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

Research output: Chapter in Book/Report/Conference proceedingConference contribution

5 Citations (Scopus)
310 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 languageEnglish
Title of host publicationIEEE 13th International Conference on Industrial Informatics (INDIN 2015)
PublisherInstitute of Electrical and Electronics Engineers (IEEE)
ISBN (Print)978-1-4799-6649-3
DOIs
Publication statusPublished - 2015
Event2015 IEEE 13th International Conference on Industrial Informatics - Cambridge, United Kingdom
Duration: 22 Jun 201524 Jun 2015

Conference

Conference2015 IEEE 13th International Conference on Industrial Informatics
Country/TerritoryUnited Kingdom
CityCambridge
Period22/06/1524/06/15

Keywords

  • Ontology
  • Fault diagnosis
  • Rotating machinery
  • Knowledge
  • Model

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