@inproceedings{5a32821826f648988126cd08b0bff7cd,
title = "Diagnosis of shorted-turns faults in electrical machine using Neural Network",
abstract = "This paper discusses the diagnosis of shorted-turn faults in the electrical machine using Neural Networks (NN). This leads to a design process of a work-flow for the NN. The work-flow has three stages: data acquisition, training algorithm and diagnosis and detection of machine condition. Samples data of electrical machine in healthy and shorted-turn fault conditions were collected by interfacing data acquisition device with a computer laboratory. A two-layer feed-forward network with back-propagation algorithm is created and configured with data collected for NN training. The network model gives a high correlation coefficient of R = 0.9992, R = 0.99917 and R = 0.99923 in the training, validation and test phase respectively as well as the overall correlation which is R = 0.9992. This connotes that the NN model gives a high correlation coefficient between predicted outputs (NN) and targets (Fault Index (FI)). Using the NN model, the healthy and shorted-turn electrical machine are predicted correctly and this is compared with the diagnosis done using FI. Thus, with an NN, a robust and reliable method to diagnose shorted-turn fault in the electrical machine can be achieved.",
keywords = "Electrical machine, Fault diagnosis, Fault index (FI), Neural network(NN), Shorted-turn",
author = "O. dunAyo and Nelwamondo, {Fulufhelo V.} and Jimoh, {Adisa A.} and Tshilidzi Marwala",
note = "Publisher Copyright: {\textcopyright} 2018 Newswood Limited.; 2018 World Congress on Engineering and Computer Science, WCECS 2018 ; Conference date: 23-10-2018 Through 25-10-2018",
year = "2018",
language = "English",
series = "Lecture Notes in Engineering and Computer Science",
publisher = "Newswood Limited",
pages = "303--307",
editor = "W.S. Grundfest and Craig Douglas and S.I. Ao",
booktitle = "Proceedings of the World Congress on Engineering and Computer Science 2018, WCECS 2018",
}