Gaussian Mixture Model Based Damage Evaluation for Aircraft Structures

Gaussian Mixture Model Based Damage Evaluation for Aircraft Structures

Qiuhui Xu, Shenfang Yuan, Yuanqiang Ren

download PDF

Abstract. The Guided Wave (GW) based Structural Health Monitoring (SHM) method is of significant research interest because of its wide monitoring range and high sensitivity. However, there are still many challenges in real engineering applications due to complex time-varying conditions, such as changes in temperature and humidity, random dynamic loads, and structural boundary conditions. In this paper, a Gaussian Mixture Model (GMM) is adopted to deal with these problems. Multi-dimensional GMM (MDGMM) is proposed to model the probability distribution of GW features under time-varying conditions. Furthermore, to measure the migration degree of MDGMM to reveal the crack propagation, research on migration indexes of the probability model is carried out. Finally, the validation in an aircraft fatigue test shows a good performance of the MDGMM.

Structural Health Monitoring, Guided Wave Features, Aircraft Fatigue Test, Multi-Dimensional Gaussian Mixture Model, Migration Index

Published online 2/20/2021, 7 pages
Copyright © 2021 by the author(s)
Published under license by Materials Research Forum LLC., Millersville PA, USA

Citation: Qiuhui Xu, Shenfang Yuan, Yuanqiang Ren, Gaussian Mixture Model Based Damage Evaluation for Aircraft Structures, Materials Research Proceedings, Vol. 18, pp 154-160, 2021


The article was published as article 18 of the book Structural Health Monitoring

Content from this work may be used under the terms of the Creative Commons Attribution 3.0 licence. Any further distribution of this work must maintain attribution to the author(s) and the title of the work, journal citation and DOI.

[1] Boller C, Chang FK and Fujino Y. Encyclopedia of structural health monitoring. New York: John Wiley & Sons, 2009.
[2] Yuan S F, Ren Y Q, Qiu L, Mei H F. A multi-response-based wireless impact monitoring network for aircraft composite structures. IEEE Transactions on Industrial Electronics, 2016, 63(12): 7712-7722.
[3] Su Z, Ye L. Identification of Damage Using Lamb Waves. Springer London, 2009.
[4] Yuan S F, Zhang J J, Chen J, et al. A uniform initialization Gaussian mixture model–based guided wave–hidden Markov model with stable damage evaluation performance. Structural Health Monitoring, 2019, 18(3):853-868.
[5] Tschope C, Wolff M. Statistical Classifiers for Structural Health Monitoring. Sensors Journal, IEEE, 2009, 9(11):1567-1576.
[6] Banerjee S, Qing X P, Beard S, et al. Prediction of Progressive Damage State at the Hot Spots using Statistical Estimation. Journal of intelligent material systems and structures, 2010, 21(6):595-605.
[7] Qiu L, Fang F, Yuan S F, et al. An enhanced dynamic Gaussian mixture model–based damage monitoring method of aircraft structures under environmental and operational conditions. Structural Health Monitoring, 2018:147592171875934.
[8] Torkamani S, Roy S, Barkey M E, et al. A novel damage index for damage identification using guided waves with application in laminated composites. Smart Materials & Structures, 2014, 23(9):095015.
[9] Liberti L, Lavor C, Maculan N, Mucherino A. Euclidean Distance Geometry and Applications. SIAM Review. 2014; 56:3-69.
[10] De Maesschalck R, Jouan-Rimbaud D, Massart DL. The Mahalanobis distance. Chemometrics and Intelligent Laboratory Systems. 2000; 50:1-18.
[11] You C H, Lee K A, Li H. GMM-SVM Kernel with a Bhattacharyya-Based Distance for Speaker Recognition. Audio Speech & Language Processing IEEE Transactions on, 2010, 18(6):1300-1312.
[12] Goldberger J, Gordon S, Greenspan H. An efficient image similarity measure based on approximations of KL-divergence between two Gaussian mixtures// Proceedings Ninth IEEE International Conference on Computer Vision. IEEE, 2003.