Bearing Fault Prediction Using Current Signature Analysis in Electric Water Pump

Yuvaraj, M.; Elzhiloan, P.; Thiruvaran, T.; Aravinthan, V.; Thanatheepan, B.

dc.contributor.author	Yuvaraj, M.
dc.contributor.author	Elzhiloan, P.
dc.contributor.author	Thiruvaran, T.
dc.contributor.author	Aravinthan, V.
dc.contributor.author	Thanatheepan, B.
dc.date.accessioned	2021-03-19T02:55:49Z
dc.date.accessioned	2022-06-27T10:02:15Z
dc.date.available	2021-03-19T02:55:49Z
dc.date.available	2022-06-27T10:02:15Z
dc.date.issued	2018
dc.identifier.citation	Yuvaraj, M., Elzhiloan, P., Thiruvaran, T., Aravinthan, V., & Thanatheepan, B. (2018, December). Bearing Fault Prediction Using Current Signature Analysis in Electric Water Pump. In 2018 IEEE International Conference on Information and Automation for Sustainability (ICIAfS) (pp. 1-5). IEEE.	en_US
dc.identifier.uri	http://repo.lib.jfn.ac.lk/ujrr/handle/123456789/2089
dc.description.abstract	This paper present an initial attempt to develop a simple algorithm for a device to predict the bearing fault in electric water pump using current signature. Bearing faults cause variations in the physical air gap of the rotating machine. It can modulate the air gap flux density and may vary the magnitude of harmonics of stator current. The current signatures has been collected for various fault bearings. Magnitude features has been extracted from harmonics of electrical current. These features have been used to build prediction models using SVM classifier. Maximum accuracy of 64.7% was achieved.	en_US
dc.language.iso	en	en_US
dc.publisher	IEEE	en_US
dc.subject	Bearings	en_US
dc.subject	Current signature analysis	en_US
dc.title	Bearing Fault Prediction Using Current Signature Analysis in Electric Water Pump	en_US
dc.type	Article	en_US