  Nuclear reactor inspections are critical to ensure the safety and reliability
of plants operation. Inspections occur during planned outages and include the
inspection of the reactor's fuel channels. In Canada, Ultrasonic Testing (UT)
is used to inspect the health of fuel channels in Canada's Deuterium Uranium
(CANDU) reactors. Currently, analysis of the UT scans is performed by manual
visualization and measurement to locate, characterize, and disposition flaws.
Therefore, there is a 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. This industry research was conducted at
Alithya Digital Technology Corporation in Pickering, Ontario, Canada. The CNN
model was trained after constructing a dataset using historical UT scans and
the corresponding inspection results. This data was obtained from a large
nuclear power generation company in Ontario. The requirement for this prototype
was to identify the location of at least a portion of each flaw in fuel channel
scans while minimizing false positives (FPs). This allows for automatic
detection of the location of each flaw where further manual analysis is
performed to identify the extent and the type of the flaw. Based on the defined
requirement, the proposed model was able to achieve 100% accuracy for UT scans
with minor chatter and a 100% sensitivity with minimal FPs for complicated UT
scans with severe chatter using 18 UT full test scans.
