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 |
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Title of host publication | AIP Conference Proceedings |
Pages | 639-646 |
Number of pages | 8 |
Volume | 1430 |
Edition | 31 |
DOIs | |
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) |
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Abbreviated title | QNDE 2011 |
Country/Territory | United States |
City | Burlington, VT |
Period | 17/07/11 → 22/07/11 |
Keywords
- Bayesian Modeling
- Sparse Array Localization
- Ultrasonic Guided Waves