Anomaly Detection for Cavity Signals - Results from the European XFEL

The data throughput of the European XFEL DAQ is about 1.5 Gb/s. Data depicting the cavity signal behavior is currently only saved manually. This either happens, when cavity tests are being performed, or an operator detects a fault in the cavity system, that has to be further investigated. Those instances of interest are neither systematically nor automatically stored. It can therefore be assumed that unwanted or degraded cavity behavior is detected late or not at all. It is proposed to change the focus from detecting known faults (such as quenches) to additionally detect anomalies in the cavity system behavior. In order to detect anomalies in the cavity signals, an algorithm is proposed using a cavity model. It aims on finding those data sets, which diverge from the nominal cavity behavior, saving those instances for later analysis. The nominal behavior is defined by the cavity electromagnetic resonance model with beam loading as well as the model for the mechanical oscillations due to the Lorentz Forces. By using such an approach, the detection of anomalies, as well as faults could be automated. This contribution aims to summarize the influence of beam loading on the detection and gives examples for anomalies that were found in several cavities

Self-organized Critical Control using a model-based Quench Detection for the European XFEL

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Self-Organzied Critical Control for the European XFEL Using Black Box Parameter Identification for the Quench Detection System

The European Free Electron Laser (XFEL) consists of a large and complex plant, with many cost intensive and technological high-end components. It is therefore important that the XFEL can be operated reliably and safely using exception handling and fault detection systems. A crucial part of the system are the superconducting cavities for which especially quenches, i.e. the break down of the superconductivity have to be avoided. The paper shows the interaction of the fault detection system with the Low Level RF (LLRF) control System to maximize the field gradients. This is an example for process supervision, which can neither be classified as fault-tolerant, nor is it a reconfiguration system, but uses the result of the fault detection to operate the system at its fault critical limit.This scheme simulates a system which behaves like a selforganized critical system, and drives the process at its critical performance limit. It is therefore called Self-organized CriticalControl (SOCC). The paper shows the basic set-up and quench detection methods of the European XFEL and gives an example for an application of SOCC

Physical Parameter Identification of Cross-Coupled Gun and Buncher Cavity at REGAE

A reasonable description of the system dynamics is one of the key elements to achieve high performance control for accelerating modules. This paper depicts the system identification of a cross-coupled pair of cavities for the Relativistic Electron Gun for Atomic Exploration - REGAE. Two normal conducting copper cavities driven by a single RF source accelerate and compress a low charge electron bunch with sub 10 fs length at a repetition rate up to 50 Hz. It is shown how the model parameters of the cavities and the attached radio frequency subsystem are identified from data generated at the REGAE facility

Koopman-based Kalman Filter for Fault Detection for the Superconducting Radio Frequency Cavities of the European XFEL

The Koopman operator is a novel approach to embed nonlinear dynamics into linear models. This work shows its successful application for fault detection to a large-scale facility with challenging real-time requirements: the European XFEL, which is the worldwide largest linear particle accelerator. We concentrate on the superconducting radio-frequency cavities, from which 808 exist and whose effective operation directly influences the performance of the whole facility. Thus, a proper fault detection scheme is desired. While a nonlinear state-spacedescription of the cavity dynamics is well-known, its usage along with an unscented Kalman filter is not able to cope with the challenging online implementation requirements. Therefore, in this paper, we apply the Koopman operator technique to identify a finite-dimensional linear approximation of the nonlinear system. For the data-driven identification, the model knowledge is exploited by choosing physically motivated basis functions. With the linear approximation at hand, a linear Kalman filter can be applied. Results are presented for real experimental data. Compared to the unscented Kalman filter, the same detection capability but a speed-up of three orders of magnitude in calculation time can be achieved with the Koopman-based Kalman filter, which enables its implementation to the real facility

Probabilistische, modellbasierte Fehlerdiagnose für die Kavitäten des European XFEL

The European X-ray Free Electron Laser (EuXFEL) is a complex system with many interconnected components and sensor measurements. We use factor graphs to systematically design a probabilistic fault diagnosis method for its cavity system. This approach is expandable to further subsystems and considers uncertainties from measurements and modeling. After representing a model of the cavity system in the factor graph framework, we infer marginal distributions, e. g., of the fault classes using tabulated message-passing definitions. The emerging fault diagnosis method consists of an unscented Kalman filter-based residual generator and an evaluation of the residuals using a Gaussian mixture model. We include message-passing definitions for the training of the Gaussian Mixture Model from noisy data using the expectation-maximization algorithm

Probabilistic Model-Based Fault Diagnosis for the Cavities of the European XFEL

The European X-ray Free Electron Laser (EuXFEL) is a complex system with many interconnected components and sensor measurements. We use factor graphs to systematically design a probabilistic fault diagnosis method for its cavity system. This approach is expandable to further subsystems and considers uncertainties from measurements and modeling. After representing a model of the cavity system in the factor graph framework, we infer marginal distributions, e. g., of the fault classes using tabulated message-passing definitions. The emerging fault diagnosis method consists of an unscented Kalman filter-based residual generator and an evaluation of the residuals using a Gaussian mixture model. We include message-passing definitions for the training of the Gaussian Mixture Model from noisy data using the expectation-maximization algorithm