This paper introduces a machine learning-aided fault detection and isolation method applied to the case study of quench identification at the European X-Ray Free-Electron Laser. The plant utilizes 800 superconducting radio-frequency cavities in order to accelerate electron bunches to high energies of up to 17.5 GeV. Various faulty events can disrupt the nominal functioning of the accelerator, including quenches that can lead to a loss of the superconductivity of the cavities and the interruption of their operation. In this context, our solution consists in analyzing signals reflecting the dynamics of the cavities in a two-stage approach. (I) Fault detection that uses analytical redundancy to process the data and generate a residual. The evaluation of the residual through the generalized likelihood ratio allows detecting the faulty behaviors. (II) Fault isolation which involves the distinction of the quenches from the other faults. To this end, we proceed with a data-driven model of the k-medoids algorithm that explores different similarity measures, namely, the Euclidean and the dynamic time warping. Finally, we evaluate the new method and compare it to the currently deployed quench detection system, the results show the improved performance achieved by our method.

翻译：本文介绍了一种应用于欧洲X射线自由电子激光器淬灭识别案例的机器学习辅助故障检测与隔离方法。该装置利用800个超导射频腔将电子束团加速至高能状态，最高能量可达17.5 GeV。各类故障事件可能干扰加速器的正常运行，其中淬灭故障会导致腔体超导性丧失并中断其运行。在此背景下，我们的解决方案采用两阶段方法分析反映腔体动态特性的信号：（I）故障检测阶段利用解析冗余处理数据并生成残差，通过广义似然比评估残差以实现故障行为检测；（II）故障隔离阶段通过数据驱动的k-medoids算法模型区分淬灭故障与其他故障，该模型探索了欧几里得距离与动态时间规整两种相似性度量。最终，我们评估了新方法并与当前部署的淬灭检测系统进行对比，结果表明本方法实现了性能提升。