Inproceedings
Book title :
International Conference on Information Technologies (ICIT'19)
Séries :
Studies in Systems, Decision and Control
Editeurs : Olga Dolinina; Alexander Brovko; Vitaly Pechenkin; Alexey Lvov; Vadim Zhmud; Vladik Kreinovich
Adresse :
Saratov, Russia
ISBN : 978-3-030-12072-6
Publisher :
Informations
Période : February 2019
Pages : 406-420
Détails
Towards Fuzzy Partial Global Fault Diagnosis
Diagnosis aims at identifying a faulty system based on its behavior observations. It is widely emerged in altogether computer sciences fields, among others: aeronautics, space exploration, nuclear energy, process industries, manufacturing, healthcare, networking, automatism and many other control applications. Diagnosis involves distributed components with an uncertain global view. This paper intends to provide an efficient fuzzy based diagnosis mechanism. Such mechanism enables local hosts' diagnosis. These local decisions could be merged to provide the global diagnosis. The fuzziness choice is motivated by the fact of incomplete and uncertain system descriptions and observations. Also it is justified by the difficulties of obtaining a complete viewpoint of all system parts where the control is distributed. Our diagnosis mechanism, named FPGD for Fuzzy Partial Global Diagnosis consists of two main steps: Firstly, each remote control host detects and localizes abnormal behaviors which results on a local diagnosis. Each host proceeds by applying a recovery planned actions to maintain system functioning. Furthermore, such local diagnoses should be sent to the global part in order to be merged and analyzed, hence giving a precise and exhaustive global diagnosis. The automatic diagnosis reasoning is a fuzzy system; which based on fuzzy rules, handles incomplete information to deduce system malfunctioning.
Mots clés :
Complex system Diagnosis Fuzzy logic Internet of things
Réf. de citation :
misc-lab-219
DOI :
10.1007/978-3-030-12072-6_33
Lien :
Texte intégral
ACM :
S. Kouah and I. Kitouni. 2019. Towards Fuzzy Partial Global Fault Diagnosis. In Proceedings of the International Conference on Information Technologies (ICIT'19), Saratov, Russia. Studies in Systems, Decision and Control, Olga Dolinina; Alexander Brovko; Vitaly Pechenkin; Alexey Lvov; Vadim Zhmud; Vladik Kreinovich, Ed. (February 2019), Springer, 406-420. DOI: https://doi.org/10.1007/978-3-030-12072-6_33.
APA :
Kouah, S. & Kitouni, I. (2019, February). Towards Fuzzy Partial Global Fault Diagnosis. In Proceedings of the International Conference on Information Technologies (ICIT'19), Saratov, Russia. In Olga Dolinina; Alexander Brovko; Vitaly Pechenkin; Alexey Lvov; Vadim Zhmud; Vladik Kreinovich (Ed.). Studies in Systems, Decision and Control, Springer, 406-420. DOI: https://doi.org/10.1007/978-3-030-12072-6_33
IEEE :
S. Kouah and I. Kitouni, "Towards Fuzzy Partial Global Fault Diagnosis". In Proceedings of the International Conference on Information Technologies (ICIT'19), Saratov, Russia. Studies in Systems, Decision and Control, Springer, pp. 406-420, February, 2019. DOI: https://doi.org/10.1007/978-3-030-12072-6_33.
BibTeX :
@inproceedings{misc-lab-219,
author = {Kouah, Sofia and Kitouni, Ilham},
title = {Towards Fuzzy Partial Global Fault Diagnosis},
booktitle = {International Conference on Information Technologies (ICIT'19)},
series = {Studies in Systems, Decision and Control},
location = {Saratov, Russia},
isbn = {978-3-030-12072-6},
pages = {406--420},
editor = {Dolinina, Olga and Brovko, Alexander and Pechenkin, Vitaly and Lvov, Alexey and Zhmud, Vadim and Kreinovich, Vladik},
publisher = {Springer},
year = {2019},
month = {February},
doi = {10.1007/978-3-030-12072-6\_33},
url = {https://link.springer.com/chapter/10.1007/978-3-030-12072-6\_33},
keywords = {Complex system, Diagnosis, Fuzzy logic, Internet of things}
}
RIS :
TY  - CONF
TI - Towards Fuzzy Partial Global Fault Diagnosis
AU - S. Kouah
AU - I. Kitouni
PY - 2019
BT - International Conference on Information Technologies (ICIT'19), Saratov, Russia
SN - 978-3-030-12072-6
SP - 406
EP - 420
ED - O. Dolinina
ED - A. Brovko
ED - V. Pechenkin
ED - A. Lvov
ED - V. Zhmud
ED - V. Kreinovich
PB - Springer
AB - Diagnosis aims at identifying a faulty system based on its behavior observations. It is widely emerged in altogether computer sciences fields, among others: aeronautics, space exploration, nuclear energy, process industries, manufacturing, healthcare, networking, automatism and many other control applications. Diagnosis involves distributed components with an uncertain global view. This paper intends to provide an efficient fuzzy based diagnosis mechanism. Such mechanism enables local hosts' diagnosis. These local decisions could be merged to provide the global diagnosis. The fuzziness choice is motivated by the fact of incomplete and uncertain system descriptions and observations. Also it is justified by the difficulties of obtaining a complete viewpoint of all system parts where the control is distributed. Our diagnosis mechanism, named FPGD for Fuzzy Partial Global Diagnosis consists of two main steps: Firstly, each remote control host detects and localizes abnormal behaviors which results on a local diagnosis. Each host proceeds by applying a recovery planned actions to maintain system functioning. Furthermore, such local diagnoses should be sent to the global part in order to be merged and analyzed, hence giving a precise and exhaustive global diagnosis. The automatic diagnosis reasoning is a fuzzy system; which based on fuzzy rules, handles incomplete information to deduce system malfunctioning.
KW - Complex system
KW - Diagnosis
KW - Fuzzy logic
KW - Internet of things
DO - 10.1007/978-3-030-12072-6_33
UR - https://link.springer.com/chapter/10.1007/978-3-030-12072-6_33
ID - misc-lab-219
ER -