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Feature selection for fault level diagnosis of planetary gearboxes

Author

Listed:
  • Zhiliang Liu
  • Xiaomin Zhao
  • Ming Zuo
  • Hongbing Xu

Abstract

Feature selection is critical to maintain high performance of classification-based fault diagnosis with a large feature size. In this paper, we propose a criterion to evaluate features effectiveness by class separability that is defined on cosine similarity in the kernel space of the Gaussian radial basis function. We develop a feature selection algorithm accordingly using the proposed criterion together with sequential backward selection and a feature re-ranking mechanism. We then employ the proposed feature selection algorithm to determine fault-sensitive features and select them for fault level diagnosis of planetary gearboxes. The experimental results demonstrate that the proposed algorithm can effectively reduce the feature size and improve accuracy of fault level diagnosis simultaneously. Copyright Springer-Verlag Berlin Heidelberg 2014

Suggested Citation

  • Zhiliang Liu & Xiaomin Zhao & Ming Zuo & Hongbing Xu, 2014. "Feature selection for fault level diagnosis of planetary gearboxes," Advances in Data Analysis and Classification, Springer;German Classification Society - Gesellschaft für Klassifikation (GfKl);Japanese Classification Society (JCS);Classification and Data Analysis Group of the Italian Statistical Society (CLADAG);International Federation of Classification Societies (IFCS), vol. 8(4), pages 377-401, December.
    • Handle: RePEc:spr:advdac:v:8:y:2014:i:4:p:377-401
    DOI: 10.1007/s11634-014-0168-4
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    Cited by:

    1. Asma Gul & Aris Perperoglou & Zardad Khan & Osama Mahmoud & Miftahuddin Miftahuddin & Werner Adler & Berthold Lausen, 2018. "Ensemble of a subset of kNN classifiers," Advances in Data Analysis and Classification, Springer;German Classification Society - Gesellschaft für Klassifikation (GfKl);Japanese Classification Society (JCS);Classification and Data Analysis Group of the Italian Statistical Society (CLADAG);International Federation of Classification Societies (IFCS), vol. 12(4), pages 827-840, December.
    2. E. Emary & Hossam M. Zawbaa & Aboul Ella Hassanien & B. Parv, 2017. "Multi-objective retinal vessel localization using flower pollination search algorithm with pattern search," Advances in Data Analysis and Classification, Springer;German Classification Society - Gesellschaft für Klassifikation (GfKl);Japanese Classification Society (JCS);Classification and Data Analysis Group of the Italian Statistical Society (CLADAG);International Federation of Classification Societies (IFCS), vol. 11(3), pages 611-627, September.

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