Exploiting independence in a decentralised and incremental approach of diagnosis (original) (raw)
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Decentralized diagnosis and diagnosability by model checking
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New results on decentralized diagnosis of discrete-event systems
2004
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.
Diagnosis of Discrete Event Systems Using Decentralized Architectures
Discrete Event Dynamic Systems, 2007
Decentralized diagnosis of discrete event systems has received a lot of attention to deal with distributed systems or with systems that may be too large to be diagnosed by one centralized site. This paper casts the problem of decentralized diagnosis in a new hierarchical framework. A key feature is the exploitation of different local decisions together with appropriate rules for their fusion. This includes local diagnosis decisions that can be interpreted as "conditional decisions". Under this new framework, a series of new decentralized architectures are defined and studied. The properties of their corresponding notions of decentralized diagnosability are characterized and their relationship with existing work described. Corresponding verification algorithms are also presented and on-line diagnosis strategies discussed. *
A framework for decentralized qualitative model-based diagnosis
Proceedings of the 20th International …, 2007
In this paper we propose a framework for decentralized model-based diagnosis of complex systems. We consider the case where subsystems are developed independently along with their associated (or embedded) software modules -in particular their diagnostic software. This is useful in those situations where subsystems are developed (possibly by different suppliers) without a-priori knowledge of the system in which they will be exploited, or without making assumptions on the role they will play in such system. We describe a decentralized architecture where subsystems are analyzed by Local Diagnosers, coordinated by a Supervisor. Within the framework, both the Local Diagnosers and the Supervisor can be designed independently of each other, without any advance information on how the subsystems will be connected (provided that they share a common modeling ontology) and allowing also for runtime changes in the overall system structure. Local diagnosers are thus loosely coupled and communicate with the Supervisor via a standard interface, supporting independent implementations.
Designing distributed diagnosers for complex continuous systems
2009
Abstract Wear and tear from sustained operations cause systems to degrade and develop faults. Online fault diagnosis schemes are necessary to ensure safe operation and avoid catastrophic situations, but centralized diagnosis approaches have large memory and communication requirements, scale poorly, and create single points of failure. To overcome these problems, we propose an online, distributed, model-based diagnosis scheme for isolating abrupt faults in large continuous systems.
Distributed Diagnosis of Dynamic Systems Using Dynamic Bayesian Networks?
2000
This paper presents a Dynamic Bayesian Network (DBN)-based distributed diagnosis scheme, where each distributed diagnoser generates globally correct diagnosis results without a centralized coordinator by communicating a minimal number of measurements so that each diagnoser satises local observability properties, and the overall diagnoser is globally observable. We present a procedure for designing the distributed diagnosers by factoring a system DBN
Sixth International Workshop on Discrete Event Systems, 2002. Proceedings., 2002
An algorithm is proposed for decentralized failure diagnosis with asymmetric communication in which Diagnoser 2 estimates also the observer state of Diagnoser 1 and sends only that subset of failure states which is relevant for the other diagnoser when this is useful for Diagnoser 1's control task of failure detection and diagnosis. This algorithm can help in suggesting practically implementable heuristic algorithms.
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.
Designing distributed diagnosers for complex physical systems
2005
Abstract Online diagnosis methods require large computationally expensive diagnosis tasks to be decomposed into sets of smaller tasks so that time and space complexity constraints are not violated. This paper defines the distributed diagnosis problem in the Transcend qualitative diagnosis framework, and then develops heuristic algorithms for generating a set of local diagnosers that solve the global diagnosis problem without a coordinator. Two versions of the algorithm are discussed.