@inproceedings{541dfc8f88a04930b9ede4e7cb9b7c93,
title = "Failure Prediction of Metal Oxide Arresters using Artificial Neural Networks",
abstract = "This paper investigates the suitability of Artificial Neural Networks (ANN) technique to test the correlation between thermal stress and growth in varistor grains, in order to predict failure of metal oxide (MO) arresters. Input data to the ANN model consisted of 60 points of thermal stress intensity to which low-voltage varistor samples were continuously exposed to, for a constant time-period and voltage stress. Varistor grain response obtained experimentally (targets) were compared to the ANN model output. Results show a strong relationship between the two outputs. The MSE value predicted is as low as 1.157e{\texttrademark}27 and the error histogram shows balanced error for the trained data.",
keywords = "artificial neural networks, electro-thermal thermal stress, metal oxide varistor, microstructure, varistor grain size",
author = "Lutendo Muremi and Pitshou Bokoro",
note = "Publisher Copyright: {\textcopyright} 2020 IEEE.; 2020 IEEE Electrical Insulation Conference, EIC 2020 ; Conference date: 22-06-2020 Through 03-07-2020",
year = "2020",
month = jun,
doi = "10.1109/EIC47619.2020.9158694",
language = "English",
series = "2020 IEEE Electrical Insulation Conference, EIC 2020",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
pages = "58--61",
booktitle = "2020 IEEE Electrical Insulation Conference, EIC 2020",
address = "United States",
}