Please use this identifier to cite or link to this item: http://hdl.handle.net/10397/89671
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dc.contributorDepartment of Industrial and Systems Engineeringen_US
dc.contributorInterdisciplinary Division of Aeronautical and Aviation Engineeringen_US
dc.creatorLee, CKMen_US
dc.creatorCao, Yen_US
dc.creatorNg, KHen_US
dc.date.accessioned2021-04-28T02:29:05Z-
dc.date.available2021-04-28T02:29:05Z-
dc.identifier.isbn9781522509561(ISBN13)en_US
dc.identifier.isbn1522509569(ISBN10)en_US
dc.identifier.isbn9781522509578(EISBN13)en_US
dc.identifier.urihttp://hdl.handle.net/10397/89671-
dc.language.isoenen_US
dc.rightsPosted with permission of the publisher and author.en_US
dc.rightsThe following book chapter Lee, C. K., Cao, Y., & Ng, K. H. (2017). Big Data Analytics for Predictive Maintenance Strategies. In H. Chan, N. Subramanian, & M. Abdulrahman (Ed.), Supply Chain Management in the Big Data Era (pp. 50-74). IGI Global is available from http://doi:10.4018/978-1-5225-0956-1.ch004en_US
dc.titleBig data analytics for predictive maintenance strategiesen_US
dc.typeBook Chapteren_US
dc.identifier.spage50en_US
dc.identifier.epage74en_US
dc.identifier.doi10.4018/978-1-5225-0956-1.ch004en_US
dcterms.abstractMaintenance aims to reduce and eliminate the number of failures occurred during production as any breakdown of machine or equipment may lead to disruption for the supply chain. Maintenance policy is set to provide the guidance for selecting the most cost-effective maintenance approach and system to achieve operational safety. For example, predictive maintenance is most recommended for crucial components whose failure will cause severe function loss and safety risk. Recent utilization of big data and related techniques in predictive maintenance greatly improves the transparency for system health condition and boosts the speed and accuracy in the maintenance decision making. In this chapter, a Maintenance Policies Management framework under Big Data Platform is designed and the process of maintenance decision support system is simulated for a sensor-monitored semiconductor manufacturing plant. Artificial Intelligence is applied to classify the likely failure patterns and estimate the machine condition for the faulty component.en_US
dcterms.accessRightsopen accessen_US
dcterms.bibliographicCitationIn HK Chan (Ed.), Nachiappan Subramanian, Muhammad Dan-Asabe Abdulrahman, Supply chain management in the big data era, chapter 4, p. 50-74. Hershey, PA: IGI Global, 2017en_US
dcterms.issued2017-
dc.relation.ispartofbookSupply chain management in the big data eraen_US
dc.publisher.placeHershey, PAen_US
dc.description.validate202104 bcvcen_US
dc.description.oaAccepted Manuscripten_US
dc.identifier.FolderNumbera0759-n01-
dc.identifier.SubFormID1460-
dc.description.fundingSourceOthersen_US
dc.description.fundingTextRUE9, RU8Hen_US
dc.description.pubStatusPublisheden_US
dc.description.oaCategoryPublisher permissionen_US
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