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Please use this identifier to cite or link to this item: https://doi.org/10.21256/zhaw-20359
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dc.contributor.authorLehmann, Claude-
dc.contributor.authorGoren Huber, Lilach-
dc.contributor.authorHorisberger, Thomas-
dc.contributor.authorScheiba, Georg-
dc.contributor.authorSima, Ana-Claudia-
dc.contributor.authorStockinger, Kurt-
dc.date.accessioned2020-08-17T08:39:33Z-
dc.date.available2020-08-17T08:39:33Z-
dc.date.issued2020-08-
dc.identifier.issn2196-1115de_CH
dc.identifier.urihttps://digitalcollection.zhaw.ch/handle/11475/20359-
dc.description.abstractExploiting available condition monitoring data of industrial machines for intelligent maintenance purposes has been attracting attention in various application fields. Machine learning algorithms for fault detection, diagnosis and prognosis are popular and easily accessible. However, our experience in working at the intersection of academia and industry showed that the major challenges of building an end-to-end system in a real-world industrial setting go beyond the design of machine learning algorithms. One of the major challenges is the design of an end-to-end data management solution that is able to efficiently store and process large amounts of heterogeneous data streams resulting from a variety of physical machines. In this paper we present the design of an end-to-end Big Data architecture that enables intelligent maintenance in a real-world industrial setting. In particular, we will discuss various physical design choices for optimizing high-dimensional queries, such as partitioning and Z-ordering, that serve as the basis for health analytics. Finally, we describe a concrete fault detection use case with two different health monitoring algorithms based on machine learning and classical statistics and discuss their advantages and disadvantages. The paper covers some of the most important aspects of the practical implementation of such an end-to-end solution and demonstrates the challenges and their mitigation for the specific application of laser cutting machines.de_CH
dc.language.isoende_CH
dc.publisherSpringerde_CH
dc.relation.ispartofJournal of Big Datade_CH
dc.rightshttp://creativecommons.org/licenses/by/4.0/de_CH
dc.subjectBig Data architecturede_CH
dc.subjectQuery processingde_CH
dc.subjectMachine learningde_CH
dc.subjectHeterogeneous data integrationde_CH
dc.subjectStream processingde_CH
dc.subjectIntelligent maintenancede_CH
dc.subjectPrognosticsde_CH
dc.subjectHealth managementde_CH
dc.subject.ddc006: Spezielle Computerverfahrende_CH
dc.titleBig data architecture for intelligent maintenance : a focus on query processing and machine learning algorithmsde_CH
dc.typeBeitrag in wissenschaftlicher Zeitschriftde_CH
dcterms.typeTextde_CH
zhaw.departementSchool of Engineeringde_CH
zhaw.organisationalunitInstitut für Informatik (InIT)de_CH
dc.identifier.doi10.1186/s40537-020-00340-7de_CH
dc.identifier.doi10.21256/zhaw-20359-
zhaw.funding.euNode_CH
zhaw.issue1de_CH
zhaw.originated.zhawYesde_CH
zhaw.publication.statuspublishedVersionde_CH
zhaw.volume7de_CH
zhaw.publication.reviewPeer review (Publikation)de_CH
zhaw.webfeedDatalabde_CH
zhaw.webfeedInformation Engineeringde_CH
zhaw.webfeedZHAW digitalde_CH
zhaw.funding.zhawDecision Support System For Predictive Maintenance of Laser Cutting Machinesde_CH
zhaw.author.additionalNode_CH
zhaw.display.portraitYesde_CH
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Lehmann, C., Goren Huber, L., Horisberger, T., Scheiba, G., Sima, A.-C., & Stockinger, K. (2020). Big data architecture for intelligent maintenance : a focus on query processing and machine learning algorithms. Journal of Big Data, 7(1). https://doi.org/10.1186/s40537-020-00340-7
Lehmann, C. et al. (2020) ‘Big data architecture for intelligent maintenance : a focus on query processing and machine learning algorithms’, Journal of Big Data, 7(1). Available at: https://doi.org/10.1186/s40537-020-00340-7.
C. Lehmann, L. Goren Huber, T. Horisberger, G. Scheiba, A.-C. Sima, and K. Stockinger, “Big data architecture for intelligent maintenance : a focus on query processing and machine learning algorithms,” Journal of Big Data, vol. 7, no. 1, Aug. 2020, doi: 10.1186/s40537-020-00340-7.
LEHMANN, Claude, Lilach GOREN HUBER, Thomas HORISBERGER, Georg SCHEIBA, Ana-Claudia SIMA und Kurt STOCKINGER, 2020. Big data architecture for intelligent maintenance : a focus on query processing and machine learning algorithms. Journal of Big Data. August 2020. Bd. 7, Nr. 1. DOI 10.1186/s40537-020-00340-7
Lehmann, Claude, Lilach Goren Huber, Thomas Horisberger, Georg Scheiba, Ana-Claudia Sima, and Kurt Stockinger. 2020. “Big Data Architecture for Intelligent Maintenance : A Focus on Query Processing and Machine Learning Algorithms.” Journal of Big Data 7 (1). https://doi.org/10.1186/s40537-020-00340-7.
Lehmann, Claude, et al. “Big Data Architecture for Intelligent Maintenance : A Focus on Query Processing and Machine Learning Algorithms.” Journal of Big Data, vol. 7, no. 1, Aug. 2020, https://doi.org/10.1186/s40537-020-00340-7.


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