Lifetime estimation of enameled wires under accelerated thermal aging using curve fitting methods
journal contribution
posted on 2021-03-29, 10:46 authored by Muhammad Raza Khowja, Gulrukh Turabee, Paolo Giangrande, Vincenzo Madonna, Georgina CosmaGeorgina Cosma, Gaurang Vakil, Chris Gerada, Michael Galea© 2013 IEEE. Estimating the lifetime of enameled wires using the conventional/standard test method requires a significant amount of time that can endure up to thousands of testing hours, which could considerably delay the time-To-market of a new product. This paper presents a new approach that estimates the insulation lifetime of enameled wire, employed in electrical machines, using curve fitting models whose computation is rapid and accurate. Three curve fit models are adopted to predict the insulation resistance of double-coated enameled magnet wire samples, with respect to their aging time. The samples' mean time-To-failure is estimated, and performance of the models is apprised through a comparison against the conventional 'standard method' of lifetime estimation of the enameled wires. The best prediction accuracy is achieved by a logarithmic curve fit approach, which gives an error of 0.95% and 1.62% when its thermal index is compared with the conventional method and manufacturer claim respectively. The proposed approach provides a time-saving of 67% (83 days) when its computation time is compared with respect to the 'standard method' of lifetime estimation.
Funding
Clean Sky 2 Joint Undertaking through the European Union’s Horizon 2020 Research and Innovation Program under Grant 807081 and Grant 821023
History
School
- Science
Department
- Computer Science
Published in
IEEE AccessVolume
9Pages
18993 - 19003Publisher
Institute of Electrical and Electronics Engineers (IEEE)Version
- VoR (Version of Record)
Rights holder
© The authorsPublisher statement
This is an Open Access Article. It is published by IEEE under the Creative Commons Attribution 4.0 Unported Licence (CC BY). Full details of this licence are available at: http://creativecommons.org/licenses/by/4.0/Acceptance date
2021-01-12Publication date
2021-01-18Copyright date
2021ISSN
2169-3536eISSN
2169-3536Publisher version
Language
- en
Depositor
Dr Georgina Cosma. Deposit date: 24 March 2021Usage metrics
Categories
No categories selectedKeywords
InsulationWiresCurve fittingArtificial neural networksStandardsThermal stressesStressNeural networkcurve fittinginsulation lifetimethermal agingaccelerated aging testinsulation resistance and dissipation factorScience & TechnologyTechnologyComputer Science, Information SystemsEngineering, Electrical & ElectronicTelecommunicationsComputer ScienceEngineeringInformation and Computing Sciences
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