Artificial Intelligence and Physics-of-Failure Prognostics Methods for Aircraft Maintenance and Reliability
##plugins.themes.bootstrap3.article.main##
##plugins.themes.bootstrap3.article.sidebar##
Abstract
In recent years, Physics Informed Neural Networks (PINNs) have shown promising results in Prognostics and Health Management (PHM). A PINN relies on the physical bias inside a Neural Network (NN) to predict engineering equipment’s Remaining Useful Life (RUL). This physical integration can assume three forms: observational bias, inductive bias, or learning bias. This doctoral thesis will focus on inductive bias by creating physical layers inside an NN. This research aims to create a Convolutional Neural Network (CNN) with a modular structure composed of different “lego” layers. Each layer will have a different structure and goal. We will also study how the CNN structure can be modified and optimized according to the case study.
How to Cite
##plugins.themes.bootstrap3.article.details##
PINNs, physical layers, CNN, Machine learning, CNN structure
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.