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Knowledge discovery for gearbox fault diagnosis using flow graph

  • School of Mechatronics Engineering, Harbin Institute of Technology

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

Abstract

It is difficult to discover gearbox diagnosis knowledge while diagnosis information is incomplete. To overcome this problem, a novel knowledge discovery method for gearbox fault diagnosis using flow graph (FG) is presented. In this method, FG is constructed in terms of incomplete fault decision table. The relationship among fault attributes can be represented in a graphical manner. Assignment reduction algorithm is used to remove irrelevant and redundant nodes. Therefore, FG after reduction is acquired according to the minimal reducts. To validate the performance of this method, a gearbox fault diagnosis experiment was performed. The experimental studies indicate the proposed method can be utilized to directly discover gearbox diagnosis knowledge from incomplete information in a graphical and intuitive manner.

Original languageEnglish
Title of host publicationProceedings of 2016 5th International Conference on Computer Science and Network Technology, ICCSNT 2016
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages196-200
Number of pages5
ISBN (Electronic)9781509021284
DOIs
StatePublished - 16 Oct 2017
Externally publishedYes
Event5th International Conference on Computer Science and Network Technology, ICCSNT 2016 - Changchun, China
Duration: 10 Dec 201611 Dec 2016

Publication series

NameProceedings of 2016 5th International Conference on Computer Science and Network Technology, ICCSNT 2016

Conference

Conference5th International Conference on Computer Science and Network Technology, ICCSNT 2016
Country/TerritoryChina
CityChangchun
Period10/12/1611/12/16

Keywords

  • Knowledge discovery
  • assignment reduction
  • fault diagnosis
  • flow graph
  • gearbox

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