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Author Windmann, S. ♦ Shuo Jiao ♦ Niggemann, O. ♦ Borcherding, H.
Source IEEE Xplore Digital Library
Content type Text
Publisher Institute of Electrical and Electronics Engineers, Inc. (IEEE)
File Format PDF
Copyright Year ©2013
Language English
Subject Domain (in DDC) Technology ♦ Engineering & allied operations ♦ Applied physics
Subject Keyword Energy consumption ♦ Learning automata ♦ Automata ♦ Mathematical model ♦ Kalman filters ♦ Equations ♦ Standards
Abstract In the presented work, the detection of anomalous energy consumption in hybrid industrial production systems is investigated. A model-based approach with a timed hybrid automaton as overall system model is employed for anomaly detection. The approach is based on the assumption of several system modes, i.e. phases with continuous system behavior. Transitions between the modes are attributed to discrete control events such as on/off signals. The underlying discrete event system which comprises both system modes and transitions is modeled as finite state machine. The focus of this paper is set on the modeling of the energy consumption in the particular system modes. Sequences of stochastic state space models are employed for this purpose. Model learning and anomaly detection for this approach are considered. The proposed approach is further evaluated in a small model factory. The experimental results show significant improvements compared to existing approaches to anomaly detection in hybrid industrial systems.
Description Author affiliation: Applic. Center Ind. Autom., Fraunhofer Inst. IOSB-INA, Germany (Windmann, S.; Shuo Jiao; Niggemann, O.) || LLA - Power Electron. & Electr. Drives Lab., Ostwestfalen-Lippe Univ. of Appl. Sci., Lemgo, Germany (Borcherding, H.)
ISBN 9781479907526
Educational Role Student ♦ Teacher
Age Range above 22 year
Educational Use Research ♦ Reading
Education Level UG and PG
Learning Resource Type Article
Publisher Date 2013-07-29
Publisher Place Germany
Rights Holder Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Size (in Bytes) 1.04 MB
Page Count 6
Starting Page 194
Ending Page 199

Source: IEEE Xplore Digital Library