Gearbox fault detection using static data and adaptive neurofuzzy inference system

Mabrouka Baqqar, Van Tung Tran, Fengshou Gu, Andrew Ball

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

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Abstract

Condition monitoring of a gearbox is a crucial activity due to its importance in power transmission for many industrial applications. Thus, there has always been a constant pressure to improve measuring techniques and analytical tools for early detection of faults in gearboxes. This study forces to develop the gearbox monitoring methods using the operating parameters obtained from machine control processes rather than the traditional measurements such as vibration and acoustics. To monitor the gearbox conditions, an adaptive neuro-fuzzy inference system (ANFIS) is used to captures the nonlinear connections between the electrical motor current and control parameters such as load settings and temperatures. The predicted values generated by ANFIS model are then compared with the measured values to indicate the abnormal condition in gearbox. The experimental results show that ANFIS model is adequate and is able to serve as an efficient tool for gearbox condition monitoring and fault detection.
Original languageEnglish
Title of host publicationProceedings International Congress of Condition Monitoring and Diagnostic Engineering Management (COMADEM 2013)
Publication statusPublished - 2013
Externally publishedYes
EventInternational Congress of Condition Monitoring and Diagnostic Engineering Management, COMADEM 2013 - Helsinki, Finland
Duration: 11 Jun 201313 Jun 2013
http://maintworld.mycashflow.fi/product/5/comadem-2013-proceedings

Conference

ConferenceInternational Congress of Condition Monitoring and Diagnostic Engineering Management, COMADEM 2013
Abbreviated titleCOMADEM 2013
CountryFinland
CityHelsinki
Period11/06/1313/06/13
Internet address

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