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POD Evaluation: The Key Performance Indicator for NDE 4.0

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Abstract

Reliability evaluations of modern test systems under the Industry 4.0 technologies, play a vital role in the successful transformation to NDE 4.0. This is due to the fact that NDE 4.0 is mainly based on the interconnection between the cyber-physical systems. When the individual reliability of the various important technologies from the Industry 4.0 such as the digital twin, digital thread, Industrial Internet of Things (IIoT), artificial intelligence (AI), data fusion, digitization, etc. is high, then it is possible to obtain the reliability beyond the intrinsic capability of the test system. In this paper, the significance of the reliability evaluation is reviewed under the vision of NDE 4.0, including examples of data fusion concepts as well as the importance of algorithms (like explainable artificial intelligence), the practical use is discussed and elaborated accordingly.

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References

  1. Vrana, J., Singh, R.: NDE 4.0—a design thinking perspective. J. Nondestruct. Eval. 40, 8 (2021). https://doi.org/10.1007/s10921-020-00735-9

    Article  Google Scholar 

  2. Kanzler, D., Rentala, V.K.: Reliability evaluation of testing systems and their connection to NDE 4.0. In: Meyendorf, N., Ida, N., Singh, R., Vrana, J. (eds.) Handbook of Nondestructive Evaluation 4.0. Springer, Cham (2021). https://doi.org/10.1007/978-3-030-48200-8_3-1

    Chapter  Google Scholar 

  3. Swets, J.A., Pickett, R.M.: Evaluation of Diagnostic Systems. Hrsg. von Academic Press. Academic Press. isbn: 0-12-679080-9 (1982)

  4. Rummel, W.D., Todd Jr., P.H., Frecska, S.A., Rathke, R.A.: The detection of fatigue cracks by nondestructive testing methods (1974)

  5. NDE 4.0 Webinar: Data: Perception vs. Reality with A. Anbarasu, T. Wenzel, R. Singh, J. Vrana. YouTube. 2021. https://www.youtube.com/watch?v=IpGoknrXmFs (2021)

  6. IZFP Fraunhofer, Nondestructive Monitoring along the Product Life Cycle. https://www.izfp.fraunhofer.de/content/dam/izfp/en/documents/2016/Fraunhofer%20IZFP%20-%20General%20Overview.pdf. Accessed 10 June 2021

  7. Vrana, J., Singh, R.: Cyber-physical loops as drivers of value creation in NDE 4.0. J. Nondestruct. Eval. 40, 61 (2021). https://doi.org/10.1007/s10921-021-00793-7

    Article  Google Scholar 

  8. Førli, O., Ronold, K.: Guidelines for NDE reliability determination and description. Nordtest NT TECHN report 394 (1998). http://nordtest.info/images/documents/nt-technical-reports/NT%20TR%20394_Guideline%20for%20NDE%20Reliability%20Determination%20and%20Description_Nordtest%20Technical%20Report.pdf. Accessed 30 June 2021

  9. Department of Defense, MIL-HDBK-1823: Nondestructive evaluation system reliability assessment. Handbook (1999)

  10. Ripudaman Singh: Three Decades of NDI Reliability Assessment. Special review publication for USAF. Report No. Karta-3510-99-01, USAF Contract F41608-99-C-0404 (2000)

  11. Department of Defense. MIL-HDBK-1823A: Nondestructive evaluation system reliability assessment. Handbook (2009)

  12. Gandossi, L., Annis, Ch.: Probability of Detection Curves: Statistical Best-Practices. ENIQ report No 41. ENIQ. 24429 EN (2010)

  13. ASTM E2862-12 (2012) Standard Practice for Probability of Detection Analysis for Hit/Miss Data. ASTM International.

  14. ASTM E3023-15: Standard Practice for Probability of Detection Analysis for â Versus a Data. ASTM International (2015)

  15. Kanzler, D., et al.: Die deutschen Bestrebungen zur Normierung der Fähigkeits- und uverlässigkeitsbewertung der zerstörungsfreien Prüfung. DGZfP-Jahrestagung 2021. ndt.net (2021). http://www.ndt.net/?id=26274

  16. Müller, Ch., Bertovic, M., Holstein, R., Kanzler, D., Pitkänen, J., Ronneteg, U., Heckel, T.: A Plenary View on the Vigour of our NDE Reliability Models. 5th European-American Workshop on Reliability of NDE October 7–10, 2013, Berlin, Germany (2013)

  17. Müller, C., Bertovic, M., Pavlovic, M., Kanzler, D., Ewert, U., Pitkänen, J., Ronneteg, U.: Paradigm shift in the holistic evaluation of the reliability of NDE systems. Mater. Test. 55(4), 261–269 (2013)

    Article  Google Scholar 

  18. Holstein, R., Müller, C.: Analyzing the Reliability of Non-destructive Tests Using the Modular Modell—A Practical Approach 19th World Conference on Non-Destructive Testing (2016)

  19. Mueller, C., Fritz, T., Tillack, G.-R., Bellon, C., Scharmach, M.: (2001) Theory and Application of the Modular Approach to NDE Reliability Bridging the Gap Between Safety Requirements and Economy, NDT.net, Vol. 06 No. 09.

  20. Spencer, Floyd W. (2011) Nonparametric POD estimation for hit/miss data: a goodness of fit comparison for parametric models. AIP Conference Proceedings. Vol. 1335. No. 1. American Institute of Physics.

  21. D. Kanzler, C Müller, J Pitkänen. (2013) Probability of defect detection of Posiva’s electron beam weld. WR 2013–70. Working Report. Posiva Oy.

  22. Pavlović, M., Takahashi, K., Müller, C.: Probability of detection as a function of multiple influencing parameters. Insight-Non-Destructive Testing and Condition Monitoring 54(11), 606–611 (2012)

    Article  Google Scholar 

  23. Spies, M., Rieder, H.: An approach to the question ‘How to account for human error in MAPOD? In: 12th ECNDT Conference, (Gothenburg, Sweden, 2018) (2018). https://www.ndt.net/article/ecndt2018/papers/ecndt-0571-2018.pdf. Accessed 20 Aug 2021

  24. Brewer, J., Mengert, P., DiSario, R.: Probability of visual crack detection from Japanese maintenance data. In: Review of Progress in Quantitative Nondestructive Evaluation, pp. 2029–2036. Springer, Boston (1997)

  25. Berens, A.P.: NDE reliability data analysis: nondestructive evaluation and quality control. In: Metals Handbook. 9./17, pp. 689–701. Materials Park, Ohio: ASM International (1989)

  26. Völker, C., Shokouhi, P.: Clustering based multi sensor data fusion for honeycomb detection in concrete. J. Nondestr. Eval. 34(4), 1–10 (2015)

    Article  Google Scholar 

  27. Kim, F.H., et al.: The influence of X-Ray computed tomography acquisition parameters on image quality and probability of detection of additive manufacturing defects. J. Manuf. Sci. Eng. 141(11), 111002 (2019)

    Article  Google Scholar 

  28. Kanzler, D., Müller, C., Pitkänen, J.: Probability of defect detection of Posiva's electron beam weld. No. POSIVA-WR—13-70. Posiva Oy (2013)

  29. Bismut, E., Straub, D.: A unified model of inspection and monitoring quality. arXiv preprint arXiv:2103.12853 (2021)

  30. Kanzler, D., Müller, C., Pitkänen, J. et al.: Use of Bayesian Statistics in Determining the Reliability of Non-destructive Testing Systems. In: Materials Testing 55.4, pp. 254–260 (2013). https://doi.org/10.3139/120.110432

  31. Ramos, L., Mirando, T., Mishra, M., Fernandes, F., Manning, E.: A Bayesian approach for NDT data fusion: the Saint Torcato Church Case Study. Eng Struct. 84(1), 120–129 (2015)

    Article  Google Scholar 

  32. Rentala, V.K., Mylavarapu, P., Gautam, J.P.: Issues in estimating probability of detection of NDT techniques—a model assisted approach. Ultras 87, 59–70 (2018). https://doi.org/10.1016/j.ultras.2018.02.012

    Article  Google Scholar 

  33. Virkkunen, I., Koskinen, T., Jessen-Juhler, O.: Virtual round robin—a new opportunity to study NDT reliability. Nuclear Eng Des. 380, 111297 (2021)

    Article  Google Scholar 

  34. Nockemann, Ch., Tillack, G.R., Schnitger, D.: Statistical Assessment Within a Global Conception of Reliability of NDE. Non-Destructive Examination Practice and Results—State of the Art and PISC III Results (1994)

  35. Northcutt, C., Athalye, A., Mueller, J.: Pervasive Label Errors in Test Sets Destabilize Machine Learning Benchmarks. arXiv 2103.14749 (2021)

  36. Müller, Ch., Scharmach, M., et al.: General principles of reliability assessment of nondestructive diagnostic systems and its applicability to the demining problem. NDT.net, vol. 8 no. 4 (2003)

  37. Fuchs, P., Kröger, T., Dierig, T., Garbe, Ch.: Generating Meaningful Synthetic Ground Truth for Pore Detection in Cast Aluminum Parts. 9th Conference on Industrial Computed Tomography, Padova, Italy (iCT 2019). http://www.ndt.net/?id=23730 (2019)

  38. Meeker, W.Q.: R.B.Thompson’s contribution to model assisted probability of detection. AIP Conf. Proc. 1430, 83–94 (2012)

    Article  Google Scholar 

  39. Fuchs, P., Kröger, T., Garbe, C.: Defect detection in CT scans of cast aluminum parts: a machine vision perspective. Neurocomputing 453, 85–96 (2021)

    Article  Google Scholar 

  40. Swets, J.A., Pickett, R.M.: Evaluation of Diagnostic Systems: Methods from Signal Detection Theory. Academic Press, San Diego (1982)

    Google Scholar 

  41. Everingham, M., Eslami, A., et al.: The pascal visual object classes challenge: a retrospective. Int. J. Comput. Vis. 111, 98–136 (2015)

    Article  Google Scholar 

  42. Cordts, M., Ramos, M., et al.: The Cityscapes Dataset for Semantic Urban Scene Understanding. IEEE Conf on Computer Vision and Pattern Recognition (CVPR), pp. 3213–3223 (2016)

  43. Rentala, V.K., Mylavarapu, P., et al.: POD of NDT techniques using high temperature oxidized fatigue cracks in an aero engine alloy. J. Nondestruct. Eval. 40, 41 (2021). https://doi.org/10.1007/s10921-021-00769-7

    Article  Google Scholar 

  44. Rummel Ward, D., Rathke Richard, A.: Flaw Detection Reliability Assessment and Analysis. DTIC Report-ADP000013 (1982)

  45. Rentala, V.K., Mylavarapu, P., Gautam, J., Kumar, V.: Generation of POD curves in the absence of service-induced cracked components—an experimental approach. Insight 61, 1 (2019). https://doi.org/10.1784/insi.2019.61.1.28

    Article  Google Scholar 

  46. Rentala, V.K., Mylavarapu, P., Gautam, J.P., Gp Capt, B.V.N., Shiva, K.G., Kumar, V.: Physical manifestation of a90/95 in Remnant life revision studies of aero-engine components. Struct. Integr. Procedia 14, 597–604 (2019). https://doi.org/10.1016/j.prostr.2019.05.073

    Article  Google Scholar 

  47. Rentala, V.K., Mylavarapu, P., Gautam, J.P., Kumar, V.: NDE Reliability Using Laboratory Induced Natural Fatigue Cracks. 7th European-American Workshop on Reliability of NDE 2017. e-Journal of NDT (ndt.net) (2017). http://www.ndt.net/article/reliability2017/papers/29.pdf. Accessed 30 June 2021

  48. Rentala, V.K., Mylavarapu, P, Gopinath, K., Gautam, J.P., Kumar, V.: Model Assisted Probability of Detection for Lognormally Distributed Defects. 8th International Symposium on NDT in Aerospace 2016: e-Journal of NDT. http://www.ndt.net/?id=20615 (2016)

  49. Kanzler, D., Müller, C., Pitkänen, J.: Probability of detection for surface breaking holes with low-frequency eddy current testing—a non-linear multiparametric approach. Insight 56(12), 664–668 (2014)

    Article  Google Scholar 

  50. Forsyth, D.S., Fahr, A., Leemans, D.V., McRae, K.I., Kallsen, S., Nessa, C., Thompson, D.O., Chimenti, D.E., Poore, L.: Development of POD from in-service NDI data. AIP Conf. Proc. 509, 2167–2174 (2000)

    Article  Google Scholar 

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Acknowledgements

A discussion of a topic like that is never done in a simple dialogue. It is the result of many fruitful discussions. Especially, many thanks to Dr. Ripi Singh who helped us in sharpening our views on reliability and on NDE 4.0. Also, we would like to appreciate the work done until now with our colleagues from the normPOD Project, Marija Bertovic, Florian Dethof, Thomas Heckel, Sylvia Keßler, Ricarda Stolz, Martina Rosenthal and Johannes Vrana.

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Correspondence to Vamsi Krishna Rentala.

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Rentala, V.K., Kanzler, D. & Fuchs, P. POD Evaluation: The Key Performance Indicator for NDE 4.0. J Nondestruct Eval 41, 20 (2022). https://doi.org/10.1007/s10921-022-00843-8

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