{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2023,3,31]],"date-time":"2023-03-31T16:41:15Z","timestamp":1680280875341},"reference-count":25,"publisher":"Walter de Gruyter GmbH","issue":"6","content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2021,6,25]]},"abstract":"<jats:title>Abstract<\/jats:title>\n               <jats:p>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.\u2009g., 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.<\/jats:p>","DOI":"10.1515\/auto-2020-0064","type":"journal-article","created":{"date-parts":[[2021,5,27]],"date-time":"2021-05-27T15:43:46Z","timestamp":1622130226000},"page":"538-549","source":"Crossref","is-referenced-by-count":2,"title":["Probabilistic model-based fault diagnosis for the cavities of the European XFEL"],"prefix":"10.1515","volume":"69","author":[{"given":"Ayla","family":"Nawaz","sequence":"first","affiliation":[{"name":"Institute for Electrical Engineering in Medicine , University of L\u00fcbeck , Ratzeburger Allee 160 , L\u00fcbeck , Germany"}]},{"given":"Christian","family":"Herzog n\u00e9 Hoffmann","sequence":"additional","affiliation":[{"name":"Institute for Electrical Engineering in Medicine , University of L\u00fcbeck , Ratzeburger Allee 160 , L\u00fcbeck , Germany"}]},{"given":"Jan","family":"Gra\u00dfhoff","sequence":"additional","affiliation":[{"name":"Institute for Electrical Engineering in Medicine , University of L\u00fcbeck , Ratzeburger Allee 160 , L\u00fcbeck , Germany"}]},{"given":"Sven","family":"Pfeiffer","sequence":"additional","affiliation":[{"name":"Deutsches Elektronen-Synchrotron (DESY) , Notkestr. 85 , Hamburg , Germany"}]},{"given":"Gerwald","family":"Lichtenberg","sequence":"additional","affiliation":[{"name":"Faculty of Life Sciences , Hamburg University of Applied Science , Ulmenliet 20 , Hamburg , Germany"}]},{"given":"Philipp","family":"Rostalski","sequence":"additional","affiliation":[{"name":"Institute for Electrical Engineering in Medicine , University of L\u00fcbeck , Ratzeburger Allee 160 , L\u00fcbeck , Germany"}]}],"member":"374","published-online":{"date-parts":[[2021,5,27]]},"reference":[{"key":"2023033111555180695_j_auto-2020-0064_ref_001_w2aab3b7b1b1b6b1ab2ab1Aa","doi-asserted-by":"crossref","unstructured":"Baoping Cai, Lei Huang and Min Xie. \u201cBayesian networks in fault diagnosis.\u201d IEEE Transactions on Industrial Informatics 13.5 (2017), 2227\u20142240.","DOI":"10.1109\/TII.2017.2695583"},{"key":"2023033111555180695_j_auto-2020-0064_ref_002_w2aab3b7b1b1b6b1ab2ab2Aa","unstructured":"Ali Abdollahi. \u201cOptimization and Bayesian Inference in Model-Based Decision Making.\u201d Doctoral dissertation. 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