Diagnosis on a sliding window for partially observable Petri nets

Chouchane, Amira, and Declerck, Philippe. "Diagnosis on a sliding window for partially observable Petri nets." Kybernetika 58.4 (2022): 479-497. <http://eudml.org/doc/299371>.

@article{Chouchane2022,
abstract = {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. 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 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 irrelevant basis markings based on linear programming is proposed.},
author = {Chouchane, Amira, Declerck, Philippe},
journal = {Kybernetika},
keywords = {diagnosis; state estimation; partially observed Petri net; sliding window; integer linear programming},
language = {eng},
number = {4},
pages = {479-497},
publisher = {Institute of Information Theory and Automation AS CR},
title = {Diagnosis on a sliding window for partially observable Petri nets},
url = {http://eudml.org/doc/299371},
volume = {58},
year = {2022},
}

TY - JOUR
AU - Chouchane, Amira
AU - Declerck, Philippe
TI - Diagnosis on a sliding window for partially observable Petri nets
JO - Kybernetika
PY - 2022
PB - Institute of Information Theory and Automation AS CR
VL - 58
IS - 4
SP - 479
EP - 497
AB - 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. 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 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 irrelevant basis markings based on linear programming is proposed.
LA - eng
KW - diagnosis; state estimation; partially observed Petri net; sliding window; integer linear programming
UR - http://eudml.org/doc/299371
ER -