Bearing Fault Prediction Using Current Signature Analysis in Electric Water Pump
| 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.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.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.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 |