@inproceedings{e9a66bbc8b304eaeb6358181809c2b11,
title = "Thermal lifetime evaluation of electrical machines using neural network",
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.",
keywords = "Aging time, Neural network, accelerated lifetime test, and Insulation Resistance., thermal life of insulation",
author = "G. Turabee and Khowja, {M. Raza} and V. Madonna and P. Giangrande and G. Vakil and C. Gerada and M. Galea",
note = "Publisher Copyright: {\textcopyright} 2020 IEEE.; 2020 IEEE Transportation Electrification Conference and Expo, ITEC 2020 ; Conference date: 23-06-2020 Through 26-06-2020",
year = "2020",
month = jun,
doi = "10.1109/ITEC48692.2020.9161662",
language = "English",
series = "2020 IEEE Transportation Electrification Conference and Expo, ITEC 2020",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
pages = "1153--1158",
booktitle = "2020 IEEE Transportation Electrification Conference and Expo, ITEC 2020",
address = "United States",
}