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Title: An application of autoregressive hidden Markov models for identifying machine operations
Authors: Pantazis, Dimitrios
Rodriguez, Adrian Ayastuy
Conway, Paul P.
West, Andrew A.
Keywords: Autoregressive
Hidden Markov model (HMM)
Manufacturing processes
Intrusive load monitoring (ILM)
Issue Date: 2016
Publisher: © The Authors. Published by IOS Press
Citation: 2016. An application of autoregressive hidden Markov models for identifying machine operations. IN: Goh, Y.M. and Case, K. (eds.) Advances in Manufacturing Technology XXX: Proceedings of the 14th International Conference on Manufacturing Research, Loughborough University, September 6–8, pp. 193-198.
Series/Report no.: Advances in Transdisciplinary Engineering;3
Abstract: Due to increasing energy costs there is a need for accurate management and planning of shop floor machine processes. This would entail identifying the different operation modes of production machines. The goal for industry is to provide energy monitors for all machines in factories. In addition, where they have been deployed, analysis is limited to aggregating data for subsequent processing later. In this paper, an Autoregressive Hidden Markov Model (ARHMM)-based algorithm is introduced, which can determine the operation mode of the machine in real-time and find direct application in intrusive load monitoring cases. Compared with other load monitoring techniques, such as transient analysis, no prior knowledge of the system to be monitored is required.
Description: The final publication is available at IOS Press through http://dx.doi.org/10.3233/978-1-61499-668-2-193
Sponsor: This work is supported financially by the Engineering and Physical Sciences Research Council (EPSRC) under the project titled “Adaptive Informatics for Intelligent Manufacturing (AI2M)” and the Centre for Doctoral Training in Embedded Intelligence (CDT-EI).
Version: Accepted for publication
DOI: 10.3233/978-1-61499-668-2-193
URI: https://dspace.lboro.ac.uk/2134/23377
Publisher Link: http://dx.doi.org/10.3233/978-1-61499-668-2-193
ISBN: 9781614996675
ISSN: 2352-7528
Appears in Collections:Conference Papers and Contributions (Mechanical, Electrical and Manufacturing Engineering)

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