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dc.contributor.authorHeim, Sam
dc.contributor.authorClemens, Jason R.
dc.contributor.authorSteck, James E.
dc.contributor.authorBasic, Christopher
dc.contributor.authorTimmons, David
dc.contributor.authorZwiener, Kourtney
dc.date.accessioned2021-06-01T03:13:05Z
dc.date.available2021-06-01T03:13:05Z
dc.date.issued2021-03-19
dc.identifier.citationHeim, S., Clemens, J., Steck, J. E., Basic, C., Timmons, D., & Zwiener, K. (2020). Predictive maintenance on aircraft and applications with digital twin. Paper presented at the Proceedings - 2020 IEEE International Conference on Big Data, Big Data 2020, 4122-4127. doi:10.1109/BigData50022.2020.9378433en_US
dc.identifier.isbn978-1-7281-6251-5
dc.identifier.isbn978-1-7281-6252-2
dc.identifier.urihttps://doi.org/10.1109/BigData50022.2020.9378433
dc.identifier.urihttps://soar.wichita.edu/handle/10057/20064
dc.descriptionClick on the URL link to access this conference paper on the publisher’s website (may not be free.)en_US
dc.description.abstractThe concept of predictive maintenance in aiding the manufacturing and operation of equipment is moving several industries forward, allowing for a more detailed description and justification of maintenance routines. This paper provides several methods of describing the remaining useful life of given parts for aircraft maintenance through two separate aircraft data sets. It is then shown how these results, in conjunction with a digital twin model, can be used to aid in designing a sufficiently stable supply chain and maintenance strategy.en_US
dc.language.isoen_USen_US
dc.publisherIEEEen_US
dc.relation.ispartofseries2020 IEEE International Conference on Big Data (Big Data);
dc.subjectAircraften_US
dc.subjectBig dataen_US
dc.subjectDigital twinen_US
dc.subjectSupply chainsen_US
dc.titlePredictive maintenance on aircraft and applications with digital twinen_US
dc.typeConference paperen_US
dc.rights.holder©2020 IEEEen_US


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    Research publications authored by the Department of Aerospace Engineering faculty and graduate students.

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