The trend in diagnostics and prognostics for PHM is shifting toward explainable data-driven models. However, complex engineered systems are typically challenging to develop entirely explainable models for, whether they are grounded in physics or data-driven techniques. Consequently, the development of machine learning models, including hybrid variants capable of both interpolation and extrapolation, holds significant promise for enhancing the practicality of system simulation, analysis, modeling, and control in industry. The primary objective of this data challenge is to encourage contributions that expand the scope of model generalization beyond the training domain. The second aim of this data challenge is to quantify model uncertainty and methods to incorporate it into predictions. For most PHM tasks, clear guidance of the required action is ideal. To issue a definitive guidance to end users, it is useful to quantify uncertainty for the whole model. This data challenge addresses both estimation and uncertainty.
How to Cite
Data Challenge, Results
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.