Components of rotating machines, such as shafts, bearings and gears are subject to performance degradation, which if left unattended could lead to failure or breakdown of the entire system. Analyzing condition monitoring data, implementing diagnostic techniques and using machinery prognostic algorithms will bring about accurate estimation of the remaining life and possible failures that may occur. This paper proposes a combination of two supervised machine learning techniques; namely, the regression model and multilayer artificial neural network model, to predict the remaining useful life of rolling element bearings. Root mean square and Kurtosis were analyzed to define the bearing failure stages. The proposed methodology was validated through two case studies involving vibration measurements of an operational wind turbine gearbox and a split cylindrical roller bearing in a test rig.
Bibliographical noteThis is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
- vibration measurement
- regression model
- artificial neural network
- rolling element bearing
- remaining useful life
Li, X., Elasha, F., Shanbr, S., & Mba, D. (2019). Remaining Useful Life Prediction of Rolling Element Bearings Using Supervised Machine Learning. Energies, 12(14), 2705. https://doi.org/10.3390/en12142705