Diagnosability Analysis of a Class of Hierarchical State Machines

Paoli, A.; Lafortune, Stéphane

doi:10.1007/s10626-008-0044-5

Cited by 23 publications

(10 citation statements)

References 30 publications

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“…For example, if it is obviously necessary to detect the delay in execution of a task from the predictive schedule, some of these delays might not be critical for the behavior of the system, either because they are very short, or thanks to the available free margin. Many modelling formalisms are classically used to build diagnosers, including automata [10] and their timed and probabilistic extensions, Petri nets [11][12], statecharts and hierarchical state machines [13]. The most promising perspective here would be to implement the diagnoser using online simulation, which is an efficient but hard to implement forecasting tool.…”

Section: First Challenge: Estimation Of Future Performancesmentioning

confidence: 99%

Coupling Predictive Scheduling and Reactive Control in Manufacturing: State of the Art and Future Challenges

Cardin

Trentesaux

Thomas

et al. 2015

Studies in Computational Intelligence

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Abstract. Nowadays, industrials are seeking for models and methods that are not only able to provide efficient overall production performance, but also reactive facing a growing set of unpredicted events. One important research activity in that field focuses on holonic/multi-agent control systems that couple predictive/proactive and reactive mechanisms into agents/holons. Meanwhile, not enough attention is paid to the optimization of this coupling. The aim of this paper is to depict the main research challenges that are to be addressed before expecting a large industrial dissemination. Relying on an extensive review of the state of the art, three main challenges are highlighted: the estimation of the future performances of the system in reactive mode, the design of efficient switching strategies between predictive and reactive modes and the design of efficient synchronization mechanisms to switch back to predictive mode.

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Section: First Challenge: Estimation Of Future Performancesmentioning

confidence: 99%

Coupling Predictive Scheduling and Reactive Control in Manufacturing: State of the Art and Future Challenges

Cardin

Trentesaux

Thomas

et al. 2015

Studies in Computational Intelligence

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“…HFSMs were considered for solving a class of control problems in [39]. Recently, diagnosis of HFSMs has been considered in [40], [41]. However, no patterns are involved and diagnosis is context-free.…”

Section: Correctnessmentioning

confidence: 99%

Diagnosis of Active Systems by Semantic Patterns

Lamperti

Zhao

2014

IEEE Trans. Syst. Man Cybern, Syst.

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A gap still exists between complex discrete-event systems (DESs) and the effectiveness of the state-of-the-art diagnosis techniques, where faults are defined at component levels and diagnoses incorporate the occurrences of component faults. All these approaches to diagnosis are context-free, in as much diagnosis is anchored to components, irrespective of the context in which they are embedded. By contrast, since complex DESs are naturally organized in hierarchies of contexts, different diagnosis rules are to be defined for different contexts. Diagnosis rules are specified based on associations between context-sensitive faults and regular expressions, called semantic patterns. Since the alphabets of such regular expressions are stratified, so that the semantic patterns of a context are defined based on the interface symbols of its subcontexts only, separation of concerns is achieved, and the expressive power of diagnosis is enhanced. This new approach to diagnosis is bound to seemingly contradictory but nonetheless possible scenarios: a DES can be normal despite the faulty behavior of a number of its components; also, it can be faulty despite the normal behavior of all its components.Index Terms-Artificial intelligence, decision support systems, discrete-event systems (DESs), fault diagnosis, intelligent systems.

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“…For this class of systems, results have been obtained for a wide variety of problems and system structure. They include centralized discrete-event system (DES) models, decentralized models, distributed systems, and systems with hierarchical structure [9]- [16]. Extensions to stochastic DESs and timed DESs have also been reported [17]- [21].…”

Section: Introductionmentioning

confidence: 99%

Fault Diagnosis in Discrete-Event Systems with Incomplete Models: Learnability and Diagnosability

Kwong

Yonge-Mallo

2015

IEEE Trans. Cybern.

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Most model-based approaches to fault diagnosis of discrete-event systems require a complete and accurate model of the system to be diagnosed. However, the discrete-event model may have arisen from abstraction and simplification of a continuous time system, or through model building from input-output data. As such, it may not capture the dynamic behavior of the system completely. In a previous paper, we addressed the problem of diagnosing faults given an incomplete model of the discrete-event system. We presented the learning diagnoser which not only diagnoses faults, but also attempts to learn missing model information through parsimonious hypothesis generation. In this paper, we study the properties of learnability and diagnosability. Learnability deals with the issue of whether the missing model information can be learned, while diagnosability corresponds to the ability to detect and isolate a fault after it has occurred. We provide conditions under which the learning diagnoser can learn missing model information. We define the notions of weak and strong diagnosability and also give conditions under which they hold.

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Diagnosability Analysis of a Class of Hierarchical State Machines

Cited by 23 publications

References 30 publications

Coupling Predictive Scheduling and Reactive Control in Manufacturing: State of the Art and Future Challenges

Coupling Predictive Scheduling and Reactive Control in Manufacturing: State of the Art and Future Challenges

Diagnosis of Active Systems by Semantic Patterns

Fault Diagnosis in Discrete-Event Systems with Incomplete Models: Learnability and Diagnosability

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