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System modeling based on machine learning for anomaly detection and predictive maintenance in industrial plants

: Kroll, Björn; Schaffranek, David; Schriegel, Sebastian; Niggemann, Oliver

Postprint urn:nbn:de:0011-n-3236893 (3.2 MByte PDF)
MD5 Fingerprint: 8ef4e3d81fa0c0863853eaf6d2901145
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Erstellt am: 27.1.2015

Institute of Electrical and Electronics Engineers -IEEE-; IEEE Industrial Electronics Society -IES-:
ETFA 2014, 19th IEEE International Conference on Emerging Technologies and Factory Automation : 16.- 19 September 2014, Barcelona, Spain
Piscataway, NJ: IEEE, 2014
ISBN: 978-1-4799-4846-8
ISBN: 978-1-4799-4845-1
ISBN: 978-1-4799-4844-4
7 S.
International Conference on Emerging Technologies and Factory Automation (ETFA) <19, 2014, Barcelona>
Konferenzbeitrag, Elektronische Publikation
Fraunhofer IOSB ()

Electricity, water or air are some Industrial energy carriers which are struggling under the prices of primary energy carriers. The European Union for example used more 20.000.000 GWh electricity in 2011 based on the IEA Report [1]. Cyber Physical Production Systems (CPPS) are able to reduce this amount, but they also help to increase the efficiency of machines above expectations which results in a more cost efficient production. Especially in the field of improving industrial plants, one of the challenges is the implementation of anomaly detection systems. For example as wear-level detection, which improves maintenance cycles and thus leads to a better energy usage. This paper presents an approach that uses timed hybrid automata of the machines normal behavior for a predictive maintenance of industrial plants. This hybrid model reduces discrete and continuous signals (e.g. energy data) to individual states, which refer to either the present condition of the machines. This allows an effective anomaly detection by implementing a combined data acquisition and anomaly detection approach, and the outlook for other applications, such as a predictive maintenance planning. Finally, this methodology is verified by three different industrial applications.