In this study, a full convolutional neural network is trained on a large database of experimental EAST data to classify disruptive discharges and distinguish them from non-disruptive discharges. The database contains 14 diagnostic parameters from the ∼10⁴ discharges (disruptive and non-disruptive). The test set contains 417 disruptive discharges and 999 non-disruptive discharges, which are used to evaluate the performance of the model. The results reveal that the true positive (TP) rate is ∼ 0.827, while the false positive (FP) rate is ∼0.067. This indicates that 72 disruptive discharges and 67 non-disruptive discharges are misclassified in the test set. The FPs are investigated in detail and are found to emerge due to some subtle disturbances in the signals, which lead to misjudgment of the model. Therefore, hundreds of non-disruptive discharges from training set, containing time slices of small disturbances, are artificially added into the training database for retraining the model. The same test set is used to assess the performance of the improved model. The TP rate of the improved model increases up to 0.875, while its FP rate decreases to 0.061. Overall, the proposed data-driven predicted model exhibits immense potential for application in long pulse fusion devices such as ITER.

在这项研究中，在实验EAST数据的大型数据库上训练了完整的卷积神经网络，以对破坏性放电进行分类，并将其与非破坏性放电区分开。数据库包含约10 ⁴中的14个诊断参数放电（破坏性和非破坏性）。该测试集包含417个破坏性放电和999个非破坏性放电，用于评估模型的性能。结果表明，真阳性（TP）率为〜0.827，而假阳性（FP）率为〜0.067。这表明测试集中有72个破坏性放电和67个非破坏性放电被错误分类。对FP进行了详细研究，发现它们是由于信号中一些细微的干扰而出现的，这些干扰会导致模型的错误判断。因此，人为地将来自训练集的数百个无干扰排放（包含小干扰的时间片）人为地添加到训练数据库中，以对模型进行再训练。同一测试集用于评估改进模型的性能。改进模型的TP速率增加到0.875，而FP比率减小到0.061。总体而言，所提出的数据驱动的预测模型在诸如ITER的长脉冲融合设备中具有巨大的应用潜力。