A Two-Stage Machine Learning-Aided Approach for Quench Identification at the European XFEL

TL;DR

This paper presents a novel two-stage machine learning-based method for quench detection at the European XFEL, improving fault detection and isolation accuracy over existing systems.

Contribution

The paper introduces a new two-stage approach combining analytical redundancy and data-driven clustering for more effective quench identification.

Findings

Enhanced detection accuracy compared to current systems

Effective distinction between quenches and other faults

Utilization of k-medoids with multiple similarity measures

Abstract

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.