A Deep Learning Approach to Prognostics of Rolling Element Bearings
- Authors
- Hur, Jang-Wook; Akpudo, Ugochukwu Ejike
- Issue Date
- 2020
- Publisher
- UNIV TUN HUSSEIN ONN MALAYSIA
- Keywords
- Bearing Degradation; Long short-term memory; Feature Extraction; Prognostics; Degradation assessment
- Citation
- INTERNATIONAL JOURNAL OF INTEGRATED ENGINEERING, v.12, no.3, pp.178 - 186
- Journal Title
- INTERNATIONAL JOURNAL OF INTEGRATED ENGINEERING
- Volume
- 12
- Number
- 3
- Start Page
- 178
- End Page
- 186
- URI
- https://scholarworks.bwise.kr/kumoh/handle/2020.sw.kumoh/19082
- DOI
- 10.30880/ijie.2020.12.03.021
- ISSN
- 2229-838X
- Abstract
- The use of deep learning approaches for prognostics and remaining useful life predictions have become obviously prevalent. Artificial recurrent neural networks like the long short-term memory are popularly employed for forecasting, prognostics and health management practices, and in other fields of life. As an unsupervised learning approach, the efficiency of the long short-term memory for time-series predictive purposes is quite remarkable in contrast to standard feedforward neural networks. Virtually all mechanical systems consist mostly of rotating components which are by nature, prone to degradation/failure from known and uncertain causes. As a result, condition monitoring of these rolling element bearings is necessary in order to carry out prognostics and make necessary life predictions which guide safe and cost-effective decision making. Several studies have been conducted on effective approaches and methods for accurate prognostics of rolling element bearings; however, this paper presents a case study on rolling element bearing prognostics and degradation performance using an LSTM model.
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