Knowledge Modeling of Fault Diagnosis for Rotating Machinery Based on Ontology

Rong Chen; Zude Zhou; Quan Liu; Yuanyuan Zhao; Junwei Yan; Qin Wei; Duc Pham

doi:10.1109/INDIN.2015.7281880

Knowledge Modeling of Fault Diagnosis for Rotating Machinery Based on Ontology

Rong Chen, Zude Zhou, Quan Liu, Yuanyuan Zhao, Junwei Yan, Qin Wei, Duc Pham

Mechanical Engineering

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

5 Citations (Scopus)

379 Downloads (Pure)

Abstract

For those shortcomings of the current methods of fault diagnosis knowledge representation, it is necessary to use an efficient knowledge model to improve the accuracy of fault diagnosis and realize the reusing and sharing of machinery fault knowledge. In this paper, an ontology-based fault diagnosis model is established. Focusing on fault diagnosis of rotating machinery, the domain-ontology knowledge base and structure definition of the fault diagnosis are demonstrated in detail. The protégé is used to construct the model of ontology-based fault diagnosis. Further more, on that basis, rules are added and Jena is used to realize the knowledge reasoning. The result indicate that the model of fault diagnosis based on ontology is intuitive and efficiency.

Original language	English
Title of host publication	IEEE 13th International Conference on Industrial Informatics (INDIN 2015)
Publisher	Institute of Electrical and Electronics Engineers (IEEE)
ISBN (Print)	978-1-4799-6649-3
DOIs	https://doi.org/10.1109/INDIN.2015.7281880
Publication status	Published - 2015
Event	2015 IEEE 13th International Conference on Industrial Informatics - Cambridge, United Kingdom Duration: 22 Jun 2015 → 24 Jun 2015

Conference

Conference	2015 IEEE 13th International Conference on Industrial Informatics
Country/Territory	United Kingdom
City	Cambridge
Period	22/06/15 → 24/06/15

Keywords

Ontology
Fault diagnosis
Rotating machinery
Knowledge
Model

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10.1109/INDIN.2015.7281880

Chen_et_al_Knowledge_Modeling_INDIN15
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Accepted author manuscript, 324 KBLicence: None: All rights reserved

Cite this

@inproceedings{3a70e8328d194c59902ad762c7344c6b,

title = "Knowledge Modeling of Fault Diagnosis for Rotating Machinery Based on Ontology",

abstract = "For those shortcomings of the current methods of fault diagnosis knowledge representation, it is necessary to use an efficient knowledge model to improve the accuracy of fault diagnosis and realize the reusing and sharing of machinery fault knowledge. In this paper, an ontology-based fault diagnosis model is established. Focusing on fault diagnosis of rotating machinery, the domain-ontology knowledge base and structure definition of the fault diagnosis are demonstrated in detail. The prot{\'e}g{\'e} is used to construct the model of ontology-based fault diagnosis. Further more, on that basis, rules are added and Jena is used to realize the knowledge reasoning. The result indicate that the model of fault diagnosis based on ontology is intuitive and efficiency.",

keywords = "Ontology, Fault diagnosis, Rotating machinery, Knowledge, Model",

author = "Rong Chen and Zude Zhou and Quan Liu and Yuanyuan Zhao and Junwei Yan and Qin Wei and Duc Pham",

year = "2015",

doi = "10.1109/INDIN.2015.7281880",

language = "English",

isbn = "978-1-4799-6649-3",

booktitle = "IEEE 13th International Conference on Industrial Informatics (INDIN 2015)",

publisher = "Institute of Electrical and Electronics Engineers (IEEE)",

note = "2015 IEEE 13th International Conference on Industrial Informatics ; Conference date: 22-06-2015 Through 24-06-2015",

}

Chen, R, Zhou, Z, Liu, Q, Zhao, Y, Yan, J, Wei, Q & Pham, D 2015, Knowledge Modeling of Fault Diagnosis for Rotating Machinery Based on Ontology. in IEEE 13th International Conference on Industrial Informatics (INDIN 2015). Institute of Electrical and Electronics Engineers (IEEE), 2015 IEEE 13th International Conference on Industrial Informatics, Cambridge, United Kingdom, 22/06/15. https://doi.org/10.1109/INDIN.2015.7281880

TY - GEN

T1 - Knowledge Modeling of Fault Diagnosis for Rotating Machinery Based on Ontology

AU - Chen, Rong

AU - Zhou, Zude

AU - Liu, Quan

AU - Zhao, Yuanyuan

AU - Yan, Junwei

AU - Wei, Qin

AU - Pham, Duc

PY - 2015

Y1 - 2015

N2 - For those shortcomings of the current methods of fault diagnosis knowledge representation, it is necessary to use an efficient knowledge model to improve the accuracy of fault diagnosis and realize the reusing and sharing of machinery fault knowledge. In this paper, an ontology-based fault diagnosis model is established. Focusing on fault diagnosis of rotating machinery, the domain-ontology knowledge base and structure definition of the fault diagnosis are demonstrated in detail. The protégé is used to construct the model of ontology-based fault diagnosis. Further more, on that basis, rules are added and Jena is used to realize the knowledge reasoning. The result indicate that the model of fault diagnosis based on ontology is intuitive and efficiency.

AB - For those shortcomings of the current methods of fault diagnosis knowledge representation, it is necessary to use an efficient knowledge model to improve the accuracy of fault diagnosis and realize the reusing and sharing of machinery fault knowledge. In this paper, an ontology-based fault diagnosis model is established. Focusing on fault diagnosis of rotating machinery, the domain-ontology knowledge base and structure definition of the fault diagnosis are demonstrated in detail. The protégé is used to construct the model of ontology-based fault diagnosis. Further more, on that basis, rules are added and Jena is used to realize the knowledge reasoning. The result indicate that the model of fault diagnosis based on ontology is intuitive and efficiency.

KW - Ontology

KW - Fault diagnosis

KW - Rotating machinery

KW - Knowledge

KW - Model

U2 - 10.1109/INDIN.2015.7281880

DO - 10.1109/INDIN.2015.7281880

M3 - Conference contribution

SN - 978-1-4799-6649-3

BT - IEEE 13th International Conference on Industrial Informatics (INDIN 2015)

PB - Institute of Electrical and Electronics Engineers (IEEE)

T2 - 2015 IEEE 13th International Conference on Industrial Informatics

Y2 - 22 June 2015 through 24 June 2015

ER -

Knowledge Modeling of Fault Diagnosis for Rotating Machinery Based on Ontology

Abstract

Conference

Keywords

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