6533b7d7fe1ef96bd1267880
RESEARCH PRODUCT
Autoencoders and Recurrent Neural Networks Based Algorithm for Prognosis of Bearing Life
Kjell G. RobbersmyrHuynh Van KhangJagath Sri Lal Senanayakasubject
Electric motor021103 operations researchBearing (mechanical)Computer science020208 electrical & electronic engineeringFeature extraction0211 other engineering and technologies02 engineering and technologyBearing fault detectionAutoencoderlaw.inventionRecurrent neural networkTest caselaw0202 electrical engineering electronic engineering information engineeringPrognosticsAlgorithmdescription
Bearings are one of the most critical components in electric motors, gearboxes and wind turbines. Therefore, bearing fault detection and prognosis of remaining useful life are important to prevent productivity losses. In this study, a novel method is proposed for prognosis of bearing life using an autoencoder and recurrent neural networks-based prediction algorithm. Promising results have been obtained from the experimental data. A monotonic upward trend of the produced health indicator is obtained for all test cases, being one of critical indicators of a proper prognosis. The remaining useful life estimation is moderately accurate under a limited data.
| year | journal | country | edition | language |
|---|---|---|---|---|
| 2018-10-01 | 2018 21st International Conference on Electrical Machines and Systems (ICEMS) |