A SHM damage diagnosis model evolution mechanism for individual aircraft structure
Hutao Jing, Shenfang Yuan
Abstract. The concept of aircraft health management is evolving from conventional fleet-based management to individual aircraft-based management. Accurate damage diagnosis with guided wave (GW)-based structural health monitoring (SHM) is of great significance for individual aircraft in service. However, both the damage propagation and monitoring of individual aircraft structures are affected by various uncertainties, such as time-varying environmental and operational conditions, different flight missions, and different damage morphologies. Consequently, employing a prior trained damage diagnosis model inevitably introduces errors, thereby limiting the engineering applicability of SHM. To achieve more reliable damage diagnosis for in-service aircraft structures, this paper proposes a whole lifetime data-based damage diagnosis model evolution mechanism. Multi-source data from the design, service, and maintenance stages are used to continuously evolve the probabilistic damage diagnosis model, enabling it to track the specific damage propagation. The proposed method is validated through fatigue crack monitoring experiments of a typical aircraft load-carrying structure. The results demonstrate that it can significantly improve the damage diagnosis performance for in-service aircraft structures under the influence of uncertainties.
Keywords
Guided Wave, Structural Health Monitoring, Individual Aircraft Structure, Hidden Markov Model, Model Evolution Mechanism
Published online 3/25/2025, 7 pages
Copyright © 2025 by the author(s)
Published under license by Materials Research Forum LLC., Millersville PA, USA
Citation: Hutao Jing, Shenfang Yuan, A SHM damage diagnosis model evolution mechanism for individual aircraft structure, Materials Research Proceedings, Vol. 50, pp 1-7, 2025
DOI: https://doi.org/10.21741/9781644903513-1
The article was published as article 1 of the book Structural Health Monitoring
Content from this work may be used under the terms of the Creative Commons Attribution 3.0 license. Any further distribution of this work must maintain attribution to the author(s) and the title of the work, journal citation and DOI.
References
[1] M. Kordestani, M.E. Orchard, K. Khorasani and M. Saif, An overview of the state of the art in aircraft prognostic and health management strategies, IEEE T. Instrum. Meas. 72 (2023) 1-15. https://doi.org/10.1109/TIM.2023.3236342
[2] V. Giurgiutiu, Structural health monitoring: with piezoelectric wafer active sensors, second ed., Academic Press, Burlington, 2014. https://doi.org/10.1016/B978-0-12-418691-0.00007-1
[3] A. Güemes, A. Fernandez-Lopez, A.R. Pozo and J. Sierra-Pérez, Structural health monitoring for advanced composite structures: a review, J. Compos. Sci. 4 (2020) 13. https://doi.org/10.3390/jcs4010013
[4] R. Gorgin, Y. Luo and Z.J. Wu, Environmental and operational conditions effects on Lamb wave based structural health monitoring systems: A review, Ultrasonics 105 (2020) 106114. https://doi.org/10.1016/j.ultras.2020.106114
[5] M. Mitra and S. Gopalakrishnan, Guided wave based structural health monitoring: A review, Smart Mater. Struct. 25(5) (2016) 053001. https://doi.org/10.1088/0964-1726/25/5/053001
[6] P. Cawley, Guided waves in long range nondestructive testing and structural health monitoring: Principles, history of applications and prospects, NDT & E Int. (2023) 103026. https://doi.org/10.1016/j.ndteint.2023.103026
[7] L. Qiu, S.F. Yuan, Q. Bao, H.F. Mei and Y.Q. Ren, Crack propagation monitoring in a full-scale aircraft fatigue test based on guided wave-Gaussian mixture model, Smart Mater. Struct. 25(5) (2016) 055048. https://doi.org/10.1088/0964-1726/25/5/055048
[8] S. Sawant, S. Patil, J. Thalapil, S. Banerjee and S. Tallur, Temperature variation compensated damage classification and localisation in ultrasonic guided wave SHM using self-learnt features and Gaussian mixture models, Smart Mater. Struct. 31(5) (2022) 055008. https://doi.org/10.1088/1361-665X/ac5ce3
[9] S.R. Yeratapally, M.G. Glavicic, C. Argyrakis and M.D. Sangid, Bayesian uncertainty quantification and propagation for validation of a microstructure sensitive model for prediction of fatigue crack initiation, Reliab. Eng. Syst. Saf. 164 (2017) 110-123. https://doi.org/10.1016/j.ress.2017.03.006
[10] J.S. Yang, J.J. He, X.F. Guan, D.J. Wang, H.P. Chen, W.F. Zhang and Y.M. Liu, A probabilistic crack size quantification method using in-situ Lamb wave test and Bayesian updating, Mech. Syst. Signal Process. 78 (2016) 118-133. https://doi.org/10.1016/j.ymssp.2015.06.017
[11] S.F. Yuan, H. Wang and J. Chen, A pzt based on-line updated guided wave-gaussian process method for crack evaluation, IEEE Sens. J. 20(15) (2019) 8204-8212. https://doi.org/10.1109/JSEN.2019.2960408
