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Moosavian, A., Ahmadi, H., Tabatabaeefar, A. and Khazaee, M. (2013) Comparison of Two Classifiers; K-Nearest Neighbor and Artificial Neural Network, for Fault Diagnosis on a Main Engine Journal-Bearing. Shock and Vibration, 20, 263-272.
https://doi.org/10.1155/2013/360236

has been cited by the following article:

• TITLE: An Evolving Fuzzy Classifier for Induction Motor Health Condition Monitoring

  AUTHORS: Peter Luong, Wilson Wang

  KEYWORDS: Evolving Fuzzy Classifier, Clustering, Automatic Fault Diagnostics, Induction Motors

  JOURNAL NAME: Intelligent Control and Automation, Vol.10 No.4, October 17, 2019

  ABSTRACT: Induction motor (IM) is commonly used in various industrial applications. Reliable online IM health condition monitoring systems are critically needed in industries to improve operational accuracy and safety of the IMs and the machinery. A new evolving algorithm is proposed to provide more decision-making transparency, as well as better classification and processing efficiency. The effectiveness of the developed intelligent classifier is examined by simulation and experimental tests.