Bushing monitoring using MLP and RBF

Sizwe M. Dhlamini, Tshilidzi Marwala

Research output: Contribution to conferencePaperpeer-review

10 Citations (Scopus)


This paper examines the use of artificial neural networks (ANN) for monitoring bushings. The first ANN uses 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.

Original languageEnglish
Number of pages6
Publication statusPublished - 2004
Event2004 IEEE AFRICON: 7th AFRICON Conference in Africa: Technology Innovation - Gaborone, Botswana
Duration: 15 Sept 200417 Sept 2004


Conference2004 IEEE AFRICON: 7th AFRICON Conference in Africa: Technology Innovation


  • Bushing
  • Diagnosis
  • Dissolved gas analysis
  • Multi layer perceptron
  • Radial basis

ASJC Scopus subject areas

  • Computer Science Applications
  • Electrical and Electronic Engineering


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