Temperature Robust PCA Based Stress Monitoring Approach

Jabid Quiroga; John Quiroga; Luis Mujica; Rodolfo Villamizar; Magda Ruiz

doi:10.4028/www.scientific.net/KEM.713.288

Paper Titles

The Numerical Analysis for a Crack in the Cathode Electrode Surface of Planar SOFC
p.273

Experimental Test of Compressive Strength after Impact Damage of Natural Composite Laminate
p.277

High-Temperature Structural Analysis of a Lab-Scale Alloy 800HT PCHE
p.280

Influence of Compressive Stress to the Corrosion Rate of Magnesium
p.284

Temperature Robust PCA Based Stress Monitoring Approach
p.288

Thermal Stress in Volcanic-Ash-Deposited Thermal Barrier Coatings Monitored by Long-Range Laser System
p.293

Measure of Nonlocal Response in Multiscale Gradient Modeling
p.297

Effect of the Crack Surface Friction on the Field Strength of Bi-Material-Interfacial Crack
p.301

Studies on Mode II Rock Crack Propagation on Different Loading Environments
p.305

HomeKey Engineering MaterialsKey Engineering Materials Vol. 713Temperature Robust PCA Based Stress Monitoring...

Temperature Robust PCA Based Stress Monitoring Approach

Abstract:

In this paper, a guided wave temperature robust PCA-based stress monitoring methodology is proposed. It is based on the analysis of the longitudinal guided wave propagating along the path under stress. Slight changes in the wave are detected by means of PCA via statistical T² and Q indices. Experimental and numerical simulations of the guided wave propagating in material under different temperatures have shown significant variations in the amplitude and the velocity of the wave. This condition can jeopardize the discrimination of the different stress scenarios detected by the PCA indices. Thus, it is proposed a methodology based on an extended knowledge base, composed by a PCA statistical model for different discrete temperatures to produce a robust classification of stress states under variable environmental conditions. Experimental results have shown a good agreement between the predicted scenarios and the real ones

You might also be interested in these eBooks

Advances in Fracture and Damage Mechanics XV

View Preview

Info:

Periodical:

Key Engineering Materials (Volume 713)

Pages:

288-292

DOI:

https://doi.org/10.4028/www.scientific.net/KEM.713.288

Citation:

Cite this paper

Online since:

September 2016

Authors:

Jabid Quiroga*, John Quiroga, Luis Mujica, Rodolfo Villamizar, Magda Ruiz

Keywords:

Guided Waves, PCA, Stress Monitoring, Temperature Robust

Export:

RIS, BibTeX

Price:

Permissions CCC:

Request Permissions

Permissions PLS:

Request Permissions

Сopyright:

Citation:

* - Corresponding Author

References

[1] S. Chaki and G. Bourse: Stress level measurement in prestressed steel strands using acoustoelastic effect. Exp. Mech., vol. 49, no. 5 (2009), p.673–681.

DOI: 10.1007/s11340-008-9174-9

Google Scholar

[2] D.S. Hughes, J.L. Kelly: Second-Order Elastic Deformations of Solids. Phys. Rev., vol. 92, no. 5, (1953) p.1145–1150.

Google Scholar

[3] S. Chaki and G. Bourse: Guided ultrasonic waves for non-destructive monitoring of the stress levels in prestressed steel strands. Ultrasonics, vol. 49, no. 2, (2009) p.162–171.

DOI: 10.1016/j.ultras.2008.07.009

Google Scholar

[4] F. Gharibnezhad: Robust Damage Detection in Smart Structures. PhD Thesis. U.P. Cataluña (2014).

Google Scholar

[5] J. P. Andrews: Lamb Wave Propagation in Varying Thermal Enviroments. Air Force Institute of Technology Air, (2007).

Google Scholar

[6] A. J. Croxford, P. D. Wilcox, B. W. Drinkwater, and G. Konstantinidis; Strategies for guided-wave structural health monitoring. Proc. R. Soc. A Math. Phys. Eng. Sci., vol. 463, no. 2087, (2007) p.2961–2981.

DOI: 10.1098/rspa.2007.0048

Google Scholar

[7] L. Zhong, H. Song, and B. Han; Extracting structural damage features: Comparison between PCA and ICA. Lect. Notes Control Inf. Sci., vol. 345, (2006), p.840–845.

DOI: 10.1007/978-3-540-37258-5_101

Google Scholar

[8] A. M. Yan, G. Kerschen, P. De Boe, and J. C. Golinval: Structural damage diagnosis under varying environmental conditions - Part I: A linear analysis. Mech. Syst. Signal Process., vol. 19, no. 4, (2005), p.847–864.

DOI: 10.1016/j.ymssp.2004.12.002

Google Scholar

[9] A. Hot, G. Kerschen, E. Foltête, and S. Cogan: Detection and quantification of non-linear structural behavior using principal component analysis. Mech. Syst. Signal Process., vol. 26, no. 1, (2012), p.104–116.

DOI: 10.1016/j.ymssp.2011.06.006

Google Scholar