Selection of efficient degradation features for rolling element bearing prognosis using Gaussian Process Regression method

Rolling Element Bearings are one of the most ubiquitous machine elements used in various machineries in the manufacturing industry. Prognosis and estimation of residual life of rolling element bearing are very important for efficient implementation of health monitoring and condition-based maintenance. In this paper, a rolling element bearing fault or degradation trend prediction is modeled using Gaussian Process Regression (GPR) method. Various vibration features based on signal complexity, namely Shannon entropy, permutation entropy, and approximate entropy are estimated to obtain the bearing degradation trend. When fault or degradation occurs in rolling element bearing, there is a subtle change in the dynamics of the system and subsequently, there are changes in the features extracted from the vibration signal. In this paper, a comparative analysis of various kernel functions of the GPR model is carried out using accuracy-based metrics. In addition, the combination of goodness of metric (monotonicity (Mon), robustness (Rob), and prognosability (Pro)), namely hybrid metric, is proposed to select the efficient bearing degradation trend of features. Further, the GPR at ARD exponential kernel has been employed to make the prognosis of degradation trend in bearings with a 95% confidence interval (CI). The proposed methodology is validated through a mathematical model of the simulated vibration signal. Finally, from the simulated and experimental data, it is demonstrated that the entropy features have better performance than the statistical features.