Probabilistic model-based fault diagnosis for the cavities of the European XFEL

Abstract

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.

Anomaly Detection at the European XFEL using a Parity Space based Method

“…The electrical and mechanical behaviour of SRF cavities is well understood and can be modelled [4]. Thus, model-based fault detection methods can be used to detect faults as it has been demonstrated in [5][6][7]. These model-based approaches provide a promising insight on anomaly detection and categorization.…”