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Synthetic data for hybrid prognosis
Luleå University of Technology, Department of Civil, Environmental and Natural Resources Engineering, Operation, Maintenance and Acoustics.
Luleå University of Technology, Department of Civil, Environmental and Natural Resources Engineering, Operation, Maintenance and Acoustics.ORCID iD: 0000-0003-4913-6438
Luleå University of Technology, Department of Civil, Environmental and Natural Resources Engineering, Operation, Maintenance and Acoustics.ORCID iD: 0000-0002-4107-0991
2014 (English)In: Proceedings of the European Conference of the Prognostics and Health Management Society 2014, 2014, 796-801 p.Conference paper, Published paper (Refereed)
Abstract [en]

Using condition-based maintenance (CBM) to assess machinery health is a popular technique in many industries, especially those using rotating machines. CBM is relevant in environments where the prediction of a failure and the prevention and mitigation of its consequences increase both profit and safety. Prognosis is the most critical part of this process and the estimation of Remaining Useful Life (RUL) is essential once failure is identified. This paper presents a method of synthetic data generation for hybrid model-based prognosis. In this approach, physical and data-driven models are combined to relate process features to damage accumulation in time-varying service equipment. It uses parametric models and observer-based approaches to Fault Detection and Identification (FDI). A nominal set of parameters is chosen for the simulated system, and a sensitivity analysis is performed using a general-purpose simulation package. Synthetic data sets are then generated to compensate for information missing in the acquired data sets. Information fusion techniques areproposed to merge real and synthetic data to create training data sets which reproduce all identified failure modes, even those that do not occur in the asset, such as Reliability Centered Maintenance (RCM), Failure Mode and Effect Analysis(FMEA). This new technology can lead to better prediction of remaining useful life of rotating machinery and minimizing and mitigating the costly effects of unplanned maintenance actions.

Place, publisher, year, edition, pages
2014. 796-801 p.
National Category
Other Civil Engineering
Research subject
Operation and Maintenance
Identifiers
URN: urn:nbn:se:ltu:diva-29401Local ID: 2df8ba06-abd3-4540-a8ba-97446ef21a6dISBN: 978-1-936263-16-5 (print)OAI: oai:DiVA.org:ltu-29401DiVA: diva2:1002625
Conference
European Conference of the Prognostics and Health Management Society : 08/07/2014 - 10/07/2014
Note

Godkänd; 2014; 20140712 (urklet)

Available from: 2016-09-30 Created: 2016-09-30 Last updated: 2017-11-25Bibliographically approved

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https://www.phmsociety.org/node/1180

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Mishra, MadhavLeturiondo, UrkoGalar, Diego
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