journal article·2013-03

Statistical Analysis of Eddy Current Data from Fastener Site Inspections

Jeremy S. Knopp, Ramana Grandhi, John C. Aldrin, Inseok Park

Publisher page

Abstract

Recent work on reliably detecting and characterizing cracks in multi-layer airframe structures has used modeling and simulation to extract features from raw eddy current data, and to assist in the evaluation of probability of detection (POD). This paper focuses on the statistical analysis of the data from these studies. Hit/miss, linear, and physics-inspired methods are employed to evaluate POD. The Box-Cox transformation is used as a remedy for violations of the constant variance assumption. In addition, a bootstrapping method is introduced for confidence bound calculation on a 2nd order linear model. The objective of this work is to provide on insight how different models and assumptions impact POD evaluation.
Source: openaire · Quality: candidate unverified · Source record ↗

Research context

OpenAlex topics: Probabilistic and Robust Engineering Design · Non-Destructive Testing Techniques · Fatigue and fracture mechanics

SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.

References (18)

Show source-provided references
1. Berens, A.P.: NDE reliability data analysis. In: American Society for Metals Handbook Nondestructive Evaluation and Quality Control, vol. 17, pp. 689–701. ASM International, Materials Park (1989)
2. Knopp, J.S., Aldrin, J.C.: Fundamental feature extraction methods for the analysis of eddy current data. Electromagn. Nondestruct. Eval. Proc. 31 (2008)
3. Aldrin, J.C., Knopp, J.S.: Crack characterization method with invariance to noise features for eddy current inspection of fastener sites. J. Nondestruct. Eval. 25(4), 165–181 (2006)10.1007/s10921-006-0014-5 ↗
4. Aldrin, J.C., Knopp, J.S., Blodgett, M.P.: Model-based image processing in eddy current nondestructive evaluation. Electromagn. Nondestruct. Eval. Proc. 33, 254–261 (2010)
5. Knopp, J.S., Aldrin, J.C., Lindgren, E, Annis, C.: Investigation of a model-assisted approach to probability of detection evaluation. Rev. Prog. Quant. Nondestruct. Eval. 26, 1775–1782 (2007)10.1063/1.2718178 ↗
6. Aldrin, J.C., Knopp, J.S., Lindgren, E.A., Jata, K.V.: Model-assisted probability of detection (MAPOD) evaluation for eddy current inspection of fastener sites. Rev. Prog. Quant. Nondestruct. Eval. 28, 1784–1791 (2009)10.1063/1.3114175 ↗
7. Aldrin, J.C., Knopp, J.S.: Modeling and simulation for nondestructive testing with applications to aerospace structures. Mater. Eval. 66, 53–59 (2008)
8. U.S. Department of Defense: MIL-HDBK-1823A Nondestructive Evaluation System Reliability Assessment, August, 2010
9. Gandossi, L., Simola, K.: Derivation and use of probability of detection curves in the nuclear industry. Insight 52(12) (2010)10.1784/insi.2010.52.12.657 ↗
10. Drury, C.G., Ghylin, K.M., Holness, K.: Error analysis and threat magnitude for carry-on bag inspection. In: Proceedings of the Human Factors and Ergonomic Society 50th Annual Meeting, vol. 50, pp. 1189–1193 (2006)
11. Li, M., Nakagawa, N., Larson, B.F., Meeker, W.Q.: Statistical assessment of probability of detection for automated eddy current nondestructive evaluation inspection. Preprint, available at last accessed May. http://www.stat.iastate.edy/preprint/articles/2011-05.pdf (2012)
12. Kutner, M.H., Nachtsheim, C.J., Neter, J., Li, W.: Applied Linear Statistical Models, 5th edn. McGraw-Hill/Irwin, New York (2004)
13. Thompson, R.B., Meeker, W.Q.: Assessing the POD of HardAlpha inclusions from field data. Rev. Progr. Quant. Nondestruct. Eval. 26, 1759–1766 (2007)10.1063/1.2718176 ↗
14. Efron, B., Tibshirani, R.J.: An Introduction to the Bootstrap. Chapman & Hall, New York (1993)10.1007/978-1-4899-4541-9 ↗
15. McCulloch, C.C., Murphy, J.: Local regression modeling for accurate analysis of probability of detection data. Mater. Eval. 60(12), 1438–1443 (2002)
16. Amarchinta, H.K., Tarpey, T., Grandhi, R.V.: Probabilistic confidence bound framework for residual stress field predictions. In: 12th AIAA Non-Deterministic Approaches Conference, Orlando, FL (2010). AIAA-2010-2519
17. Kennedy, M.C., O’Hagan, A.: Bayesian calibration of computer models. J. R. Statist. Soc. B. 63 (2001)10.1111/1467-9868.00294 ↗
18. Annis, C.: Statistical Best-Practices for Building Probability of Detection (POD) models, R package mh1823, version 2.5.4.1. http://statisticalengineering.com/mh1823