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Please use this identifier to cite or link to this item: https://dspace.iiti.ac.in/handle/123456789/6779
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dc.contributor.authorBakliwal, Kshitijen_US
dc.contributor.authorDhada, Maharshi Harshadbhaien_US
dc.date.accessioned2022-03-17T01:00:00Z-
dc.date.accessioned2022-03-21T10:51:19Z-
dc.date.available2022-03-17T01:00:00Z-
dc.date.available2022-03-21T10:51:19Z-
dc.date.issued2018-
dc.identifier.citationPalau, A. S., Bakliwal, K., Dhada, M. H., Pearce, T., & Parlikad, A. K. (2018). Recurrent neural networks for real-time distributed collaborative prognostics. Paper presented at the 2018 IEEE International Conference on Prognostics and Health Management, ICPHM 2018, doi:10.1109/ICPHM.2018.8448622en_US
dc.identifier.isbn9781538611647-
dc.identifier.otherEID(2-s2.0-85062827229)-
dc.identifier.urihttps://doi.org/10.1109/ICPHM.2018.8448622-
dc.identifier.urihttps://dspace.iiti.ac.in/handle/123456789/6779-
dc.description.abstractWe present the first steps towards real-time distributed collaborative prognostics enabled by an implementation of the Weibull Time To Event - Recurrent Neural Network (WTTE-RNN) algorithm. In our system, assets determine their time to failure (TTF) in real-time according to an asset-specific model that is obtained in collaboration with other similar assets in the asset fleet. The presented approach builds on the emergent field of similarity analysis in asset management, and extends it to distributed collaborative prognostics. We show how through collaboration between assets and distributed prognostics, competitive time to failure estimates can be obtained. © 2018 IEEE.en_US
dc.language.isoenen_US
dc.publisherInstitute of Electrical and Electronics Engineers Inc.en_US
dc.source2018 IEEE International Conference on Prognostics and Health Management, ICPHM 2018en_US
dc.subjectSystems engineeringen_US
dc.subjectReal timeen_US
dc.subjectSimilarity analysisen_US
dc.subjectTime to eventsen_US
dc.subjectTime to failureen_US
dc.subjectWeibullen_US
dc.subjectRecurrent neural networksen_US
dc.titleRecurrent Neural Networks for real-time distributed collaborative prognosticsen_US
dc.typeConference Paperen_US
dc.rights.licenseAll Open Access, Green-
Appears in Collections:Department of Mechanical Engineering

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