Large Data and AI Analysis Based Online Diagnosis System Application of Steel Ladle Slewing Bearing

Wei Hu, Fengshou Gu, Shiqi Chen

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

Abstract

Setting the default diagnosis and residual longevity of steel ladle turret bearing as the research subject, this article developed a large data and fusion of data mining with expert system based AI default diagnosis system, which has been successfully applicated in the default diagnosis of steel ladle turret bearing of a steel, saving tremendous time for steel mill’s decisive equipment maintenance by precisely predict the residual longevity of the equipment.

Original languageEnglish
Title of host publicationAdvances in Asset Management and Condition Monitoring, COMADEM 2019
EditorsAndrew Ball, Len Gelman, B.K.N. Rao
PublisherSpringer, Cham
Pages1519-1527
Number of pages9
Volume166
ISBN (Electronic)9783030577452
ISBN (Print)9783030577445
DOIs
Publication statusPublished - 28 Aug 2020
Event32nd International Congress and Exhibition on Conditioning Monitoring and Diagnostic Engineering Management Conference - University of Huddersfield, Huddersfield, United Kingdom
Duration: 3 Sep 20195 Sep 2019
Conference number: 32
http://www.comadem2019.com/ (Link to Conference Website)

Publication series

NameSmart Innovation, Systems and Technologies
PublisherSpringer
Volume166
ISSN (Print)2190-3018
ISSN (Electronic)2190-3026

Conference

Conference32nd International Congress and Exhibition on Conditioning Monitoring and Diagnostic Engineering Management Conference
Abbreviated titleCOMADEM 2019
CountryUnited Kingdom
CityHuddersfield
Period3/09/195/09/19
Internet address

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