Simulation-Based Transfer Learning for Support Stiffness Identification

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dc.contributorAalto-yliopistofi
dc.contributorAalto Universityen
dc.contributor.authorBobylev, Denisen_US
dc.contributor.authorChoudhury, Tuhinen_US
dc.contributor.authorMiettinen, Jesseen_US
dc.contributor.authorViitala, Ristoen_US
dc.contributor.authorKurvinen, Emilen_US
dc.contributor.authorSopanen, Jussien_US
dc.contributor.departmentDepartment of Mechanical Engineeringen
dc.contributor.groupauthorMechatronicsen
dc.contributor.organizationLUT Universityen_US
dc.date.accessioned2021-09-15T06:41:49Z
dc.date.available2021-09-15T06:41:49Z
dc.date.issued2021-09-08en_US
dc.descriptionPublisher Copyright: Author
dc.description.abstractThe support structures of a rotating machine affect its overall dynamic behavior. The stiffness of the support structures often differs at the actual sites compared to the test rigs, which leads to uncertain dynamics. In this research, a novel method is developed to identify the support stiffness for an in-situ machine using a simulation-data-driven, deep learning algorithm. In this transfer learning approach, a deep learning algorithm is trained with a simulation model and then tested with measured vibration of a rotor-bearing-support system. To validate the algorithm for multiple cases, an experimental test rig with variable horizontal support stiffness is used. The results from the deep learning algorithm are compared with Linear regression (LR), Artificial Neural Network (ANN), and Support vector regression (SVR) for benchmarking. The models are trained with filtered frequency domain response, and challenges due to measurement uncertainty are analyzed. With the proposed pre-processing steps of the frequency domain response and outlier elimination, the deep learning-based virtual sensor can predict the support stiffness with reasonable accuracy, where the limiting factor is the data quality and lack of excitation at critical speed frequencies.en
dc.description.versionPeer revieweden
dc.format.extent13
dc.format.mimetypeapplication/pdfen_US
dc.identifier.citationBobylev, D, Choudhury, T, Miettinen, J, Viitala, R, Kurvinen, E & Sopanen, J 2021, ' Simulation-Based Transfer Learning for Support Stiffness Identification ', IEEE Access, vol. 9, 9524624, pp. 120652-120664 . https://doi.org/10.1109/ACCESS.2021.3108414en
dc.identifier.doi10.1109/ACCESS.2021.3108414en_US
dc.identifier.issn2169-3536
dc.identifier.otherPURE UUID: e8a727d7-00e4-4cf5-9f12-d8fb21c2691fen_US
dc.identifier.otherPURE ITEMURL: https://research.aalto.fi/en/publications/e8a727d7-00e4-4cf5-9f12-d8fb21c2691fen_US
dc.identifier.otherPURE LINK: http://www.scopus.com/inward/record.url?scp=85113866148&partnerID=8YFLogxKen_US
dc.identifier.otherPURE FILEURL: https://research.aalto.fi/files/67401901/ENG_Bobylev_et_al_Simulation_Based_Transfer_Learning_IEEE_Access.pdfen_US
dc.identifier.urihttps://aaltodoc.aalto.fi/handle/123456789/109969
dc.identifier.urnURN:NBN:fi:aalto-202109159192
dc.language.isoenen
dc.publisherIEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
dc.relation.ispartofseriesIEEE Accessen
dc.rightsopenAccessen
dc.subject.keywordDeep Learningen_US
dc.subject.keywordMachine Learningen_US
dc.subject.keywordParameter estimationen_US
dc.subject.keywordPhysics-based simulationen_US
dc.subject.keywordSupport Stiffnessen_US
dc.subject.keywordTransfer Learningen_US
dc.titleSimulation-Based Transfer Learning for Support Stiffness Identificationen
dc.typeA1 Alkuperäisartikkeli tieteellisessä aikakauslehdessäfi
dc.type.versionpublishedVersion
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