Study on the winding quality for spiral HTS cables based on AI detection model

Abstract

The development of high temperature superconducting (HTS) conductors is leading to the diverse structure designs of HTS cable. (RE)Ba2Cu3Ox (REBCO) tapes using spiral geometry has been a popular compact HTS cable structure, which is in the critical stage of engineering production and application. However, the winding quality of REBCO tapes is unstable for spiral HTS cables, because of the different winding methods like manual winding, device-assisted winding, or automatic winding. Although automatic winding will be the first choice for the actual applications by spiral HTS cables, the related winding quality is not monitored effectively yet. In this paper, we first discuss the possible influence of the winding quality on the critical current performance of spiral HTS cables. Then, an artificial intelligence (AI) based method is implemented to realize the detection model for the winding quality. From image data preparation to AI detection and postprocessing, the detection model provides the final results to show the winding intervals as a binary image. Through the intuitive analysis and the evaluation metrics, both error and correct winding conditions obtain acceptable detection results, and the correct one has a better performance. The identification of the winding intervals will help to determine the monitoring strategy for the spiral HTS cable fabrication.

Artificial intelligence methods for applied superconductivity: material, design, manufacturing, testing, operation, and condition monitoring

“…Winding methods also impact the quality of the cable and could increase the possibility of weak points and burnout during the operation of the cable. To avoid this, an AI-based technique with three steps was presented in [133] to analyse the quality of winding in a CORC cable. The first step was data preparation based on video of winding process and turning them into a couple of image samples.…”

Artificial intelligence, machine learning, deep learning, and big data techniques for the advancements of superconducting technology: a road to smarter and intelligent superconductivity

“…The development of new high-quality HTS tapes has opened up opportunities for device manufacturers to explore novel structural designs for HTS cables, including the spiral geometry, which has gained popularity as a compact HTS cable structure. However, one of the main challenges in manufacturing spiral HTS cables lies in the instability of the winding quality due to various winding methods, such as manual winding, device-assisted winding, or automatic winding [19]. In [19], an AI-based model is developed to monitor the winding quality and prevent errors during automatic winding.…”

“…However, one of the main challenges in manufacturing spiral HTS cables lies in the instability of the winding quality due to various winding methods, such as manual winding, device-assisted winding, or automatic winding [19]. In [19], an AI-based model is developed to monitor the winding quality and prevent errors during automatic winding. The model encompasses image data preparation, AI detection, and post-processing, ultimately providing the final results in the form of a binary image that shows the winding intervals.…”

“…The identification of these winding intervals helps determine the monitoring strategy for the fabrication of spiral HTS cables. DL techniques were successfully employed in [19] to achieve effective real-time interval detection results.…”