Reformulation for the Diagnosis of Discrete-Event Systems

Alban Grastien and Gianluca Torta
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phmc_10_098.pdf119.12 KBSeptember 22, 2010 - 2:30pm

Diagnosis is traditionally defined on a space of hypotheses (typically, all the combinations of zero or more possible faults).
In the present paper, we argue that a suitable reformulation of this hypothesis space can lead to more efficient diagnostic algorithms and more compact diagnoses, most notably by exploiting opportunities for various forms of model abstraction. We also study several formal properties related to the correctness and precision of the diagnoses obtained through reformulation.
Our approach consists in the following main steps:
- the hypothesis space is formulated differently,
- the diagnosis for this new problem is computed,
- the diagnosis is mapped back to the original formulation of the hypothesis space.
The main benefit of this process is that a suitably defined new hypothesis space may allow powerful model abstractions.
In this paper, we focus on the first and last steps, i.e. on the operations related to the mapping from one hypothesis space to another one.

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