Incremental Learning and Its Application to Bushing Condition Monitoring

  • Authors:

  • Christina B. Vilakazi;Tshilidzi Marwala

  • Affiliations:

  • School of Electrical and Information Engineering, University of the Witwatersrand, Private Bag 3, Wits, 2050, Johannesburg, South Africa;School of Electrical and Information Engineering, University of the Witwatersrand, Private Bag 3, Wits, 2050, Johannesburg, South Africa

  • Venue:
  • ISNN '07 Proceedings of the 4th international symposium on Neural Networks: Advances in Neural Networks
  • Year:
  • 2007

Quantified Score

Hi-index 0.00

Visualization

Abstract

The problem of fault diagnosis of electrical machine has been an ongoing research in power systems. Many machine learning tools have been applied to this problem using static machine learning structures such as neural network, support vector machine that are unable to accommodate new information as it becomes available into their existing models. This paper presents a new method to bushing fault condition monitoring using fuzzy ARTMAP(FAM). FAM is introduced for bushing condition monitoring because it has the ability to incrementally learn information as it becomes available. An ensemble of classifiers is used to improve the classification accuracy of the systems. The testing results show that FAM ensemble gave an accuracy of 98.5%. Furthermore, the results show that fuzzy ARTMAP can update its knowledge in an incremental fashion without forgetting previously learned information.