Ensuring the reliability of plasma diagnostic data is essential for both safe operation and accurate analysis in Magnetic confinement fusion (MCF) devices. However, diagnostic signals are often corrupted by electromagnetic interference, hardware faults, or neutron irradiation, resulting in erroneous diagnostic data. To address this issue, a novel Real-Time Time-Domain Global Similarity (RT-TDGS) method based on machine learning is proposed. RT-TDGS transforms the multi-label classification problem of identifying erroneous channels into a binary classification of physical similarity between pairs of channels, exhibiting intrinsic robustness against variations in discharge parameters. During feature extraction, multiple evaluation metrics are employed to quantify the similarity between pairs of channels after feature engineering, and a machine learning model is then used to classify and identify erroneous channels based on these features. In the training phase, a multi-scale temporal sampling strategy is introduced, which constructs an augmented dataset by extracting features at different temporal scales to enhance the classification accuracy across various temporal scales of the experiments. Applied to the Experimental Advanced Superconducting Tokamak POlarimeter-INTerferomete system, the method achieves an accuracy of 0.9647 with an average processing time of 0.59 ms, fully satisfying real-time requirements. The RT-TDGS method significantly improves the reliability of real-time plasma control data and shows broad application potential in MCF devices.
The joint recognition of multi-MHD instabilities on HL-2A