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Abstract
Non-destructive testing is vitally important in ensuring the safe and economic operation of aging nuclear power plants and other industrial systems. Reliable detection of service induced damage as early as possible allows for improved safety and effective maintenance. At the same time, finding flaws early pushes the NDT methods to the limits of detectability. Thus, effective estimation of NDE reliability is vital to successful application of NDE. While much progress has been made with NDE qualification, quantitative estimation of NDE reliability (namely, probability of detection, POD) is still not commonly used in the nuclear industry. The inspections vary, and providing sufficient empirical data for POD has remained infeasible.
In the RACOON project, inspection reliability and POD is studied with new tools
(eFlaw’s) that allow better empirical data gathering. Based on previous work, in the current project a first of a kind virtual round robin (VRR) with world-wide participation was conducted in collaboration with an international project. The target was the ultrasonic inspection of dissimilar metal welds (DMWs). The VRR demonstrated the capability and efficiency of the approach to determine NDT reliability and performance.
In addition to theoretical reliability, the consistency and repeatability of inspections are crucial to actual field reliability. In a short project ANDIE, now merged to the RACOON project, the application of machine learning to automated data analysis and defect detection was studied. Machine learning (ML) powered ultrasonic inspection has proven highly feasible approach to increase reliability, repeatability and efficiency of mechanized ultrasonic inspection. Originally short project ANDIE demonstrated the capability to use virtual flaws for training the ML models. In year 2020 the effect of different flaw types and sizes used in training the ML model was studied to have impact on detectability.
In the RACOON project, inspection reliability and POD is studied with new tools
(eFlaw’s) that allow better empirical data gathering. Based on previous work, in the current project a first of a kind virtual round robin (VRR) with world-wide participation was conducted in collaboration with an international project. The target was the ultrasonic inspection of dissimilar metal welds (DMWs). The VRR demonstrated the capability and efficiency of the approach to determine NDT reliability and performance.
In addition to theoretical reliability, the consistency and repeatability of inspections are crucial to actual field reliability. In a short project ANDIE, now merged to the RACOON project, the application of machine learning to automated data analysis and defect detection was studied. Machine learning (ML) powered ultrasonic inspection has proven highly feasible approach to increase reliability, repeatability and efficiency of mechanized ultrasonic inspection. Originally short project ANDIE demonstrated the capability to use virtual flaws for training the ML models. In year 2020 the effect of different flaw types and sizes used in training the ML model was studied to have impact on detectability.
| Original language | English |
|---|---|
| Title of host publication | SAFIR2022 - The Finnish Research Programme on Nuclear Power Plant Safety 2019-2022 |
| Publisher | VTT Technical Research Centre of Finland |
| Pages | 300-312 |
| Number of pages | 13 |
| ISBN (Electronic) | 978-951-38-8743-8 |
| Publication status | Published - Mar 2021 |
| MoE publication type | D2 Article in a professional research book (incl. an introduction by the editor) |
Publication series
| Name | VTT Technology |
|---|---|
| Volume | 383 |
| ISSN (Electronic) | 2242-1211 |
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Dive into the research topics of 'Non-destructive examination of NPP primary circuit components and reliability of inspection (RACOON)'. Together they form a unique fingerprint.Projects
- 1 Finished
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Non destructive examination of NPP primary circuit components and concrete infrastucture
Puttonen, J. (Principal investigator), Ojala, T. (Project Member), Al-Neshawy, F. (Project Member) & Huuskonen-Snicker, E. (Project Member)
01/01/2017 → 31/01/2018
Project: Other external funding: Other public funding