Accurate remaining useful life (RUL) prediction of industrial system is critical to ensure smooth operation and its safety. Various prognostic methods have been developed but there still exist critical challenges for field applications. One challenge is the unhealth degradation exhibiting the change of state from those of normal degradation. Another is the prediction in the face of severe noise with limited data (i.e., early prediction) using empirical models. Final challenge is the prediction under varying operating conditions, which occurs in practice in various industrial applications. To overcome these challenges, this research proposes advanced prognostics methods with different recipes featured by high adaptability, physical constraints, and monotonic health indicator (HI). The developed methods are validated with specific case studies involved with the challenges.
How to Cite
prognostics, remaining useful life, Bayesian inference, state change, low-fidelity physical information, time-varying operating conditions
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.