Please use this identifier to cite or link to this item: https://doi.org/10.1109/ICCIS.2011.6070317
Title: Intelligent energy audit and machine management for energy-efficient manufacturing
Authors: Pang, C.K. 
Le, C.V.
Gan, O.P.
Chee, X.M.
Zhang, D.H.
Luo, M.
Chan, H.L.
Lewis, F.L.
Keywords: Classification
energy monitoring
Finite-State Machine (FSM)
Neural Network (NN)
Savizky-Golay (SG) filter
Issue Date: 2011
Source: Pang, C.K.,Le, C.V.,Gan, O.P.,Chee, X.M.,Zhang, D.H.,Luo, M.,Chan, H.L.,Lewis, F.L. (2011). Intelligent energy audit and machine management for energy-efficient manufacturing. Proceedings of the 2011 IEEE 5th International Conference on Cybernetics and Intelligent Systems, CIS 2011 : 142-147. ScholarBank@NUS Repository. https://doi.org/10.1109/ICCIS.2011.6070317
Abstract: To reduce energy consumption for sustainable and energy-efficient manufacturing, a good understanding of the dynamic energy consumption patterns on the manufacturing shop floor is essential. In this paper, we introduce a novel approach to address the challenge of missing operation context information during in-situ energy data measurement. Finite-State Machines (FSMs) are used to model the engineering processes, and a two-stage framework for online classification of real time energy measurement data in terms of machine operation states is proposed for energy audit and machine management. The first stage uses advanced signal processing techniques to reduce noise while preserving important features, and the second stage uses intelligent pattern recognition algorithms to cluster energy consumption patterns. Our proposed two-stage framework is evaluated on an industrial injection moulding system using a Savizky-Golay (SG) filter and a Neural Network (NN), and our experimental results show a 95.85% accuracy in identification of machine operation states. © 2011 IEEE.
Source Title: Proceedings of the 2011 IEEE 5th International Conference on Cybernetics and Intelligent Systems, CIS 2011
URI: http://scholarbank.nus.edu.sg/handle/10635/70637
ISBN: 9781612841984
DOI: 10.1109/ICCIS.2011.6070317
Appears in Collections:Staff Publications

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