Efficient fault diagnosis of ball bearing using ReliefF and Random Forest classifier View Full Text


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Article Info

DATE

2017-01-27

AUTHORS

V. Vakharia, V. K. Gupta, P. K. Kankar

ABSTRACT

The present study focuses on identifying various faults present in ball bearing from the measured vibration signal. Features such as kurtosis, skewness, mean, and root mean square, and complexity measure such as Shannon Entropy are calculated from time domain and Discrete Wavelet Transform. To select the best wavelet function, Maximum Energy to Shannon Entropy ratio criterion is used. Information Gain and ReliefF ranking methods are used to assess the quality of features and features are ranked based on the weight gain obtained from the methods used. Support Vector Machine and Random Forest classifier are selected to identify bearing faults and comparison is made to diagnose faults on the ranked feature set. Experiments are conducted on Case Western Reserve University bearing data sets. Results show that ranking method is useful for identifying best feature set and to improve classification accuracy simultaneously. Cross-validation efficiency of 98.38% is obtained when ReliefF is used with Random Forest. More... »

PAGES

2969-2982

References to SciGraph publications

  • 2015-12-08. Computational intelligence methods for rolling bearing fault detection in JOURNAL OF THE BRAZILIAN SOCIETY OF MECHANICAL SCIENCES AND ENGINEERING
  • 1994. Estimating attributes: Analysis and extensions of RELIEF in MACHINE LEARNING: ECML-94
  • 2001-10. Random Forests in MACHINE LEARNING
  • 2003-10. Theoretical and Empirical Analysis of ReliefF and RReliefF in MACHINE LEARNING
  • 2015-12-09. Application of fuzzy C-means method and classification model of optimized K-nearest neighbor for fault diagnosis of bearing in JOURNAL OF THE BRAZILIAN SOCIETY OF MECHANICAL SCIENCES AND ENGINEERING
  • 2014-12-12. Combined rotor fault diagnosis in rotating machinery using empirical mode decomposition in JOURNAL OF MECHANICAL SCIENCE AND TECHNOLOGY
  • 2008-09. Random forests classifier for machine fault diagnosis in JOURNAL OF MECHANICAL SCIENCE AND TECHNOLOGY
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