等离子体诊断模型中传感器鲁棒性的基准测试：基于TokaMark的系统评估

摘要

托卡马克聚变装置的等离子体诊断模型几乎完全基于干净、完整的传感器数据进行评估。然而在实际运行中，聚变诊断系统经常出现故障：采集系统启动延迟、个别传感器失效、信号丢失现象在等离子体破裂临近时尤为集中。我们基于包含11,573次MAST放电的TokaMark数据集，首次提出了等离子体诊断机器学习的系统化鲁棒性基准测试，评估了XGBoost、LSTM、Transformer及TokaMark CNN基线模型在六种物理失效场景和三种插补策略下的表现。我们引入鲁棒性评分（RS）以实现跨架构的标准化比较。核心发现是：临近破裂的传感器失效（在窗口末段时间步注入干扰）会导致序列模型性能崩溃（LSTM的NRMSE上升+212%），而统计特征模型保持相对稳定（XGBoost仅上升+37%）。前向填充插补可几乎消除序列模型因随机丢失导致的性能退化（LSTM从+57%降至约0%），但当窗口末端受损时收效甚微。基于实际破裂时间戳的放电级报警评估显示，LSTM在临近传感器失效下报警检测率骤降至TPR=0.00，而均值填充插补可将其恢复至TPR=1.00，这一结果与NRMSE观测模式完全相反。在所有架构中，等离子体电流被确认为最关键的单一诊断参数（移除后模型性能下降+73%至+140%）。相关代码、数据集及训练模型检查点已开源：https://github.com/Neerav-Gupta/tokamark-robustness。

English

Plasma diagnostic models for tokamak fusion devices are almost universally evaluated on clean, complete sensor data. In practice, fusion diagnostics fail regularly: acquisition systems start late, individual sensors die, and signal dropouts cluster precisely when a plasma disruption is approaching. We present the first systematic robustness benchmark for plasma diagnostic ML using the TokaMark dataset of 11,573 MAST shots, evaluating XGBoost, LSTM, Transformer, and the TokaMark CNN baseline across six physically-grounded failure scenarios and three imputation strategies. We introduce the Robustness Score (RS) for standardized cross-architecture comparison. Our central finding is that disruption-proximate sensor failure (corruption injected in the final window timesteps) collapses sequence model performance (LSTM +212% NRMSE) while a statistical feature model remains comparatively stable (XGBoost +37%). Forward-fill imputation eliminates nearly all degradation from random dropout for sequence models (LSTM +57% to ~0%), but offers little help when the end of the window is corrupted. Shot-level alarm evaluation using ground-truth disruption timestamps reveals that LSTM alarm detection collapses to TPR=0.00 under proximate sensor failure, while mean-fill imputation recovers it to TPR=1.00, a reversal of the pattern observed in NRMSE. Plasma current emerges as the single most critical diagnostic across all architectures (+73% to +140% upon removal). Code, data, and trained checkpoints are available at https://github.com/Neerav-Gupta/tokamark-robustness.