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Stefan cel Mare
University of Suceava
Faculty of Electrical Engineering and
Computer Science
13, Universitatii Street
Suceava - 720229
ROMANIA

Print ISSN: 1582-7445
Online ISSN: 1844-7600
WorldCat: 643243560
doi: 10.4316/AECE


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  4/2015 - 15
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 HIGH-IMPACT PAPER 

Fuzzy Ontology Reasoning for Power Transformer Fault Diagnosis

SAMIRMI, F. D. See more information about SAMIRMI, F. D. on SCOPUS See more information about SAMIRMI, F. D. on IEEExplore See more information about SAMIRMI, F. D. on Web of Science, TANG, W. See more information about  TANG, W. on SCOPUS See more information about  TANG, W. on SCOPUS See more information about TANG, W. on Web of Science, WU, Q. See more information about WU, Q. on SCOPUS See more information about WU, Q. on SCOPUS See more information about WU, Q. on Web of Science
 
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Download PDF pdficon (1,274 KB) | Citation | Downloads: 1,034 | Views: 3,539

Author keywords
fault diagnosis, ontology, fuzzy sets, multi-agent systems, power transformer

References keywords
power(13), systems(12), fuzzy(8), agent(8), ontology(7), multi(6), diagnosis(6), transformers(5), system(5), fault(5)
Blue keywords are present in both the references section and the paper title.

About this article
Date of Publication: 2015-11-30
Volume 15, Issue 4, Year 2015, On page(s): 107 - 114
ISSN: 1582-7445, e-ISSN: 1844-7600
Digital Object Identifier: 10.4316/AECE.2015.04015
Web of Science Accession Number: 000368499800014
SCOPUS ID: 84949971675

Abstract
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This paper presents a novel fuzzy ontology reasoner for power transformer fault diagnosis under a multi-agent framework. The developed ontology provides a comprehensive knowledge base as part of a multi-agent system to enable imprecision reasoning. It is the first time that a fuzzy ontology model is developed for accurate power transformer fault diagnosis. It aims to develop an improved ontology model for transformer fault diagnosis by applying the fuzzy ontology. The proposed technique deals with the imprecision situation using the fuzzy ontology, in order to build an ontology-based knowledge representation for accurate power transformer fault diagnosis. The proposed system is tested with actual transformer online data to demonstrate the functionality of the developed fuzzy ontology, which can identify the faults that are unidentifiable using a basic ontology model, and this can significantly improve the overall accuracy for transformer fault diagnosis under a multi-agent framework.


References | Cited By

Cited-By Clarivate Web of Science

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Cited-By SCOPUS

SCOPUS® Times Cited: 15
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Cited-By CrossRef

[1] Ontology-Based Method for Fault Diagnosis of Loaders, Xu, Feixiang, Liu, Xinhui, Chen, Wei, Zhou, Chen, Cao, Bingwei, Sensors, ISSN 1424-8220, Issue 3, Volume 18, 2018.
Digital Object Identifier: 10.3390/s18030729
[CrossRef]

[2] Distributed Knowledge Inference Framework for Intelligent Fault Diagnosis in IIoT Systems, Chi, Yuanfang, Wang, Z. Jane, Leung, Victor C. M., IEEE Transactions on Network Science and Engineering, ISSN 2327-4697, Issue 5, Volume 9, 2022.
Digital Object Identifier: 10.1109/TNSE.2021.3128171
[CrossRef]

[3] Vibration Based Broken Bar Detection in Induction Machine for Low Load Conditions, MATIC, D., KANOVIC, Z., Advances in Electrical and Computer Engineering, ISSN 1582-7445, Issue 1, Volume 17, 2017.
Digital Object Identifier: 10.4316/AECE.2017.01007
[CrossRef] [Full text]

[4] Knowledge-Based Fault Diagnosis in Industrial Internet of Things: A Survey, Chi, Yuanfang, Dong, Yanjie, Wang, Z. Jane, Yu, F. Richard, Leung, Victor C. M., IEEE Internet of Things Journal, ISSN 2327-4662, Issue 15, Volume 9, 2022.
Digital Object Identifier: 10.1109/JIOT.2022.3163606
[CrossRef]

[5] Engineering Support for Handling Controller Conflicts in Energy Storage Systems Applications, Zanabria, Claudia, Tayyebi, Ali, Pröstl Andrén, Filip, Kathan, Johannes, Strasser, Thomas, Energies, ISSN 1996-1073, Issue 10, Volume 10, 2017.
Digital Object Identifier: 10.3390/en10101595
[CrossRef]

[6] Rapid Prototyping of Multi-Functional Battery Energy Storage System Applications, Zanabria, Claudia, Andrén, Filip, Kathan, Johannes, Strasser, Thomas, Applied Sciences, ISSN 2076-3417, Issue 8, Volume 8, 2018.
Digital Object Identifier: 10.3390/app8081326
[CrossRef]

[7] An Ontology-Based Cognitive Model for Faults Diagnosis of Hazardous Chemical Storage Devices, Feng, Lixiao, Chen, Guorong, Peng, Jun, International Journal of Cognitive Informatics and Natural Intelligence, ISSN 1557-3958, Issue 4, Volume 12, 2018.
Digital Object Identifier: 10.4018/IJCINI.2018100106
[CrossRef]

[8] Ontology-based Fault Diagnosis, Liu, Bin, Wu, Junfeng, Yao, Li, Ding, Zheyuan, Proceedings of the 11th International Conference on Computer Modeling and Simulation, ISBN 9781450366199, 2019.
Digital Object Identifier: 10.1145/3307363.3307381
[CrossRef]

[9] Ontology Faults Diagnosis Model for the Hazardous Chemical Storage Device, Feng, Lixiao, Chen, Guorong, Chen, Chengyuan, Chen, Liukui, Peng, Jun, 2018 IEEE 17th International Conference on Cognitive Informatics & Cognitive Computing (ICCI*CC), ISBN 978-1-5386-3360-1, 2018.
Digital Object Identifier: 10.1109/ICCI-CC.2018.8482025
[CrossRef]

[10] Transformer breather thermal image decomposition for fault diagnosis, Vidhya, R, Ranjan, P.Vanaja, Shanker, N.R., 2021 7th International Conference on Electrical Energy Systems (ICEES), ISBN 978-1-7281-7612-3, 2021.
Digital Object Identifier: 10.1109/ICEES51510.2021.9383639
[CrossRef]

[11] System for monitoring of hot spot temperature of power transformer windings using fiber optic sensors, Kalman Filter and SCADA integration, Nicola, Marcel, Nicola, Claudiu-Ionel, Dutta, Marian, Sacerdotianu, Dumitru, Hurezeanu, Iulian, 2018 International Conference on Development and Application Systems (DAS), ISBN 978-1-5386-1493-8, 2018.
Digital Object Identifier: 10.1109/DAAS.2018.8396079
[CrossRef]

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