Wind Energy Science
Wind Energy Science
WES
Articles | Volume 11, issue 3
Articles | Volume 11, issue 3
https://doi.org/10.5194/wes-11-737-2026
© Author(s) 2026. This work is distributed under
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/wes-11-737-2026
© Author(s) 2026. This work is distributed under
the Creative Commons Attribution 4.0 License.
Articles | Volume 11, issue 3
Research article
 | 
04 Mar 2026
Research article |  | 04 Mar 2026

Gearbox bearing crack growth prognostics and uncertainty quantification with physics-informed machine learning

Mario De Florio, Gabriel Appleby, Jonathan Keller, Ali Eftekhari Milani, Donatella Zappalá, and Shawn Sheng

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Cited articles

Ahmadi Daryakenari, N., De Florio, M., Shukla, K., and Karniadakis, G. E.: AI-Aristotle: A physics-informed framework for systems biology gray-box identification, PLOS Computational Biology, 20, e1011916, https://doi.org/10.1371/journal.pcbi.1011916, 2024. a, b
Bechhoefer, E. and Dubé, M.: Contending Remaining Useful Life Algorithms, in: Annual Conference of the PHM Society, vol. 12, 9–9, https://doi.org/10.36001/phmconf.2020.v12i1.1274, 2020. a, b, c, d, e, f, g, h, i
Bechhoefer, E., Xiao, L., and Zhang, X.: Remaining Useful Life Calculation of a Component using Hybrid Fatigue Crack Model, in: Annual Conference of the PHM Society, vol. 13, https://doi.org/10.36001/phmconf.2021.v13i1.3062, 2021. a
Beer, F., Johnston, E., and DeWolf, J.: Mechanics of materials, 5th SI Edition, Stress, 1, 1–12, 1999. a, b
Cai, S., Mao, Z., Wang, Z., Yin, M., and Karniadakis, G. E.: Physics-informed neural networks (PINNs) for fluid mechanics: A review, Acta Mechanica Sinica, 37, 1727–1738, https://doi.org/10.1007/s10409-021-01148-1, 2021. a
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We developed a new method to predict when wind turbine parts are likely to fail, allowing maintenance to be planned before costly breakdowns occur. By combining real measurements from turbines with knowledge of how cracks grow in metal, our approach gives more reliable forecasts even when only limited data are available. We also measure how confident the predictions are, helping operators make better decisions. This can reduce downtime and lower the cost of wind energy.
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