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Evaluation of model-based condition monitoring systems in industrial application cases

: Windmann, Stefan; Eickmeyer, Jens; Jungbluth, Florian; Badinger Johann; Niggemann, Oliver

Fulltext urn:nbn:de:0011-n-3701373 (488 KByte PDF)
MD5 Fingerprint: 2b9e46335f01f625d76106df38b55b1f
Created on: 15.12.2015

Niggemann, Oliver (Ed.); Beyerer, Jürgen (Ed.) ; Fraunhofer-Institut für Optronik, Systemtechnik und Bildauswertung -IOSB-, Karlsruhe:
ML4CPS 2015, 1st Conference on Machine Learning for Cyber Physical Systems and Industry 4.0. CD-ROM : Centrum Industrial IT, Lemgo, Germany, October 1st and 2nd, 2015
Lemgo, 2015
6 pp.
Conference on Machine Learning for Cyber Physical Systems and Industry 4.0 (ML4CPS) <1, 2015, Lemgo>
Conference Paper, Electronic Publication
Fraunhofer IOSB ()

In this paper, model-based condition monitoring methods are investigated. Reliable process monitoring allows costs and risks to be reduced by the early detection of faults and problems in the process behavior and the prevention of component failures or in extreme cases a production stop of the complete plant. The principal of model-based condition monitoring consists of comparing the actual process behavior with the behavior as predicted from process models. For this purpose, a Hidden Markov Model and a method based on principal component analysis are applied. Both methods are evaluated in industrial application cases. In doing so, F-measures of 88:25% and 98:84% are achieved for a wind power station and a glue production plant, respectively.