e-ISSN 2231-8526
ISSN 0128-7680
Jan Lean Tai, Mohamed Thariq Hameed Sultan, Farah Syazwani Shahar, Noorfaizal Yidris, Adi Azriff Basri and Ain Umaira Md Shah
Pertanika Journal of Science & Technology, Volume 32, Issue 5, August 2024
DOI: https://doi.org/10.47836/pjst.32.5.14
Keywords: â versus a, hit/miss, model-assisted probability of detection, nondestructive testing, phased array ultrasonic testing, probability of detection
Published on: 26 August 2024
In nondestructive testing (NDT), ensuring defect detection, measurement accuracy, and reliability guarantees various components’ structural integrity and safety. The Probability of Detection (POD) concept has emerged as a fundamental measure of the effectiveness of an inspection technique in identifying defects. Since NDT plays a crucial role in aerospace, manufacturing, and infrastructure industries, enhancing POD has become critical. POD refers to the likelihood that a flaw or defect of a certain size will be detected using the NDT technique. The “â versus a” and the “hit/miss” methods are particularly notable among the commonly employed POD estimation methods. The POD curve is determined based on crack size measurements in the “â versus a” approach, typically used in ultrasonic testing. On the other hand, the “hit/miss” method establishes the POD curve by analysing binary outcomes, where a “hit” signifies successful detection and a “miss” denotes detection failure. This review focuses on POD in the context of NDT, specifically in phased array ultrasonic corrosion mapping (PAUCM), to uncover current uncertainty parameters and explore an innovative avenue for enhancing POD assessment by incorporating the material surface temperature as an additional parameter.
Abdelli, D. E., Nguyen, T. T., Clenet, S., & Cheriet, A. (2019). Stochastic metamodel for probability of detection estimation of eddy-current testing problem in random geometric. IEEE Transactions on Magnetics, 55(6), 1–4. https://doi.org/10.1109/TMAG.2019.2893421
Accardi, E. D., Palumbo, D., Errico, V., Fusco, A., Angelastro, A., & Galietti, U. (2023). Analysing the probability of detection of shallow spherical defects by means of pulsed thermography. Journal of Nondestructive Evaluation, 42(1), 1–16. https://doi.org/10.1007/s10921-023-00936-y
Ameyaw, D. A., Deng, Q., & Söffker, D. (2019). Probability of detection (POD)-based metric for evaluation of classifiers used in driving behavior prediction. Proceedings of the Annual Conference of the Prognostics and Health Management Society, PHM, 11(1), 1–7. https://doi.org/10.36001/phmconf.2019.v11i1.774
Annis, C. (2014). Influence of sample characteristics on probability of detection curves. 40th Annual Review of Progress in Quantitative Nondestructive Evaluation AIP Conference Proceedings, 1581, 2039–2046. https://doi.org/10.1063/1.4865074
Annis, C., Aldrin, J. C., & Sabbagh, H. A. (2015a). Profile likelihood: What to do when maximum probability of detection never gets to one. Materials Evaluation, 73(1), 96–100.
Annis, C., Aldrin, J. C., & Sabbagh, H. A. (2015b). What is missing in nondestructive testing capability evaluation? Materials and Design, 73(1), 44–54.
Bajgholi, M. E., Rousseau, G., Ginzel, E., Thibault, D., & Viens, M. (2023). Total focusing method applied to probability of detection. International Journal of Advanced Manufacturing Technology, 126(7–8), 3637–3647. https://doi.org/10.1007/s00170-023-11328-x
Baskaran, P., Pasadas, D. J., Ramos, H. G., & Ribeiro, A. L. (2021). Integration of multiple response signals into the probability of detection modelling in eddy current NDE of flaws. NDT and E International, 118, Article 102401. https://doi.org/10.1016/j.ndteint.2020.102401
Bato, M. R., Hor, A., Rautureau, A., & Bes, C. (2017). Implementation of a robust methodology to obtain the probability of detection (POD) curves in NDT: Integration of human and ergonomic factors. LES JOURNÉES COFREND 2017, 1–16.
Bato, M. R., Hor, A., Rautureau, A., & Bes, C. (2020). Experimental and numerical methodology to obtain the probability of detection in eddy current NDT method. NDT and E International, 114, 1–35. https://doi.org/10.1016/j.ndteint.2020.102300
Bayoumi, A., Minten, T., & Mueller, I. (2021). Determination of detection probability and localization accuracy for a guided wave-based structural health monitoring system on a composite structure. Applied Mechanics, 2(4), 996–1008. https://doi.org/10.3390/applmech2040058
Brown, J. H. (2009). Probability of detection analysis for eddy current inspection systems. The American Society for Nondestructive Testing.
Burch, S. F., Stow, B. A., & Wall, M. (2005). Computer modelling for the prediction of the probability of detection of ultrasonic corrosion mapping. Insight: Non-Destructive Testing and Condition Monitoring, 47(12), 761–764. https://doi.org/10.1784/insi.2005.47.12.761
Calmon, P., Mesnil, O., Miorelli, R., Artusi, X., Chapuis, B., & D’Almeida, O. (2019). Model assisted probability of detection for guided wave imaging structural health monitoring. Proceedings of the 12th International Workshop on Structural Health Monitoring, 1, 811–816. https://doi.org/10.12783/shm2019/32190
Carboni, M., & Cantini, S. (2016). Advanced ultrasonic “Probability of detection” curves for designing in-service inspection intervals. International Journal of Fatigue, 86, 77–87. https://doi.org/10.1016/j.ijfatigue.2015.07.018
Caturano, G., Cavaccini, G., Ciliberto, A., Pianese, V., & Fazio, R. (2009). Probability of detection for penetrant testing in industrial environment. In Applied and industrial mathematics in Italy III (pp. 186-195). World Scientific. https://doi.org/10.1142/9789814280303_0017
Caulder, A. (2018). Full matrix capture and total focusing method: The next evolution in ultrasonic testing. Materials Evaluation, 76(5), 591–597.
Choi, Y. M., Kang, D., Kim, Y. L., Cho, S., Park, T., & Park, I. K. (2022). Reliability assessment of PAUT technique in Lieu of RT for tube welds in thermal power plant facilities. Applied Sciences, 12(12), Article 5867. https://doi.org/10.3390/app12125867
DOD. (2009). MIL-HDBK-1823A, Nondestructive evaluation system reliability assessment. Department of Defense Handbook.
Dominguez, N., Feuillard, V., Jenson, F., & Willaume, P. (2012). Simulation assisted pod of a phased array ultrasonic inspection in manufacturing. AIP Conference Proceedings, 1430(31), 1765–1772. https://doi.org/10.1063/1.4716425
Dominguez, N., Jenson, F., & Defense, E. A. (2010, June 7-11). Simulation assisted POD of a high frequency Eddy current inspection procedure. In Proceedings of the 10th European Conference on Non-Destructive Testing. European Conference on Non-Destructive Testing (pp. 1-10). Moscow, Russia.
Dominguez, N., Rodat, D., Guibert, F., Rautureau, A., & Calmon, P. (2016). POD evaluation using simulation: Progress, practice and perspectives regarding human factor. AIP Conference Proceedings, 1706, 3–9. https://doi.org/10.1063/1.4940651
Feistkorn, S., & Taffe, A. (2014). Methods to assess the quality of non-destructive testing in civil engineering using POD and GUM for static calculations of existing structures. Materialpruefung/Materials Testing, 56(7–8), 611–616. https://doi.org/10.3139/120.110602
Forsyth, D. S. (2016). Structural health monitoring and probability of detection estimation. In AIP Conference Proceedings (Vol. 1706, No. 1). AIP Publishing. https://doi.org/10.1063/1.4940648
Forsyth, D. S., & Aldrin, J. C. (2009, June 24-26). Build your own POD. In Proceedings of the 4th European-American Workshop on Reliability of NDE (pp. 1–8). Berlin, Germany.
Foucher, F., Fernandez, R., Leberre, S., & Calmon, P. (2018). New tools in CIVA for model assisted probability of detection (MAPOD) to support NDE reliability studies. NDE of Aerospace Materials & Structures 2018, 32–43.
Generazio, E. R. (2009). Design of experiments for validating probability of detection capability of NDT systems and for qualification of inspectors. Materials Evaluation, 67(6), 730–738.
Georgiou, G. A. (2007). PoD curves, their derivation, applications and limitations. Insight: Non-Destructive Testing and Condition Monitoring, 49(7), 409–414. https://doi.org/10.1784/insi.2007.49.7.409
Ghose, B. (2013). Evaluation of probability of detection (POD) and minimum number of exposures required for detection of planar flaw in cylindrical object by radiographic NDE method. Asia Pacific Conference on Non-Destructive Testing, 19, 1-6.
Gianneo, A., Carboni, M., & Giglio, M. (2016a). Feasibility study of a multi-parameter probability of detection formulation for a lamb waves–based structural health monitoring approach to light alloy aeronautical plates. Structural Health Monitoring, 16(2), 225–249. https://doi.org/10.1177/1475921716670841
Gianneo, A., Carboni, M., & Giglio, M. (2016b). Reliability aspects and multi-parameter POD formulation for guided wave based SHM techniques. 19th World Conference on Non-Destructive Testing 2016, 1–11.
Gollwitzer, C., Bellon, C., Deresch, A., & Ewert, U. (2011). On POD estimations with radiographic simulator aRTist. In International Symposium on Digital Industrial Radiology and Computed Tomography (No. DGZfP-BB 128 [Tu. 2.3]) (pp. 1-8). Deutsche Gesellschaft für zerstörungsfreie Prüfung eV (DGZfP).
Goursolle, T., Fauret, T., & Juliac, E. (2016, June 13-17). Effect of data amount on probability of detection estimation: Application to Eddy current testing. In 19th World Conference on Non-Destructive Testing 2016 (pp. 1-8). Munich, Germany.
Guan, X., Zhang, J., Zhou, S., Rasselkorde, E. M., & Abbasi, W. (2014). Probabilistic modeling and sizing of embedded flaws in ultrasonic non-destructive inspections for fatigue damage prognostics and structural integrity assessment. NDT and E International, 61, 1–9. https://doi.org/10.1016/j.ndteint.2013.09.003
Haapalainen, J., & Leskelä, E. (2012, April 16-20). Probability of detection simulations for ultrasonic pulse-echo testing. In 18th World Conference on Nondestructive Testing (pp. 1-5). Durban, South Africa.
He, X., Jiang, X., Guo, J., Xu, L., & Mo, R. (2024). Ultrasonic evaluation of wire-to-terminal joints: integrating XGBoost machine learning with finite element feature analysis. Nondestructive Testing and Evaluation, 1–18. https://doi.org/10.1080/10589759.2024.2304265
Herberich, J. (2009). Applying MIL-HDBK-1823 for POD demonstration on a fluorescent penetrant system. Materials Evaluation, 67(3), 293–301.
Hossain, M., Ziehl, P., Yu, J., Caicedo, J., & Matta, F. (2013). Assessing probability of detection based on acoustic emission associated with fatigue crack extension in steel bridge elements. The American Society for Nondestructive Testing.
Jenson, F., Iakovleva, E., & Dominguez, N. (2011). Simulation supported POD: Methodology and HFET validation case. AIP Conference Proceedings, 1335, 1573–1580. https://doi.org/10.1063/1.3592117
Kanzler, D., & Müller, C. (2016a, June 13-17). Evaluating RT systems with a new POD approach. In Proceedings of the 19th World Conference on Non-Destructive Testing (pp. id-19535). Munich, Germany.
Kanzler, D., & Müller, C. (2016b). How much information do we need? A reflection of the correct use of real defects in POD-evaluations. In AIP Conference Proceedings (Vol. 1706, No. 1). AIP Publishing. https://doi.org/10.1063/1.4940652
Kanzler, D., Müller, C., Pitkänen, J., & Ewert, U. (2012, April 16-20). Bayesian approach for the evaluation of the reliability of non-destructive testing methods: Combination of data from artificial and real defects. In 18th world conference on nondestructive testing (pp. 1-6). Durban, South Africa.
Kanzler, D., Milsch, S., Pavlovic, M., Müller, C., & Pitkänen, J. (2019). Concept of total reliability of NDT methods for inspection of the EB weld of the copper canister used for a long-term storage of spent nuclear fuel. Structural Integrity and NDE Reliability III Concept, 1-6.
Kim, F. H., Pintar, A., Obaton, A. F., Fox, J., Tarr, J., & Donmez, A. (2021). Merging experiments and computer simulations in X-ray computed tomography probability of detection analysis of additive manufacturing flaws. NDT and E International, 119, Article 102416. https://doi.org/10.1016/j.ndteint.2021.102416
Knopp, J. S., & Zeng, L. (2013). Statistical analysis of Hit/Miss data. Materials Evaluation, 71(3), 322–329.
Kojima, M., Takahashi, H., & Kikura, H. (2019). Evaluation of capabilities on ultrasonic testing examiners using probability of defect detection and cumulative failure probability. Journal of Advanced Maintenance, 11(2), 65–78.
Kurz, J. H., Jüngert, A., Dugan, S., & Dobmann, G. (2012, April 16-20). Probability of detection (POD) determination using ultrasound phased array for considering NDT in probabilistic damage assessments. In South-African Insitute for Non-destructive Testing: World Conference on Nondestructive Testing (pp. 1-10). Durban, South Africa.
Kurz, J. H., Jüngert, A., Dugan, S., Dobmann, G., & Boller, C. (2013). Reliability considerations of NDT by probability of detection (POD) determination using ultrasound phased array. Engineering Failure Analysis, 35, 609-617. https://doi.org/10.1016/j.engfailanal.2013.06.008
Le Gratiet, L., Iooss, B., Blatman, G., Browne, T., Cordeiro, S., & Goursaud, B. (2017). Model assisted probability of detection curves: New statistical tools and progressive methodology. Journal of Nondestructive Evaluation, 36(8), 1–12. https://doi.org/10.1007/s10921-016-0387-z
Lei, X., Wirdelius, H., & Rosell, A. (2022). Simulation-based investigation of a probability of detection (POD) model using phased array ultrasonic testing (PAUT) technique. Journal of Nondestructive Evaluation, 41(2), 1–13. https://doi.org/10.1007/s10921-022-00873-2
Lembersky, L., Adams, R., Tamutus, T., & Watson, J. (2012). Suspension cable wire break monitoring using acoustic emission for economic and probability of detection advantages. In Structural Materials Technology 2012 (pp. 169-176). PubGenius Inc.
Marcotte, O., & Liyanage, T. (2017). Nondestructive examination (NDE) used fuel containers probability of detection for increased probability of detection. The American Society for Nondestructive Testing.
Meeker, W. Q. (2000). A methodology for predicting probability of detection for ultrasonic testing. In AIP Conference Proceedings (Vol. 557, No. 1, pp. 1972-1978). AIP Publishing. https://doi.org/10.1063/1.1373994
Mohr, G. A., & Willems, P. (2008, October 25-28). Factors affecting Probability of Detection with Computed Radiography. In 17th World Conference on Nondestructive Testing (pp. 1-8). Shanghai, China.
Peeters, J., Ibarra-Castanedo, C., Khodayar, F., Mokhtari, Y., Sfarra, S., Zhang, H., Maldague, X., Dirckx, J. J. J., & Steenackers, G. (2018). Optimised dynamic line scan thermographic detection of CFRP inserts using FE updating and POD analysis. NDT and E International, 93, 141–149. https://doi.org/10.1016/j.ndteint.2017.10.006
Poudel, A., Galvan-Nunez, S., Lindeman, B., & Gonzalez, F. (2022). A quantitative assessment of historical nondestructive evaluation (NDE) probability of detection (POD) data for railroad tank cars. The American Society for Nondestructive Testing.
Rentala, V. K., Mylavarapu, P., & Gautam, J. P. (2018). Issues in estimating probability of detection of NDT techniques – A model assisted approach. Ultrasonics, 87, 59–70. https://doi.org/10.1016/j.ultras.2018.02.012
Rentala, V. K., Mylavarapu, P., K.Gopinath, Gautam, J. P., & Kumar, V. (2016, November 3-5). Model assisted probability of detection for lognormally distributed defects. In 8th International Symposium on NDT in Aerospace (pp. 1-8). Bangalore, India.
Ribay, G., Mahaut, S., Cattiaux, G., & Sollier, T. (2017, September 4-7). Assessment of the reliability of phased array NDT of coarse grain component based on simulation. In Proceedings of the 7th European-American Workshop on Reliability of NDE (pp. 1–8). Potsdam, Germany. http://www.nde-reliability.de/portals/nde17/BB/21.pdf
Rodat, D., Guibert, F., Dominguez, N., & Calmon, P. (2017). Operational NDT simulator, towards human factors integration in simulated probability of detection. In AIP Conference Proceedings (Vol. 1806, No. 1). AIP Publishing. https://doi.org/10.1063/1.4974719
Schneider, C. R. A., Sanderson, R. M., Carpentier, C., Zhao, L., & Nageswaran, C. (2012, September 11-13). Estimation of probability of detection curves based on theoretical simulation of the inspection process. In 51st Annual Conference of the British Institute of Non-Destructive Testing (pp. 393–404). Northamptonshire, UK.
Spies, M., & Rieder, H. (2018, June 11-15). An approach to the question ‘How to account for human error in MAPOD?’ In 12th European Conference on Non-Destructive Testing (ECNDT 2018) (pp. 1–5). Gothenburg, Sweden. https://www.ndt.net/article/ecndt2018/papers/ecndt-0571-2018.pdf
Stubbs, D. A. (2005). Probability of detection for embedded defects: Needs for ultrasonic inspection of aerospace turbine engine components. AIP Conference Proceedings, 760, 1909–1916. https://doi.org/10.1063/1.1916903
Subair, S. M., Balasubramaniam, K., Rajagopal, P., Kumar, A., Rao, B. P., & Jayakumar, T. (2014). Finite element simulations to predict probability of detection (PoD) curves for ultrasonic inspection of nuclear components. Procedia Engineering, 86, 461–468. https://doi.org/10.1016/j.proeng.2014.11.059
Tai, J. L., Grzejda, R., Sultan, M. T. H., Łukaszewicz, A., Shahar, F. S., Tarasiuk, W., & Rychlik, A. (2023). Experimental investigation on the corrosion detectability of A36 low carbon steel by the method of phased array corrosion mapping. Materials, 16(15), Article 5297. https://doi.org/10.3390/ ma16155297
Tisseur, D., Costin, M., Fournier, S., Reece, C., & Schumm, A. (2019, October 1-3). POD calculation on a radiographic weld inspection with CIVA 11 RT module. In 10th International Conference on NDE in Relation to Structural Integrity for Nuclear and Pressurized Components (JRC-NDE 2013) (pp. 123–129). Cannes, France.
Topp, M., & Strothmann, L. (2021). How can NDT 4.0 improve the Probability of Detection (POD)? E-Journal of Nondestructive Testing (NDT), 26(4), 1–10. http://www.ndt.net/?id=26013
Tschoke, K., Mueller, I., Memmolo, V., Moix-Bonet, M., Moll, J., Lugovtsova, Y., Golub, M., Venkat, R. S., & Schubert, L. (2021). Feasibility of model-assisted probability of detection principles for structural health monitoring systems based on guided waves for fiber-reinforced composites. IEEE Transactions on Ultrasonics, Ferroelectrics, and Frequency Control, 68(10), 3156–3173. https://doi.org/10.1109/TUFFC.2021.3084898
Underhill, P. R., & Krause, T. W. (2011). Quantitative fractography for improved probability of detection (POD) analysis of bolt hole eddy current. Research in Nondestructive Evaluation, 22(2), 92–104. https://doi.org/10.1080/09349847.2011.553349
Underhill, P. R., & Krause, T. W. (2016). Eddy current probability of detection for mid-bore and corner cracks in bolt holes of service material. Research in Nondestructive Evaluation, 27(1), 34–47. https://doi.org/10.1080/09349847.2015.1045642
Underhill, P. R., Uemura, C., & Krause, T. W. (2018). Probability of detection for bolt hole eddy current in extracted from service aircraft wing structures. In AIP Conference Proceedings (Vol. 1949, No. 1). AIP Publishing. https://doi.org/10.1063/1.5031619
Virkkunen, I., Koskinen, T., Papula, S., Sarikka, T., & Hänninen, H. (2019). Comparison of â versus a and Hit/Miss POD-estimation methods: A European viewpoint. Journal of Nondestructive Evaluation, 38, 1-13. https://doi.org/10.1007/s10921-019-0628-z
Volker, A. W. F., Dijkstra, F. H., Terpstra, S., Heerings, H. A. M., & Lont, M. A. (2004, August 30 – September 3). Modeling of NDE reliability: Development of a “POD-Generator”. In Proceedings of the 16th World Conference on Nondestructive Testing (pp. 1-8). Montreal, Canada.
Wall, M., Burch, S., & Lilley, J. (2009). Human factors in POD modelling and use of trial data. Insight: Non-Destructive Testing and Condition Monitoring, 51(10), 553–561. https://doi.org/10.1784/insi.2009.51.10.553
Wright, M. (2016, November 15-17). How to implement a PoD into a highly effective inspection strategy. In NDT in Canada 2016 & 6th International CANDU In-Service Inspection Workshop (pp. 1-8). Burlington, Canada. https://www.ndt.net/search/docs.php3?id=20418
Xu, Z., Zhou, Z., Chen, H., Qu, Z., & Liu, J. (2023). Effects of the wire mesh on pulsed eddy current detection of corrosion under insulation. Nondestructive Testing and Evaluation, 38(2), 233–253. https://doi.org/10.1080/10589759.2022.2102167
Yosifov, M., Reiter, M., Heupl, S., Gusenbauer, C., Fröhler, B., Fernández- Gutiérrez, R., De Beenhouwer, J., Sijbers, J., Kastner, J., & Heinzl, C. (2022). Probability of detection applied to X-ray inspection using numerical simulations. Nondestructive Testing and Evaluation, 37(5), 536–551. https://doi.org/10.1080/10589759.2022.2071892
Yosifov, M., Weinberger, P., Reiter, M., Fröhler, B., Beenhouwer, J. De, Sijbers, J., Kastner, J., & Heinzl, C. (2023). Defect detectability analysis via Probability of defect detection between traditional and deep learning methods in numerical simulations. E-Journal of Nondestructive Testing, 28(3), 2–11. https://doi.org/10.58286/27716
Yusa, N. (2017). Probability of detection model for the non-destructive inspection of steam generator tubes of PWRs. Journal of Physics: Conference Series, 860(1), 6–13. https://doi.org/10.1088/1742-6596/860/1/012032
Yusa, N., Chen, W., & Hashizume, H. (2016). Demonstration of probability of detection taking consideration of both the length and the depth of a flaw explicitly. NDT and E International, 81, 1–8. https://doi.org/10.1016/j.ndteint.2016.03.001
Yusa, N., Tomizawa, T., Song, H., & Hashizume, H. (2018). Probability of detection analyses of eddy current data for the detection of corrosion. Nondestructive Testing and Diagnostics, 4, 3–7. https://doi.org/10.26357/BNiD.2018.031
Zhao, J., Yang, K., Du, X., Yao, S., & Zhao, Y. (2023). Automated quantification of small defects in ultrasonic phased array imaging using AWGA-gcForest algorithm. Nondestructive Testing and Evaluation, 1–22. https://doi.org/10.1080/10589759.2023.2274001
Zhu, J., Min, Q., Wu, J., & Tian, G. Y. (2018). Probability of detection for eddy current pulsed thermography of angular defect quantification. IEEE Transactions on Industrial Informatics, 14(12), 5658–5666. https://doi.org/10.1109/TII.2018.2866443
ISSN 0128-7680
e-ISSN 2231-8526