Analysis for Diagnosability in Concurrent Systems
arXiv (Cornell University), 2015
Complex systems often exhibit unexpected faults that are difficult to handle. Such systems are desirable to be diagnosable, i.e. faults can be automatically detected as they occur (or shortly afterwards), enabling the system to handle the fault or recover. A system is diagnosable if it is possible to detect every fault, in a finite time after they occurred, by only observing the available information from the system. Complex systems are usually built from simpler components running concurrently. We study how to infer the diagnosability property of a complex system (distributed and with multiple faults) from a parallelized analysis of the diagnosability of each of its components synchronizing with fault free versions of the others. In this paper we make the following contributions: (1) we address the diagnosability problem of concurrent systems with arbitrary faults occurring freely in each component. (2) We distribute the diagnosability analysis and illustrate our approach with examples. Moreover, (3) we present a prototype tool that implements our techniques showing promising results.
Diagnosability Analysis for Self-observed Distributed Discrete Event Systems
2013
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...
A µ-calculus formulation of the diagnosability of discrete event systems
International Journal of Critical Computer-Based Systems, 2016
Diagnosability is a procedure whose goal is to determine whether any failure-or a class of failures-can be determined in finite time after its occurrence. Earlier works on diagnosability of discrete event systems (DES) establish some intermediary models from the analyzed model and then call some procedures to check diagnosablity based on these models, while recent works try to give a diagnosability formulation as a modelchecking problem. However, there still lacks a single framework able to handle both of the diagnosability issues: how to model the problem? and how to decide it? In this paper, we build on some existing works which have formally established necessary and sufficient conditions for diagnosability of DES and we propose a generic operative formulation of diagnosability using the µ-calculus logic, which allows resolving the diagnosability issue within a single formalism.
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.
Generalizing diagnosability definition and checking for open systems: a Game structure approach
2010
Diagnosability is the property of a partially observable system with a given set of possible faults, that these faults can be detected with certainty with a finite observation. Usually, the definition and the verification methods of diagnosability ignore the nature of the system events, controllable (by the system) or uncontrollable. In this paper we show the influence of controllability of events on the diagnosability definition and verification. We show that the classical diagnosability is a special case where we consider the whole system as controllable. Using Game Structure we generalize the definition of diagnosability by the mean of strategies. Then, Alternating-time Temporal Logic is used in order to model check diagnosability in the case of uncontrollable events. We show how the framework is suitable for one system and also for a set of interacting systems.
IFAC Proceedings Volumes, 2014
In order to diagnose the occurrence of a fault event in a Discrete-Event System (DES), 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. In some cases, the language of the system is also diagnosable even when a subset of the set of observable events under consideration is used as the actual observable event set. Among the benefits that such a reduction may bring we list the reduction in the number of sensors used in the diagnosis, therefore reducing the cost of the system, and the possibility to deploy the sensor redundancy to obtain a more reliable diagnosis decision. In this work, we propose two algorithms 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 methods are based on the construction of verifiers and have lower computational complexity than another method recently proposed in the literature.
A µ-Calculus Framework for the Diagnosability of Discrete Event Systems
2014
Diagnosability is a procedure whose goal is to determine whether any failure – or a class of failures – can be determined in finite time after its occurrence. Earlier works on diagnosability of discrete event systems (DES) establish some intermediary models from the analyzed model and then call some procedures to check diagnosablity based on these models, while recent works try to give a diagnosability formulation as a modelchecking problem. However, there still lacks a single framework able to handle both of the diagnosability issues: how to model the problem? and how to decide it? In this paper, we build on some existing works which have formally established necessary and sufficient conditions for diagnosability of DES and we propose a generic operative formulation of diagnosability using the μ-calculus logic, which allows resolving the diagnosability issue within a single formalism.
Decentralized Diagnosis of Event-Driven Systems for Safely Reacting to Failures
IEEE Transactions on Automation Science and Engineering, 2009
We introduce the notion of safe-codiagnosability, extending the notion of safe-diagnosability [8] to the decentralized setting. For a system, a certain sub-behavior is deemed safe (captured via a safety specification), and a further sub-behavior is deemed non-faulty (captured via a non-fault specification). Safe-codiagnosability requires that when the system executes a trace that is faulty, there exists at least one diagnoser that can detect this within bounded delay and also before the safety specification is violated. The above notion of safe-codiagnosability may also be viewed as an extension of the notion of codiagnosability [11], where the latter did not have any safety requirement. We show that safe-codiagnosability is equivalent to codiagnosability together with "zero-delay codiagnosability" of "boundary safe traces". (A safe trace is a boundary safe trace if there exists a single-event extension that is unsafe.) We give an algorithm of polynomial complexity for verifying safecodiagnosability. For a safe-codiagnosable system, the same methods as those proposed in [11] can be applied for off-line synthesis of individual diagnosers, as well as for on-line diagnosis using them.