Thermal lifetime evaluation of electrical machines using neural network

G. Turabee, M. Raza Khowja, V. Madonna, P. Giangrande, G. Vakil, C. Gerada, M. Galea

Research output: Chapter in Book/Conference proceedingConference contributionpeer-review

2 Citations (Scopus)

Abstract

This paper proposes a surrogate approach which utilises an supervised neural network to significantly shorten the time required for thermal qualification of electrical machines' insulation. The proposed approach is based on a feedforward neural network trained with Bayesian Regularization Back-Propagation (BRP) algorithm. The network predicts the winding's insulation resistance trend with respect to its thermal aging time. The predicted insulation resistance is evaluated against experimental measurements and an excellent match is found. Its trend is used for estimating the sample's time to failure under thermal stress at various temperatures. The temperature index of the insulating material, predicted by the neural network, matches with an error of just 0.4% margin against the experimental findings.

Original languageEnglish
Title of host publication2020 IEEE Transportation Electrification Conference and Expo, ITEC 2020
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages1153-1158
Number of pages6
ISBN (Electronic)9781728146294
DOIs
Publication statusPublished - Jun 2020
Event2020 IEEE Transportation Electrification Conference and Expo, ITEC 2020 - Chicago, United States
Duration: 23 Jun 202026 Jun 2020

Publication series

Name2020 IEEE Transportation Electrification Conference and Expo, ITEC 2020

Conference

Conference2020 IEEE Transportation Electrification Conference and Expo, ITEC 2020
Country/TerritoryUnited States
CityChicago
Period23/06/2026/06/20

Keywords

  • Aging time
  • Neural network
  • accelerated lifetime test
  • and Insulation Resistance.
  • thermal life of insulation

ASJC Scopus subject areas

  • Energy Engineering and Power Technology
  • Automotive Engineering
  • Transportation

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