journal article·2019-12Open access

Comparison of â Versus a and Hit/Miss POD-Estimation Methods: A European Viewpoint

Iikka Virkkunen, Tuomas Koskinen, Suvi Papula, Teemu Sarikka, Hannu Hänninen

Abstract

Abstract For estimating the probability of detection (POD) in non-destructive evaluation (NDE), there are two standard methods, the so-called â versus a approach and the hit/miss approach. The two approaches have different requirements for the quality and quantity of input data as well as for the underlying NDE method. There is considerable overlap between the methods, and they have different limitations, so it is of interest to study the differences arising from using each methodology. In particular, if the dataset is not ideal, the methodologies may exhibit different problems dealing with various limitations in the data. In this paper, a comparison between â versus a and hit/miss analysis was completed for two different data sets, a manual aerospace eddy-current inspection and a nuclear industry phased array ultrasonic weld inspection using a simplified online tool. It was found that the two standard methods (â vs. a and hit/miss) may give significantly different results, if the true hit/miss decision is based on inspector judgement and not automated signal threshold. The true inspector hit/miss performance shows significant variance that is not attributable to signal amplitude. Model-assisted POD was not able to model the inspector performance due to lack of representative amplitude threshold and difficulties in capturing true signal variance. The paper presents experience from practical cases and may be considered a European viewpoint.
Source: crossref · Quality: baseline

Research context

OpenAlex topics: Non-Destructive Testing Techniques · Probabilistic and Robust Engineering Design · Ultrasonics and Acoustic Wave Propagation

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References (26)

Show source-provided references
1. Charles Annis, P.E.: Statistical best-practices for building Probability of Detection\n(POD) models. R package mh1823, version 4.3.2 (2016). http://StatisticalEngineering.com/mh1823/
2. Underhill, P.R., Krause, T.W.: Eddy current analysis of mid-bore and corner cracks in bolt holes. NDT&E Int. 44, 513–518 (2011). https://doi.org/10.1016/j.ndteint.2011.05.00710.1016/j.ndteint.2011.05.007 ↗
3. Rummel, W.D.: Nondestructive evaluation—a critical part of structural integrity. Procedia Eng. 86, 375–383 (2014). https://doi.org/10.1016/j.proeng.2014.11.05110.1016/j.proeng.2014.11.051 ↗
4. Garza, J., Millwater, H.: Sensitivity of the probability of failure to probability of detection curve regions. Int. J. Press. Vessels Pip. 141, 26–39 (2016). https://doi.org/10.1016/j.ijpvp.2016.03.01210.1016/j.ijpvp.2016.03.012 ↗
5. Carboni, M., Cantini, S.: A model assisted probability of detection approach for ultrasonic inspection of railway axles. In: 18th World Conference on Nondestructive Testing, 16–20 April 2012 (2012)
6. Carboni, M., Cantini, S.: Advanced ultrasonic “Probability of Detection” curves for designing in-service inspection intervals. Int. J. Fatigue 86, 77–87 (2016). https://doi.org/10.1016/j.ijfatigue.2015.07.01810.1016/j.ijfatigue.2015.07.018 ↗
7. Gandossi, L., Annis, C.: Probability of Detection Curves: Statistical Best-Practices. ENIQ report nr. 41, vol EUR 24429 EN. European Commission (2010)
8. ASTM: Standard Practice for Probability of Detection Analysis for Hit/Miss Data, vol. E2862-12. West Conshohocken, ASTM International (2012)
9. ASTM: Standard Practice for Probability of Detection Analysis for â Versus a Data, vol. ASTM-E3023. West Conshohocken, ASTM International (2015)
10. Berens, A.: NDE reliability data analysis. In: Lampman, S.R., Zorc, T.B. (eds.) ASM Metals Handbook, 9th edn. ASM, Ohio (1989)
11. Syed Akbar Ali, M., Kumar, A., Rao, P., Tammana, J., Balasubramaniam, K., Rajagopal, P.: Bayesian synthesis for simulation-based generation of probability of detection (PoD) curves. Ultrasonics 84, 210–222 (2018). https://doi.org/10.1016/j.ultras.2017.11.00410.1016/j.ultras.2017.11.004 ↗
12. Syed Akbar Ali, M.S., Rajagopal, P.: Probability of detection (PoD) curves based on weibull statistics. J. Nondestr. Eval. (2018). https://doi.org/10.1007/s10921-018-0468-210.1007/s10921-018-0468-2 ↗
13. Le Gratiet, L., Iooss, B., Blatman, G., Browne, T., Cordeiro, S., Goursaud, B.: Model assisted probability of detection curves: new statistical tools and progressive methodology. J. Nondestr. Eval. (2017). https://doi.org/10.1007/s10921-016-0387-z10.1007/s10921-016-0387-z ↗
14. Annis, C., Gandossi, L., Martin, O.: Optimal sample size for probability of detection curves. Nucl. Eng. Des. 262, 98–105 (2013). https://doi.org/10.1016/j.nucengdes.2013.03.05910.1016/j.nucengdes.2013.03.059 ↗
15. Knopp, J.G., Zeng, L., Aldrin, J.: Considerations for statistical analysis of nondestructive evaluation data: hit/miss analysis. E J. Adv. Maint. 4(3), 105–115 (2012)
16. Harding, C.A., Hugo, G.R.: Statistical analysis of probability of detection hit/miss data for small data sets. In: AIP Conference Proceedings. AIP, pp 1838–1845 (2003)10.1063/1.1570352 ↗
17. McGrath, B.: Programme for the assessment of NDT in industry. PANI 3. Serco Assurance, UK (2008)
18. D’Agostino, A., Morrow, S., Franklin, C., Hughes, N.: Review of Human Factors Research in Nondestructive Examination. NRC, Rockville (2017)
19. Bato, M.R., Hor, A., Rautureau, A., Bes, C.: Impact of human and environmental factors on the probability of detection during NDT control by eddy currents. Measurement 133, 222–232 (2019). https://doi.org/10.1016/j.measurement.2018.10.00810.1016/j.measurement.2018.10.008 ↗
20. Ben Abdessalem, A., Jenson, F., Calmon, P.: Quantifying uncertainty in parameter estimates of ultrasonic inspection system using Bayesian computational framework. Mech. Syst. Signal Process. 109, 89–110 (2018). https://doi.org/10.1016/j.ymssp.2018.02.03710.1016/j.ymssp.2018.02.037 ↗
21. Yusa, N., Chen, W., Hashizume, H.: Demonstration of probability of detection taking consideration of both the length and the depth of a flaw explicitly. NDT E Int. 81, 1–8 (2016). https://doi.org/10.1016/j.ndteint.2016.03.00110.1016/j.ndteint.2016.03.001 ↗
22. Seuaciuc-Osorio, T., Ammirato, F.: Materials Reliability Program: Development of Probability of Detection Curves for Ultrasonic Examination of Dissimilar Metal Welds MRP-262, 3rd edn. EPRI, Charlotte (2017)
23. Generazio, E.R.: Design of experiments for validating probability of detection capability of NDT systems and for qualification of inspectors. Mater. Eval. 67(6), 730–738 (2009)
24. Generazio, E.R.: Validating design of experiments for determining probability of detection capability for fracture critical applications. Mater. Eval. 69(12), 1399–1407 (2011)
25. Generazio, E.R.: Directed Design of Experiments for Validating Probability of Detection Capability of NDE Systems (DOEPOD) (2015)
26. Virkkunen, M., Ylitalo, M.: Practical experiences in POD determination for airframe ET inspection. In: 2016/11/3 (2016)