@inproceedings{273a0f67c9424cb7bc0289b523924295,
title = "An extension neural network and genetic algorithm for bearing fault classification",
abstract = "A Genetic Algorithm enhanced Extension Neural Network (GA-ENN) is presented which improves on the traditional ENN by including the automatic determination of the learning rate. The GA allows the best network that produces the lowest classification error to be obtained. The effectiveness of this new system is proven using the Iris dataset. The system is then applied to the problem of bearing condition monitoring, where vibration data from bearings are analysed, diagnosed as faulty or not and their severity classified. This system is found to be 100% accurate in detecting bearing faults with an accuracy of 95% in diagnosing the severity of the fault.",
author = "Shakir Mohamed and Thando Tettey and Tshilidzi Marwala",
year = "2006",
doi = "10.1109/ijcnn.2006.246914",
language = "English",
isbn = "0780394909",
series = "IEEE International Conference on Neural Networks - Conference Proceedings",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
pages = "3942--3948",
booktitle = "International Joint Conference on Neural Networks 2006, IJCNN '06",
address = "United States",
note = "International Joint Conference on Neural Networks 2006, IJCNN '06 ; Conference date: 16-07-2006 Through 21-07-2006",
}