Probabilistische, modellbasierte Fehlerdiagnose für die Kavitäten des European XFEL

Translated title of the contribution: Probabilistic model-based fault diagnosis for the cavities of the European XFEL

Ayla Nawaz*, Christian Herzog né Hoffmann, Jan Graßhoff, Sven Pfeiffer, Gerwald Lichtenberg, Philipp Rostalski

*Corresponding author for this work
2 Citations (Scopus)


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.

Translated title of the contributionProbabilistic model-based fault diagnosis for the cavities of the European XFEL
Original languageGerman
Issue number6
Pages (from-to)538-549
Number of pages12
Publication statusPublished - 25.06.2021

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