Download Discrete Event Systems: Diagnosis and Diagnosability by Moamar Sayed-Mouchaweh PDF

By Moamar Sayed-Mouchaweh

Discrete occasion structures: analysis and Diagnosability addresses the matter of fault prognosis of Discrete occasion platforms (DESs). This booklet presents the fundamental strategies and ways valuable for the layout of an effective fault analysis method for a variety of glossy engineering functions. This ebook classifies different concepts and ways in keeping with a number of standards comparable to: modeling instruments (Automata, Petri nets, Templates) that's used to build the version; the data (qualitative in response to occasions occurrences and/or states outputs, quantitative in response to sign processing, facts research) that's had to examine and attain the prognosis; the choice constitution (centralized, decentralized) that's required to accomplish the prognosis; in addition to the complexity (polynomial, exponential) of the set of rules that's used to figure out the set of faults that the proposed process is ready to diagnose in addition to the hold up time required for this analysis. The target of this category is to choose the effective technique to in achieving the fault analysis based on the appliance constraints. This publication will contain illustrated examples of the awarded tools and methods in addition to a dialogue at the software of those tools on a number of real-world problems.

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Extra resources for Discrete Event Systems: Diagnosis and Diagnosability

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Therefore, the new content of C is achieved as follows: Cnew = ( {(1N, 2N), (1N, 10F1)} ∩RC {(1N, 1N), (1N, 2N), (1N, 10F 1)} )∩C (1N , 1N ) Cnew = ( {(1N, 2N), (1N, 10F1)} )∩C (1N, 1N ) = {(1N, 2N), (1N, 10F1)} . 10 Let us see now how the coordinator can solve the decision ambiguity due to the partial observation of local diagnosers. Let us take the faulty event sequence uF = and let us see how the coordinator can find the right global state with its fault label after the occurrence of each observable event by one or both of local diagnosers.

For our example of the pump and the valve, we can notice easily that K is not {Σi } separable sinceR = R1 R2 . 2 Approaches Based on Synchronous Composition of Local Diagnosers Typical application example of this category of diagnosis approaches is the monitoring of hierarchical telecommunication networks [31–33]. The latter are composed of one supervision center (SC) and a set of technical centers (TC). Each TC has a set of data switches (SW) routing data through the network. SC is in charge of receiving alarms of each SW through its corresponding TC.

23. 24 shows the observable automaton OBS(GΩ ) for GΩ . Since OBS(GΩ ) is deterministic, thus it corresponds to the diagnoser of supervision pattern Ω representing the occurrence of the stuck close fault event. 11 (see Fig. 7) because the diagnoser contains the Ω -indeterminate cycle (< No-Load > < CV >)∗ shown in dotted line in Fig. 24. 12 Let us take the example of the pump-valve system. Let us consider that the valve can fail at the stuck-closed failure mode (occurrence of fault f1 of type F1 ), as it is depicted in Fig.

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