Remaining Useful Life Prediction via Computation of Physical and Material Properties
##plugins.themes.bootstrap3.article.main##
##plugins.themes.bootstrap3.article.sidebar##
Abstract
Remaining useful life (RUL) prediction is a crucial aspect of predictive maintenance, enabling industries to optimize asset longevity and minimize unexpected failures. RUL becomes increasingly difficult with system complexity. Such systems are built up of components exhibiting known material properties. When degradation occurs, these materials are predictably affected and can indicate the remaining life of the system, both directly and indirectly. Changes reflected from damage, ageing and wear are particularly detectable in stiffness of materials for example. This paper aims to utilize the physics and domain knowledge of systems along with material degradation indicators such as Young’s Modulus and the interdependencies between such metrics, to accomplish RUL with a deeper understanding of wear and fatigue as compared to purely data-driven methods. Experimental studies and computational simulations demonstrate the effectiveness of this approach, offering a novel perspective on predictive maintenance strategies.
How to Cite
##plugins.themes.bootstrap3.article.details##
Material science, RUL, Remaining useful life, Machine learning, Neural networks, State space, spring, suspension
Wang Y., Zhao Y., & Addepalli S., (2020). Remaining Useful Life Prediction using Deep Learning Approaches: A Review, Procedia Manufacturing. (Volume 49, Pages 81-88), doi:10.1016/2020.06.015
Liao J., Peng T., Xu Y., Gui G., Yang C., Yang C., Gui W., (2024). Task-orientated probabilistic damage model with interdependent degradation behaviors for RUL prediction of traction converter systems, Reliability Engineering & System Safety. (Volume 250), doi:10.1016/2024.110302
Li Z., Zheng H., Xiang X., Liu S., Wan Y., (2025). Remaining useful life prediction with limited run-tofailure data: A Bayesian ensemble approach combining mode-dependent RVM and similarity, ISA Transactions. (Volume 156, Pages 307-319), doi:10.1016/2024.11.023
Vennerød C. B., Kjærran A. and Bugge E. S., (2021). Long Short-term Memory RNN, arXiv, doi: 2105.06756
Mitici M., Pater I. d., Barros A., Zeng Z., (2023). Dynamic predictive maintenance for multiple components using data-driven probabilistic RUL prognostics: The case of turbofan engines, Reliability Engineering & System Safety, doi: 10.1016/2023.109199
Ott, L., Gräber, T., Unterreiner, M., Edelmann, J., Plöchl, M., (2024). Simulating Effects of Suspension Damper Degradation on Common Sensor Signals for Diagnosis Models in the Context of Condition-Based Maintenance. In: Mastinu, G., Braghin, F., Cheli, F., Corno, M., Savaresi, S.M. (eds) 16th International Symposium on Advanced Vehicle Control. AVEC 2024. Lecture Notes in Mechanical Engineering. Springer, Cham. https://doi.org/10.1007/978-3-031-70392-8_122
TSHGS. (2023) How Long Do Coil Springs Last. Available at: https://www.tshgcoilspring.com/resources/howlong-do-suspension-springs-last.html (Accessed: 27 May 2025).
Xia S. (2025) Why Is CNC Machining the Future of Aircraft Landing Gear Systems?. Available at: https://bccncmilling.com/why-is-cnc-machining-the-future-of-aircraft-landing-gear-systems/ (Accessed: 27 May 2025).
Zaman, Z., Apostolou, E. & Stecki, C. (2022) Predictive Maintenance for Safety-critical systems with a Digital Diagnostic Twin, International Conference on System Safety.
Rotable Repairs Ltd. (2025) Airbus A320. Available at: https://rotablerepairs.com/a320-airbus.html (Accessed: 9 May 2025).
Xingang S., Liangcai C., Guanhu W. & Lei L. (2020) A New Aircraft Taxiing Model Based on Filtering White Noise Method, IEEE Access, Volume 8. doi: 10.1109/ACCESS.2020.2964754, IEEE
Bardou N. & Owens D. (2014) Hard Landing, a Case Study for Crews and Maintenance Personnel, Safety First, Issue 17.
Airbus (2023). A320, AIRCRAFT CHARACTERISTICS AIRPORT AND MAINTENANCE PLANNING, AC. AIRBUS S.A.S. Customer Services, Technical Data Support and Services, 31707 Blagnac Cedex, FRANCE
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.