Please use this identifier to cite or link to this item: https://doi.org/10.1177/0142331212460883
DC FieldValue
dc.titleClassification of energy consumption patterns for energy audit and machine scheduling in industrial manufacturing systems
dc.contributor.authorLe, C.V.
dc.contributor.authorPang, C.K.
dc.contributor.authorGan, O.P.
dc.contributor.authorChee, X.M.
dc.contributor.authorZhang, D.H.
dc.contributor.authorLuo, M.
dc.contributor.authorChan, H.L.
dc.contributor.authorLewis, F.L.
dc.date.accessioned2014-06-17T02:41:30Z
dc.date.available2014-06-17T02:41:30Z
dc.date.issued2013-07
dc.identifier.citationLe, 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
dc.identifier.issn01423312
dc.identifier.urihttp://scholarbank.nus.edu.sg/handle/10635/55301
dc.description.abstractTo 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.
dc.description.urihttp://libproxy1.nus.edu.sg/login?url=http://dx.doi.org/10.1177/0142331212460883
dc.sourceScopus
dc.subjectClassification
dc.subjectenergy monitoring
dc.subjectfinite-state machine (FSM)
dc.subjectneural network (NN)
dc.subjectSavizky-Golay (SG) filter
dc.typeArticle
dc.contributor.departmentELECTRICAL & COMPUTER ENGINEERING
dc.description.doi10.1177/0142331212460883
dc.description.sourcetitleTransactions of the Institute of Measurement and Control
dc.description.volume35
dc.description.issue5
dc.description.page583-592
dc.description.codenTICOD
dc.identifier.isiut000320845000003
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