Genetic algorithm based discriminant feature selection for improved fault diagnosis of induction motor

Y.-H. Kim, M.M.M. Islam, R. Islam, J.-M. Kim

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review


In this paper, we present an efficient model for reliable fault diagnosis of the induction motor. This is now a growing demand for high classification accuracy in fault diagnosis. However, the system performance is highly dependable on superior feature analysis. But, it’s still crucial and computational complex to select discernment features, thus, a new genetic algorithm (GA) with optimum class separability criteria is utilized to find most discriminate features from a hybrid feature vector. For this approach, wavelet packet decomposition (WPD) is applied on Acoustic Emission (AE) fault signal and hybrid statistical features are extracted from a decomposed wavelet packet coefficient, which has maximum energy. GA and Euclidean distance based novel, optimum class separability (OCS) are used to select the optimal low-dimensional feature set from high dimensional feature set. The efficacy of this proposed model, in terms of classification accuracy, is validated by the k-nearest neighbor (k-NN) classifier. Experimental results show that the proposed model has a superior classification, yielding an average classification accuracy above 98%. © 2017 CSREA Press. All rights reserved.
Original languageEnglish
Title of host publication ICAI 2017: Proceedings of the 2017 International Conference on Artificial Intelligence
EditorsH.R. Arabnia, D. de la Fuente, E.B. Kozerenko, J.A. Olivas, F.G. Tinetti
PublisherCSREA Press
Number of pages7
ISBN (Electronic)978-160132460-3
Publication statusPublished (in print/issue) - 20 Jul 2017
Event2017 International Conference on Artificial Intelligence - Las Vegas, United States
Duration: 17 Jul 201720 Jul 2017
Conference number: 2017


Conference2017 International Conference on Artificial Intelligence
Abbreviated titleICAI
Country/TerritoryUnited States
CityLas Vegas


  • Acoustic emission (AE) signal
  • Genetic algorithm (GA)
  • Induction motor
  • K-NN
  • Rotor bar
  • Wavelet analysis


Dive into the research topics of 'Genetic algorithm based discriminant feature selection for improved fault diagnosis of induction motor'. Together they form a unique fingerprint.

Cite this