The integrity of tokamak plasma diagnostic data is critical for physics research and the development of experimental fusion device. Sensor failures, data acquisition errors, or limitations in diagnostic systems (e.g. absent electron temperature diagnostic) pose significant challenges for experimental data analysis, physical integrated simulation, and the design and optimization of tokamak experiment. To address this, we propose a data-driven machine learning approach based on time series extrinsic regression (TSER) to reconstruct missing electron temperature data in tokamak experiments. By constructing and training TSER models, we can effectively reconstruct missing electron temperature measurement while capturing the complex interrelationships within multiple time series signals. This study provides a solution for enhancing the integrity and reliability of tokamak diagnostic, thereby strengthening the foundation for future fusion research. Comprehensive test and evaluation results show that our method is capable of achieving a confidence level of over 95.9% to ensure that the reconstruction of electron temperature throughout the discharge process is within double the standard deviation error of the true value, meeting the stringent requirements of physics research and integrated simulations.
This paper presents a machine learning approach to reconstruct missing electron temperature data in tokamak experiments, which is crucial for fusion research and device optimization. The method uses time series extrinsic regression to capture complex relationships between multiple sensor signals and effectively estimate the missing electron temperature.