Acoustic emission-based prognostics of slow rotating bearing using Bayesian techniques under dependent and independent samples

Abstract

This study develops a novel degradation assessment index (DAI) from acoustic emission signals obtained from slow rotating bearings and integrates the same into alternative Bayesian methods for the prediction of remaining useful life (RUL). The DAI is obtained by the integration of polynomial kernel principal component analysis (PKPCA), Gaussian mixture model (GMM), and exponentially weighted moving average (EWMA). The DAI is then used as inputs in several Bayesian regression models, such as the multilayer perceptron (MLP), radial basis function (RBF), Bayesian linear regression (BLR), Gaussian mixture regression (GMR), and the Gaussian process regression (GPR) for RUL prediction. The combination of the DAI with the GPR model, otherwise known as the DAIGPR gave the best prediction with the least error. The findings show that the GPR model is suitable and effective in the prediction of RUL of slow rotating bearings and robust to varying operating conditions. Further, the findings are also robust when the training and tests sets are obtained from dependent and independent samples. Therefore, the GPR model is found useful for monitoring the condition of machines in order to implement effective preventive rather than reactive maintenance, thereby maximizing safety and asset availability.