Remaining Useful Life Prediction using Gaussian Process Regression Model

Abstract

This paper evaluates the merits of a multi-variable Gaussian process regression (GPR) model for remaining useful life (RUL) estimation. The paper presents an optimization method that trains the GPR model to find the best kernel type and hyper-parameter combination. Furthermore, the paper evaluates the performance of the GPR model for small training datasets and with a reduction (missing) of input features. A comparison is made to the multi-layer perceptron (MLP) neural network which forms the basis of deep learning models. To illustrate model performance, an air filter clogging RUL dataset is used. The performance results show that both GPR and MLP models have similar sensitivity to training set size but GPR also computes the uncertainty. Empirically, MLP is more robust to a test set with a missing input while the data suggests that the GPR performs better when the training data also did not include the same input.