Physics Informed Machine Learning Techniques for Condition Monitoring of Drivetrains
##plugins.themes.bootstrap3.article.main##
##plugins.themes.bootstrap3.article.sidebar##
Abstract
This work highlights the main limitations of current condition monitoring approaches and the need for methodologies capable of handling limited data while ensuring physical consistency. PINNs have been introduced as a suitable framework for addressing these challenges. The proposed hybrid approach, together with the role of contact mechanics and tribology, has been outlined as a central element of the research. Additionally, a dedicated test rig has been introduced to support the validation of the proposed methodology under controlled conditions. Finally, a structured research plan has been defined, with the objective of developing a robust framework for fault detection and prognostics in drivetrains, contributing to the advancement of PHM.
How to Cite
##plugins.themes.bootstrap3.article.details##
Machine Learning, Drivetrains, Condition Monitoring, Physics-Informed
Fu, X., Fang, Y., Xu, Y., Xu, H., Ma, G., & Peng, N. (2024). Current status of research on fault diagnosis using machine learning for gear transmission systems. Machines, 12(10). doi: 10.3390/machines12100679
Karniadakis, G. E., Kevrekidis, I. G., Lu, L., Perdikaris, P., Wang, S., & Yang, L. (2021). Physics-informed machine learning. Nature Reviews Physics, 3. doi: 10.1038/s42254-021-00314-5
Kim, S., Choi, J.-H., & Kim, N. H. (2022). Datadriven prognostics with low-fidelity physical information for digital twin: physics-informed neural network. Advanced Engineering Informatics, 54. doi: 10.1016/j.aei.2022.101803
Lei, Y., Yang, B., Jiang, X., Jia, F., Li, N., & Nandi, A. K. (2020). Applications of machine learning to machine fault diagnosis: A review and roadmap. Mechanical Systems and Signal Processing, 138. doi: 10.1016/j.ymssp.2019.106587
Ni, Q., Ji, J. C., Halkon, B., Feng, K., & Nandi, A. K. (2023). Physics-informed residual network (piresnet) for rolling element bearing fault diagnostics. Mechanical Systems and Signal Processing, 198. doi: 10.1016/j.ymssp.2023.110449
Shen, S., Lu, H., Sadoughi, M., Hu, C., Nemani, V., Thelen, A., . . . Kenny, S. (2024). M-band wavelet network for machine anomaly detection from a frequency perspective. Mechanical Systems and Signal Processing, 217. doi: 10.1016/j.ymssp.2024.111489
Yucesan, Y. A., & Viana, F. A. C. (2020). A physics-informed neural network for wind turbine main bearing fatigue. International Journal of Prognostics and Health Management, 11(1). doi: 10.36001/ijphm.2020.v11i1.2594
Zhang, X., Gong, J.-G., & Xuan, F.-Z. (2021). A physics-informed neural network for creep-fatigue life prediction of components at elevated temperatures. Engineering Fracture Mechanics, 258. doi: 10.1016/j.engfracmech.2021.108130
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.