Abstract
Assurance of the normal condition of components is necessary for safe continued operation. Manual visual inspection may be prone to large volume of inspection data, making the manual assessment laborious and lengthy. This project develops a decision-support tool based on deep learning to automatically detect crack features in the inspection footage of superheater tube plate upper radius region.
| Original language | English |
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| Number of pages | 1 |
| Publication status | Published - 28 Jun 2023 |
| Event | Nuclear Decommissioning Authority Innovation and Technology Roadshow - Glasgow, UK Duration: 28 Jun 2023 → 28 Jun 2023 https://www.strath.ac.uk/research/advancednuclearresearchcentre/news/ndaevent28june23/ |
Exhibition
| Exhibition | Nuclear Decommissioning Authority Innovation and Technology Roadshow |
|---|---|
| Period | 28/06/23 → 28/06/23 |
| Internet address |
Keywords
- automated anomaly detection
- nuclear power plant inspection
- remote visual inspection (RVI)