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.
