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.
How to Cite
Gaussian Process Regression, RUL, Neural Networks, Data-driven
This work is licensed under a Creative Commons Attribution 3.0 Unported License.
The Prognostic and Health Management Society advocates open-access to scientific data and uses a Creative Commons license for publishing and distributing any papers. A Creative Commons license does not relinquish the author’s copyright; rather it allows them to share some of their rights with any member of the public under certain conditions whilst enjoying full legal protection. By submitting an article to the International Conference of the Prognostics and Health Management Society, the authors agree to be bound by the associated terms and conditions including the following:
As the author, you retain the copyright to your Work. By submitting your Work, you are granting anybody the right to copy, distribute and transmit your Work and to adapt your Work with proper attribution under the terms of the Creative Commons Attribution 3.0 United States license. You assign rights to the Prognostics and Health Management Society to publish and disseminate your Work through electronic and print media if it is accepted for publication. A license note citing the Creative Commons Attribution 3.0 United States License as shown below needs to be placed in the footnote on the first page of the article.
First Author et al. This is an open-access article distributed under the terms of the Creative Commons Attribution 3.0 United States License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.