Machine learning based diagnosis engines require large data sets for training. When experimental data is insucient, system models can be used to supplement the data. Such models are typically simplified and imprecise, hence with some degree of uncertainty. In this paper we show how to deal with uncertainty in synthetic training data. The data is produced using a model with uncertainties. The uncertainties originate from inaccurate parameter values or parameters that take dierent values based on the mode of operation. We demonstrate how techniques from the uncertainty quantification field can be used to reduce the numerical complexity of the training algorithm. In particular, we use generalize polynomial chaos to eciently approximate the loss function. In addition, we present a neural network architecture specifically designed to deal with uncertainties in the training data. As an illustrative example, we show how our approach can be used to detect faults in an elevator system.
How to Cite
diagnosis, uncertainty quantification, neural networks, synthetic data
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.