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An application of autoregressive hidden Markov models for identifying machine operations

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conference contribution
posted on 08.12.2016, 09:56 by Dimitrios Pantazis, Adrian Ayastuy Rodriguez, Paul ConwayPaul Conway, Andrew WestAndrew West
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.

Funding

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).

History

School

  • Mechanical, Electrical and Manufacturing Engineering

Published in

ADVANCES IN MANUFACTURING TECHNOLOGY XXX

Volume

3

Pages

193 - 198

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.

Publisher

© The Authors. Published by IOS Press

Version

AM (Accepted Manuscript)

Publisher statement

This work is made available according to the conditions of the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International (CC BY-NC-ND 4.0) licence. Full details of this licence are available at: https://creativecommons.org/licenses/by-nc-nd/4.0/

Publication date

2016

Notes

The final publication is available at IOS Press through http://dx.doi.org/10.3233/978-1-61499-668-2-193

ISBN

9781614996675

ISSN

2352-7528

Book series

Advances in Transdisciplinary Engineering;3

Language

en