DocumentCode :
1792527
Title :
System modeling based on machine learning for anomaly detection and predictive maintenance in industrial plants
Author :
Kroll, Bjorn ; Schaffranek, David ; Schriegel, Sebastian ; Niggemann, Oliver
Author_Institution :
Applic. Center for Ind. Autom., Fraunhofer IOSB-INA, Lemgo, Germany
fYear :
2014
fDate :
16-19 Sept. 2014
Firstpage :
1
Lastpage :
7
Abstract :
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.
Keywords :
automata theory; industrial plants; learning (artificial intelligence); maintenance engineering; production engineering computing; security of data; CPPS; anomaly detection; anomaly detection systems; cost efficient production; cyber physical production systems; data acquisition; industrial plants; machine learning; predictive maintenance; system modeling; timed hybrid automata; wear-level detection; Data acquisition; Data models; Industrial plants; Learning automata; Servers; Synchronization;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Emerging Technology and Factory Automation (ETFA), 2014 IEEE
Conference_Location :
Barcelona
Type :
conf
DOI :
10.1109/ETFA.2014.7005202
Filename :
7005202
Link To Document :
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