Multistep prediction of dynamic uncertainty under limited data

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dc.contributor.authorGrenyer, Alex
dc.contributor.authorSchwabe, Oliver
dc.contributor.authorErkoyuncu, John Ahmet
dc.contributor.authorZhao, Yifan
dc.date.accessioned2022-03-03T11:33:07Z
dc.date.available2022-03-03T11:33:07Z
dc.date.issued2022-01-12
dc.description.abstractEngineering systems are growing in complexity, requiring increasingly intelligent and flexible methods to account for and predict uncertainties in service. This paper presents a framework for dynamic uncertainty prediction under limited data (UPLD). Spatial geometry is incorporated with LSTM networks to enable real-time multistep prediction of quantitative and qualitative uncertainty over time. Validation is achieved through two case studies. Results demonstrate robust prediction of trends in limited and dynamic uncertainty data with parallel determination of geometric symmetry at each time unit. Future work is recommended to explore alternative network architectures suited to limited data scenarios.en_UK
dc.description.sponsorshipEngineering and Physical Sciences Research Council (EPSRC): 1944319en_UK
dc.identifier.citationGrenyer A, Schwabe O, Erkoyuncu JA, Zhao Y. (2022) Multistep prediction of dynamic uncertainty under limited data, CIRP Journal of Manufacturing Science and Technology, Volume 37, May 2022, pp. 37-54en_UK
dc.identifier.eissn1878-0016
dc.identifier.issn1755-5817
dc.identifier.urihttps://doi.org/10.1016/j.cirpj.2022.01.002
dc.identifier.urihttps://dspace.lib.cranfield.ac.uk/handle/1826/17622
dc.language.isoenen_UK
dc.publisherElsevieren_UK
dc.rightsAttribution 4.0 International*
dc.rights.urihttp://creativecommons.org/licenses/by/4.0/*
dc.subjectForecasten_UK
dc.subjectLimited dataen_UK
dc.subjectLong-short term memory (LSTM)en_UK
dc.subjectMultistepen_UK
dc.subjectPredictionen_UK
dc.subjectSpatial geometryen_UK
dc.subjectUncertaintyen_UK
dc.titleMultistep prediction of dynamic uncertainty under limited dataen_UK
dc.typeArticleen_UK

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