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- Title
Degradation state prediction of rolling bearings using ARX-Laguerre model and genetic algorithms.
- Authors
Najeh, Taoufik; Lundberg, Jan
- Abstract
This study is motivated by the need for a new advanced vibration-based bearing monitoring approach. The ARX-Laguerre model (autoregressive with exogenous) and genetic algorithms (GAs) use collected vibration data to estimate a bearing's remaining useful life (RUL). The concept is based on the actual running conditions of the bearing combined with a new linear ARX-Laguerre representation. The proposed model exploits the vibration and force measurements to reconstruct the Laguerre filter outputs; the dimensionality reduction of the model is subject to an optimal choice of Laguerre poles which is performed using GAs. The paper explains the test rig, data collection, approach, and results. So far and compared to classic methods, the proposed model is effective in tracking the evolution of the bearing's health state and accurately estimates the bearing's RUL. As long as the collected data are relevant to the real health state of the bearing, it is possible to estimate the bearing's lifetime under different operating conditions.
- Subjects
ROLLER bearings; GENETIC models; VIBRATION measurements; GENETIC algorithms; ACQUISITION of data; FORECASTING; TRACKING algorithms
- Publication
International Journal of Advanced Manufacturing Technology, 2021, Vol 112, Issue 3/4, p1077
- ISSN
0268-3768
- Publication type
Article
- DOI
10.1007/s00170-020-06416-1