# Using Deep Learning to Automate the Detection of Flaws in Nuclear Fuel Channel UT Scans

arXiv: https://arxiv.org/abs/2102.13635 · HTML: https://mlsanity.com/paper/2102.13635/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 1 of its 32 references that are in the map.
- First posted on arXiv 26 Feb 2021.

## Builds on (most cited first, 1 of 1)

- [Model-Based Iterative Reconstruction for One-Sided Ultrasonic Non-Destructive Evaluation](https://mlsanity.com/paper/1808.03336/) (2018)

## Built on by (most cited first, 2 of 2)

- [Classification of Safety Events at Nuclear Sites using Large Language Models](https://mlsanity.com/paper/2409.00091/) (2024)
- [Subtractor-Based CNN Inference Accelerator](https://mlsanity.com/paper/2310.01022/) (2023)

## Abstract (arXiv)

Nuclear reactor inspections are critical to ensure the safety and reliability of a nuclear facility's operation. In Canada, Ultrasonic Testing (UT) is used to inspect the health of pressure tubes which are part of Canada's Deuterium Uranium (CANDU) reactor's fuel channels. Currently, analysis of UT scans is performed by manual visualization and measurement to locate, characterize, and disposition flaws. Therefore, there is motivation to develop an automated method that is fast and accurate. In this paper, a proof of concept (PoC) that automates the detection of flaws in nuclear fuel channel UT scans using a convolutional neural network (CNN) is presented. The CNN model was trained after constructing a dataset using historical UT scans and the corresponding inspection results. The requirement for this prototype was to identify the location of at least a portion of each flaw in UT scans while minimizing false positives (FPs). The proposed CNN model achieves this target by automatically identifying at least a portion of each flaw where further manual analysis is performed to identify the width, the length, and the type of the flaw.