A Data Mining based Approach for Electric Motor Anomaly Detection Applied on Vibration Data

Oche Alexander Egaji, Tobore Ekwevugbe, Mark Griffiths

Allbwn ymchwil: Pennod mewn Llyfr/Adroddiad/Trafodion CynhadleddCyfraniad i gynhadleddadolygiad gan gymheiriaid

Crynodeb

In many industrial settings, a significant amount of data is generated from electro-mechanical systems and stored, without processing to gain valuable insights that could enable optimised production while bringing down maintenance cost to its barest minimum. Data mining techniques offer potential solutions to address this concern. In this paper, anomaly detection techniques using machine learning models such as K-Nearest Neighbour (KNN), Support Vector Regression (SVR) and Random Forest (RF) have been applied to vibration sensor data for early fault detection of industrial electric motors. The models relied on vibration data collected from sensors mounted on four bearings. Initial results suggest that the RF model outperformed SVR and KNN for the data set analysed, and can be a candidate data mining technology to implement for condition monitoring of electro-mechanical systems.
Iaith wreiddiolSaesneg
TeitlProceedings of the World Conference on Smart Trends in Systems, Security and Sustainability, WS4 2020
GolygyddionXin-She Yang, Simon James Fong, Segundo Moises Toapanta, Ion Andronache, Niko Phillips
CyhoeddwrInstitute of Electrical and Electronics Engineers
Tudalennau330-334
Nifer y tudalennau5
ISBN (Electronig)978-1-7281-6823-4 , 978-1-7281-6824-1
Dynodwyr Gwrthrych Digidol (DOIs)
StatwsCyhoeddwyd - 7 Hyd 2020
Digwyddiad2020 Fourth World Conference on Smart Trends in Systems Security and Sustainability (WorldS4) (Online) - Holiday Inn London - Kensington Forum (Online), London, Y Deyrnas Unedig
Hyd: 27 Jul 202028 Jul 2020

Cyfres gyhoeddiadau

EnwProceedings of the World Conference on Smart Trends in Systems, Security and Sustainability, WS4 2020

Cynhadledd

Cynhadledd2020 Fourth World Conference on Smart Trends in Systems Security and Sustainability (WorldS4) (Online)
Teitl crynoWorldsS4
Gwlad/TiriogaethY Deyrnas Unedig
DinasLondon
Cyfnod27/07/2028/07/20

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