Remaining Useful Life Prediction of Rolling Element Bearings Using Supervised Machine Learning

Xiaochuan Li,Suliman Shanbr,Faris Elasha,David Mba

doi:10.3390/en12142705

Abstract

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.

Highlights

Rolling element bearings are critical components in rotating machinery
This study aims to ascertain the feasibility of using Artificial neural network (ANN) models to estimate the remaining useful life (RUL) of rolling element bearings, and to explore the feasibility of combining regression models with
The accelerated life test was performed on the bearing to obtain vibration measurements during conditions and the stochastic nature of the degradation processes, the raw condition indicators cannot the bearing’s entire lifecycle from beginning to final failure

Summary

Introduction

Rolling element bearings are critical components in rotating machinery. These bearings generally operate under adverse conditions, making performance degradation unavoidable. Such degradation, if left unattended, can cause failure or breakdown of the entire system. The purpose of prognostics is to use prediction techniques to predict the remaining useful life (RUL) of a system and its constituent components based upon historical information, current usage and future operating conditions so as to prevent the catastrophe from happening [1]. Future operating condition refers to operational and environmental factors that could affect the future status of the system. Such information can be obtained based on expert opinion or by looking at the production plans. Prognostics offers several benefits, including [2,3]:

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Conclusion