Please use this identifier to cite or link to this item: http://hdl.handle.net/10397/81692
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dc.contributorDepartment of Mechanical Engineering-
dc.creatorXu, S-
dc.date.accessioned2020-02-10T12:28:40Z-
dc.date.available2020-02-10T12:28:40Z-
dc.identifier.issn1742-6588-
dc.identifier.urihttp://hdl.handle.net/10397/81692-
dc.descriptionInternational Symposium on Power Electronics and Control Engineering (ISPECE), Xi'an University of Technology, Xi'an, People's Republic of China, Dec 28-30, 2018en_US
dc.language.isoenen_US
dc.publisherInstitute of Physics Publishingen_US
dc.rightsContent from this work may be used under the terms of the Creative Commons Attribution 3.0 licence (https://creativecommons.org/licenses/by/3.0/). Any further distribution of this work must maintain attribution to the author(s) and the title of the work, journal citation and DOI. Published under licence by IOP Publishing Ltd.en_US
dc.rightsThe following publication Xu, S. C. (2019). A survey of knowledge-based intelligent fault diagnosis techniques. Journal of Physics. Conference Series, 1187, 32006, 1-6 is available at https://dx.doi.org/10.1088/1742-6596/1187/3/032006en_US
dc.titleA survey of knowledge-based intelligent fault diagnosis techniquesen_US
dc.typeConference Paperen_US
dc.identifier.spage1-
dc.identifier.epage6-
dc.identifier.volume1187-
dc.identifier.doi10.1088/1742-6596/1187/3/032006-
dcterms.abstractWith the development of information technologies, more and more real-time data can be obtained from production and operation process. Thus, how to extract effective information from these massive data, so as to carry out in-depth statistics and mining of faults, and gradually explore the faults laws and causes are crucial for intelligent factories. In recent years, a variety of statistical learning and data analysis methods have been used in fault diagnosis. Due to the complex structure, multi-source failure and suddenness of the industrial production system, the combination of empirical knowledge and mechanism principles can solve various fault problems. This paper summarizes several commonly used fault diagnosis methods, and focuses on knowledge-based intelligent fault diagnosis, including first-order logic knowledge representation method, production knowledge representation method, framework knowledge representation method, object-oriented knowledge representation method and Semantic-based knowledge representation methods.-
dcterms.accessRightsopen accessen_US
dcterms.bibliographicCitationJournal of physics. Conference series, 2019, v. 1187, 32006, p. 1-6-
dcterms.isPartOfJournal of physics. Conference series-
dcterms.issued2019-
dc.identifier.isiWOS:000481622600075-
dc.relation.conferenceInternational Symposium on Power Electronics and Control Engineering [ISPECE]-
dc.identifier.eissn1742-6596-
dc.identifier.artn32006-
dc.description.validate202002 bcrc-
dc.description.oaVersion of Recorden_US
dc.identifier.FolderNumberOA_Scopus/WOSen_US
dc.description.pubStatusPublisheden_US
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