Diagnosis on a sliding window for partially observable Petri nets

Title:

Diagnosis on a sliding window for partially observable Petri nets

Creator:

Chouchane, Amira and Declerck, Philippe

Identifier:

https://cdk.lib.cas.cz/client/handle/uuid:37fb97b2-105a-400a-9ea9-1cd7c33202a5
uuid:37fb97b2-105a-400a-9ea9-1cd7c33202a5
doi:10.14736/kyb-2022-4-0479

Subject:

diagnosis, state estimation, partially observed Petri net, sliding window, and integer linear programming

Type:

model:article and TEXT

Format:

bez média and svazek

Description:

In this paper, we propose an algebraic approach to investigate the diagnosis of partially observable labeled Petri nets based on state estimation on a sliding window of a predefined length h. Given an observation, the resulting diagnosis state can be computed while solving integer linear programming problems with a reduced subset of basis markings. \blue{The proposed approach consists in exploiting} a subset of h observations at each estimation step, which provides a partial diagnosis relevant to the current observation window. This technique allows a status update with a "forgetfulness" of past observations and enables distinguishing repetitive and punctual faults. The complete diagnosis state can be defined as a function of the partial diagnosis states interpreted on the sliding window. As \blue{the} analysis shows that some basis markings can present an inconsistency with a future evolution, which possibly implies unnecessary computations of basis markings, a withdrawal procedure of these \blue{irrelevant} basis markings based on linear programming is proposed.

Language:

English

Rights:

http://creativecommons.org/licenses/by-nc-sa/4.0/
policy:public

Coverage:

479-497

Source:

Kybernetika | 2022 Volume:58 | Number:4

Harvested from:

CDK

Metadata only:

false