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Automatic Time Series Segmentation as the Basis for Unsupervised, Non-Intrusive Load Monitoring of Machine Tools

dc.date.accessioned2020-12-09T08:25:47Z
dc.date.available2020-12-09T08:25:47Z
dc.date.issued2019
dc.identifierdoi:10.17170/kobra-202012032328
dc.identifier.urihttp://hdl.handle.net/123456789/12130
dc.language.isoengeng
dc.rightsAttribution-NonCommercial-NoDerivatives 4.0 International*
dc.rights.urihttp://creativecommons.org/licenses/by-nc-nd/4.0/*
dc.subjectenergy managementeng
dc.subjectsustainable manufacturingeng
dc.subjectintelligent sensoreng
dc.subjectload profile analysiseng
dc.subjectoperational state identificationeng
dc.subjectunsupervised learningeng
dc.subject.ddc380
dc.subject.ddc600
dc.titleAutomatic Time Series Segmentation as the Basis for Unsupervised, Non-Intrusive Load Monitoring of Machine Toolseng
dc.typeAufsatz
dcterms.abstractDetailed energy monitoring and benchmarking at the individual component level is necessary to increase energy efficiency in complex production systems. Non-intrusive load monitoring (NILM) provides an economical solution for operational state detection and load disaggregation without the need for large-scale use of fine-grained energy meters. Existing supervised NILM approaches require detailed training data including control information about individual devices. Unsupervised approaches, on the other hand, often require high measurement resolution and are faced with the problem of detecting continuous states. This paper proposes a simple step-by-step, completely unsupervised NILM approach that distinguishes between almost constant and non-constant segments with flexible segment lengths. Taking into account various electrical parameters and their statistical moments, hierarchical density-based spatial clustering of applications with noise (HDBScan) is applied to constant segments. The analysis of non-constant segments is based on agglomerative hierarchical clustering and dynamic time warping. Based on real energy monitoring from a gear manufacturing system we show the applicability of our methodology and discuss how it can be combined with existing NILM techniques.eng
dcterms.accessRightsopen access
dcterms.creatorSeevers, Jan-Peter
dcterms.creatorJohst, J.
dcterms.creatorWeiß, Tim
dcterms.creatorMeschede, Henning
dcterms.creatorHesselbach, Jens
dc.relation.doidoi:10.1016/j.procir.2019.03.178
dc.subject.swdEnergiemanagementger
dc.subject.swdFertigungger
dc.subject.swdNachhaltigkeitger
dc.subject.swdIntelligenter Sensorger
dc.subject.swdLastprofilger
dc.subject.swdBetriebszustandger
dc.subject.swdUnüberwachtes Lernenger
dc.type.versionpublishedVersion
dcterms.source.identifierEISSN 2212-8271
dcterms.source.journalProcedia CIRPeng
dcterms.source.pageinfo695-700
dcterms.source.volumeVolume 81
kup.iskupfalse


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