The beam interruptions (interlocks) of particle accelerators, despite being
necessary safety measures, lead to abrupt operational changes and a substantial
loss of beam time. A novel time series classification approach is applied to
decrease beam time loss in the High Intensity Proton Accelerator complex by
forecasting interlock events. The forecasting is performed through binary
classification of windows of multivariate time series. The time series are
transformed into Recurrence Plots which are then classified by a Convolutional
Neural Network, which not only captures the inner structure of the time series
but also utilizes the advances of image classification techniques. Our best
performing interlock-to-stable classifier reaches an Area under the ROC Curve
value of $0.71 \pm 0.01$ compared to $0.65 \pm 0.01$ of a Random Forest model,
and it can potentially reduce the beam time loss by $0.5 \pm 0.2$ seconds per
interlock.

利用时间序列分类方法对高强度质子加速器的中断事件进行预测，通过二元分类多元时间序列的窗口来减少光束时间损失。通过将时间序列转换为复现图并使用卷积神经网络进行分类，使得分类器的性能得到了提高。最佳分类器的 ROC 曲线下面积值为 0.71，相对于随机森林模型的 0.65，可以潜在地减少每次中断 0.5 秒的光束时间损失。