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Stages prediction of the remaining useful life of rolling bearing based on regularized extreme learning machine
- Source :
- Proceedings of the Institution of Mechanical Engineers, Part C: Journal of Mechanical Engineering Science. 235:6599-6610
- Publication Year :
- 2021
- Publisher :
- SAGE Publications, 2021.
-
Abstract
- The prediction of the remaining useful life (RUL) of rolling bearings is an important means to ensure the rotating machinery's safe operation. At present, most of the proposed methods use direct prediction based on bearing vibration signals, which not only have low prediction accuracy but also time-consuming. This paper proposes a staged prediction method, and the regularized learning machine (RELM) based on the proposed sensitive degradation feature is applied to predict RUL of the bearing with high accuracy and speed. Firstly, the relative root mean square value (RRMS) is used to divide the degradation stages of rolling bearings. Secondly, the RRMS indicator is used for multi-step time series prediction in the normal phase of the bearing. Thirdly, in the bearing's degradation stage, the Pearson Correlation Coefficient (PCC) Combined Entropy Weight Method (EWM) feature selection criterion is proposed to predict the RUL of the rolling bearing. Finally, the sensitive degradation feature of the bearing vibration signals is input into RELM to predict the RUL. The bearing data sets of PHM Challenging 2012 are used to verify the effectiveness of the proposed method. Three comparative experiments have been verified to prove the accuracy and rapidity of the proposed method in time series forecasting.
- Subjects :
- 0209 industrial biotechnology
Bearing (mechanical)
Computer science
Mechanical Engineering
020208 electrical & electronic engineering
02 engineering and technology
law.invention
Reliability engineering
020901 industrial engineering & automation
Safe operation
law
0202 electrical engineering, electronic engineering, information engineering
Time series
Extreme learning machine
Subjects
Details
- ISSN :
- 20412983 and 09544062
- Volume :
- 235
- Database :
- OpenAIRE
- Journal :
- Proceedings of the Institution of Mechanical Engineers, Part C: Journal of Mechanical Engineering Science
- Accession number :
- edsair.doi...........e62ac49ff3d361d46646e96ba6377080