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Random forests classifier for machine fault diagnosis

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Abstract

This paper investigates the possibilities of applying the random forests algorithm (RF) in machine fault diagnosis, and proposes a hybrid method combined with genetic algorithm to improve the classification accuracy. The proposed method is based on RF, a novel ensemble classifier which builds a number of decision trees to improve the single tree classifier. Although there are several existing techniques for faults diagnosis, the application research on RF is meaningful and necessary because of its fast execution speed, the characteristics of tree classifier, and high performance in machine faults diagnosis. The proposed method is demonstrated by a case study on induction motor fault diagnosis. Experimental results indicate the validity and reliability of RF-based diagnosis method.

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Authors and Affiliations

  1. School of Mechanical Engineering, Pukyong National University, San 100, Yongdang-dong, Nam-gu, Busan, 608-739, South Korea

    Bo-Suk Yang & Xiao Di

  2. School of Mechanical Engineering, University of Science and Technology Beijing, 30 Xueyuan Road, Haidian District, 100083, Beijing, China

    Tian Han

Authors
  1. Bo-Suk Yang
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  2. Xiao Di
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  3. Tian Han
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Correspondence to Bo-Suk Yang.

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Yang, BS., Di, X. & Han, T. Random forests classifier for machine fault diagnosis. J Mech Sci Technol 22, 1716–1725 (2008). https://doi.org/10.1007/s12206-008-0603-6

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  • DOI: https://doi.org/10.1007/s12206-008-0603-6

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