Abstract
This paper examines the use of artificial neural networks (ANN) for monitoring bushings. The first ANN uses a multiplayer perceptron (MLP) while the second uses radial basis activation functions (RBF). In this approach, a decision can be taken to remove or leave a bushing in service, based on analysis of bushing parameters using RBF and MLP. The results show that the RBF converges to a solution faster than the MLP. Furthermore, the MLP is found to be the best tool of the two for analyzing large amounts of non-parametric non-linear data
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