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dc.contributor.authorTrappey, Charles V.en_US
dc.contributor.authorTrappey, Amy J. C.en_US
dc.contributor.authorMa, Linen_US
dc.contributor.authorTsao, Wan-Tingen_US
dc.date.accessioned2014-12-08T15:35:27Z-
dc.date.available2014-12-08T15:35:27Z-
dc.date.issued2014-03-01en_US
dc.identifier.issn1004-3756en_US
dc.identifier.urihttp://dx.doi.org/10.1007/s11518-014-5227-zen_US
dc.identifier.urihttp://hdl.handle.net/11536/24003-
dc.description.abstractLarge sized power transformers are important parts of the power supply chain. These very critical networks of engineering assets are an essential base of a nation's energy resource infrastructure. This research identifies the key factors influencing transformer normal operating conditions and predicts the asset management lifespan. Engineering asset research has developed few lifespan forecasting methods combining real-time monitoring solutions for transformer maintenance and replacement. Utilizing the rich data source from a remote terminal unit (RTU) system for sensor-data driven analysis, this research develops an innovative real-time lifespan forecasting approach applying logistic regression based on the Weibull distribution. The methodology and the implementation prototype are verified using a data series from 161 kV transformers to evaluate the efficiency and accuracy for energy sector applications. The asset stakeholders and suppliers significantly benefit from the real-time power transformer lifespan evaluation for maintenance and replacement decision support.en_US
dc.language.isoen_USen_US
dc.subjectCondition based maintenance (CBM)en_US
dc.subjectprognostics and health management (PHM)en_US
dc.subjectlogistic regressionen_US
dc.subjectremaining life predictionen_US
dc.subjectsustainable engineering asset managementen_US
dc.titleData driven modeling for power transformer lifespan evaluationen_US
dc.typeArticleen_US
dc.identifier.doi10.1007/s11518-014-5227-zen_US
dc.identifier.journalJOURNAL OF SYSTEMS SCIENCE AND SYSTEMS ENGINEERINGen_US
dc.citation.volume23en_US
dc.citation.issue1en_US
dc.citation.spage80en_US
dc.citation.epage93en_US
dc.contributor.department管理科學系zh_TW
dc.contributor.departmentDepartment of Management Scienceen_US
dc.identifier.wosnumberWOS:000333363000004-
dc.citation.woscount0-
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