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Rolling bearing fault diagnosis based on mean multigranulation decision-theoretic rough set and non-naive Bayesian classifier

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Abstract

To analyze data from multi-level view, reduce computational burden, and improve fault diagnosis accuracy, a novel fault diagnosis method of rolling bearings based on mean multigranulation decision-theoretic rough set (MMG-DTRS) and non-naive Bayesian classifier (NNBC) is proposed in this paper. First, fault diagnosis features of rolling bearings in training samples are extracted to construct MMG-DTRS. Then, the significance degree of condition attribute in MMG-DTRS is defined to quantitatively measure the influence of condition attributes with respect to the decision ability of an information system. An attribute reduction algorithm based on MMG-DTRS is applied to acquire a lower dimensional condition attribute set, which reduces computational complexity and avoids the interference of irrelevant or redundant condition attributes. Finally, NNBC is constructed to classify rolling bearing conditions in test samples. The classification procedures by using NNBC are given. The performance of the proposed method is validated and the advantages are investigated by using a fault diagnosis experiment of rolling bearings. Experimental investigations demonstrate the proposed method is effective and reliable in identifying fault categories and fault severities of rolling bearings.

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Authors and Affiliations

  1. School of Automation, Harbin University of Science and Technology, Harbin, 150080, China

    Jun Yu

  2. School of Computer Science and Technology, Harbin University of Science and Technology, Harbin, 150080, China

    Bo Ding & Yongjun He

Authors
  1. Jun Yu
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  2. Bo Ding
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  3. Yongjun He
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Correspondence to Yongjun He.

Additional information

Recommended by Associate Editor Doo Ho Lee

Jun Yu received his M.S. and Ph.D. in Mechanical and Electrical Engineering from Harbin Institute of Technology, in China, in 2009 and 2017, respectively. He has been working in the School of Harbin University of Science and Technology. His main research interests include mechanical system fault diagnosis, knowledge discovery and data mining.

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Yu, J., Ding, B. & He, Y. Rolling bearing fault diagnosis based on mean multigranulation decision-theoretic rough set and non-naive Bayesian classifier. J Mech Sci Technol 32, 5201–5211 (2018). https://doi.org/10.1007/s12206-018-1018-7

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  • DOI: https://doi.org/10.1007/s12206-018-1018-7

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