A Deep Learning-Based Method to Detect Hot-Spots in the Visible Video Diagnostics of Wendelstein 7-X

Abstract

Wendelstein 7-X (W7-X) is currently the largest optimized stellarator in operation in the world. Its main objective is to demonstrate long pulse operation and to investigate the suitability of this type of fusion device for a power plant. Maintaining the safety of the first wall is critical to achieving the desired discharge times of approximately 30 min while keeping a steady-state condition. We present a deep learning-based solution to detect the unexpected plasma-wall and plasma-object interactions, so-called hot-spots, in the images of the Event Detection Intelligent Camera (EDICAM) system. These events can pose a serious threat to the safety of the first wall, therefore, to the operation of the device. We show that sufficiently training a neural network with relatively small amounts of data is possible using our approach of mixing the experimental dataset with new images containing so-called synthetic hot-spots generated by us. Diversifying the dataset with synthetic hot-spots increases performance and can make up for the lack of data. The best performing YOLOv5 Small model processes images in 168 ms on average during inference, making it a good candidate for real-time operation. To our knowledge, we are the first ones to be able to detect events in the visible spectrum in stellarators with high accuracy, using neural networks trained on small amounts of data while achieving near-real-time inference times.

Keywords

• first wall protection
• video diagnostics
• deep learning