Lifetime Estimation of Enameled Wires under Accelerated Thermal Aging Using Curve Fitting Methods

Muhammad Raza Khowja, Gulrukh Turabee, Paolo Giangrande, Vincenzo Madonna, Georgina Cosma, Gaurang Vakil, Chris Gerada, Michael Galea

Research output: Journal PublicationArticlepeer-review

2 Citations (Scopus)

Abstract

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.

Original languageEnglish
Article number9326307
Pages (from-to)18993-19003
Number of pages11
JournalIEEE Access
Volume9
DOIs
Publication statusPublished - 2021

Keywords

  • Neural network
  • accelerated aging test
  • curve fitting
  • insulation lifetime
  • insulation resistance and dissipation factor
  • thermal aging

ASJC Scopus subject areas

  • Computer Science (all)
  • Materials Science (all)
  • Engineering (all)

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