Please use this identifier to cite or link to this item: https://doi.org/10.1177/0142331212460883
Title: Classification of energy consumption patterns for energy audit and machine scheduling in industrial manufacturing systems
Authors: Le, C.V.
Pang, C.K. 
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: Jul-2013
Source: Le, C.V., Pang, C.K., Gan, O.P., Chee, X.M., Zhang, D.H., Luo, M., Chan, H.L., Lewis, F.L. (2013-07). Classification of energy consumption patterns for energy audit and machine scheduling in industrial manufacturing systems. Transactions of the Institute of Measurement and Control 35 (5) : 583-592. ScholarBank@NUS Repository. https://doi.org/10.1177/0142331212460883
Abstract: To reduce energy consumption for sustainable and energy-efficient manufacturing, continuous energy monitoring and process tracking of industrial machines are essential. In this paper, we introduce a novel approach to reduce the number of required sensors in process tracking by identifying the operational states based on real-time energy data. Finite-state machines 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 operational states is proposed for energy audit and machine scheduling. 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 filter and a neural network, and our experimental results show a 95.85% accuracy in identification of machine operational states. © The Author(s) 2012.
Source Title: Transactions of the Institute of Measurement and Control
URI: http://scholarbank.nus.edu.sg/handle/10635/55301
ISSN: 01423312
DOI: 10.1177/0142331212460883
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