Ultrasonic wave-based defect localization using probabilistic modeling

M. D. Todd; E. B. Flynn; P. D. Wilcox; B. W. Drinkwater; A. J. Croxford; S. Kessler

doi:10.1063/1.4716287

Ultrasonic wave-based defect localization using probabilistic modeling

M. D. Todd^*, E. B. Flynn, P. D. Wilcox, B. W. Drinkwater, A. J. Croxford, S. Kessler

^*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceeding › Conference Contribution (Conference Proceeding)

1 Citation (Scopus)

Abstract

This work presents a new approach rooted in maximum likelihood estimation for defect localization in sparse array guided wave ultrasonic interrogation applications. The approach constructs a minimally-informed statistical model of the guided wave process, where unknown or uncertain model parameters are assigned non-informative Bayesian prior distributions and integrated out of the a posteriori probability calculation. The premise of this localization approach is straightforward: the most likely defect location is the point on the structure with the maximum a posteriori probability of actually being the location of damage (i.e., the most probable location given a set of sensor measurements). The proposed approach is tested on a complex stiffened panel against other common localization approaches and found to have superior performance in all cases.

Original language	English
Title of host publication	AIP Conference Proceedings
Pages	639-646
Number of pages	8
Volume	1430
Edition	31
DOIs	https://doi.org/10.1063/1.4716287
Publication status	Published - 13 Jul 2012
Event	38th Annual Review of Progress in Quantitative Nondestructive Evaluation (QNDE 2011) - Burlington, VT, United States Duration: 17 Jul 2011 → 22 Jul 2011 Conference number: 38

Conference

Conference	38th Annual Review of Progress in Quantitative Nondestructive Evaluation (QNDE 2011)
Abbreviated title	QNDE 2011
Country	United States
City	Burlington, VT
Period	17/07/11 → 22/07/11

Keywords

Bayesian Modeling
Sparse Array Localization
Ultrasonic Guided Waves

Access to Document

10.1063/1.4716287

Link to publication in Scopus

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Cite this

Todd, M. D., Flynn, E. B., Wilcox, P. D., Drinkwater, B. W., Croxford, A. J., & Kessler, S. (2012). Ultrasonic wave-based defect localization using probabilistic modeling. In AIP Conference Proceedings (31 ed., Vol. 1430, pp. 639-646) https://doi.org/10.1063/1.4716287

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title = "Ultrasonic wave-based defect localization using probabilistic modeling",

abstract = "This work presents a new approach rooted in maximum likelihood estimation for defect localization in sparse array guided wave ultrasonic interrogation applications. The approach constructs a minimally-informed statistical model of the guided wave process, where unknown or uncertain model parameters are assigned non-informative Bayesian prior distributions and integrated out of the a posteriori probability calculation. The premise of this localization approach is straightforward: the most likely defect location is the point on the structure with the maximum a posteriori probability of actually being the location of damage (i.e., the most probable location given a set of sensor measurements). The proposed approach is tested on a complex stiffened panel against other common localization approaches and found to have superior performance in all cases.",

keywords = "Bayesian Modeling, Sparse Array Localization, Ultrasonic Guided Waves",

author = "Todd, {M. D.} and Flynn, {E. B.} and Wilcox, {P. D.} and Drinkwater, {B. W.} and Croxford, {A. J.} and S. Kessler",

year = "2012",

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Todd, MD, Flynn, EB, Wilcox, PD , Drinkwater, BW , Croxford, AJ & Kessler, S 2012, Ultrasonic wave-based defect localization using probabilistic modeling. in AIP Conference Proceedings. 31 edn, vol. 1430, pp. 639-646, 38th Annual Review of Progress in Quantitative Nondestructive Evaluation (QNDE 2011), Burlington, VT, United States, 17/07/11. https://doi.org/10.1063/1.4716287

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AU - Flynn, E. B.

AU - Wilcox, P. D.

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AU - Kessler, S.

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AB - This work presents a new approach rooted in maximum likelihood estimation for defect localization in sparse array guided wave ultrasonic interrogation applications. The approach constructs a minimally-informed statistical model of the guided wave process, where unknown or uncertain model parameters are assigned non-informative Bayesian prior distributions and integrated out of the a posteriori probability calculation. The premise of this localization approach is straightforward: the most likely defect location is the point on the structure with the maximum a posteriori probability of actually being the location of damage (i.e., the most probable location given a set of sensor measurements). The proposed approach is tested on a complex stiffened panel against other common localization approaches and found to have superior performance in all cases.

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