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Fault diagnosis method of rolling bearing using principal component analysis and support vector machine

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Abstract

To effectively extract the fault feature information of rolling bearings and improve the performance of fault diagnosis, a fault diagnosis method based on principal component analysis and support vector machine was presented, and the rolling bearings signals with different fault states were collected. To address the limitation on effectively dealing with the raw vibration signals by the traditional signal processing technology based on Fourier transform, wavelet packet decomposition was employed to extract the features of bearing faults such as outer ring flaking, inner ring flaking, roller flaking and normal condition. Compared with the previous literature on fault diagnosis using principal component analysis (PCA) and support vector machine (SVM), one-to-one and one-to-many algorithms were taken into account. Additionally, the effect of four kernel functions, such as liner kernel function, polynomial kernel function, radial basis function and hyperbolic tangent kernel function, on the performance of SVM classifier was investigated, and the optimal hype-parameters of SVM classifier model were determined by genetic algorithm optimization. PCA was employed for dimension reduction, so as to reduce the computational complexity. The principal components that reached more than 95 % cumulative contribution rate were extracted by PCA and were input into SVM and BP neural network classifiers for identification. Results show that the fault feature dimensionality of the rolling bearing is reduced from 8-dimensions to 5-dimensions, which can still characterize the bearing status effectively, and the computational complexity is reduced as well. Compared with the raw feature set, PCA has a higher fault diagnosis accuracy (more than 97 %), and a shorter diagnosis time relatively. To better verify the superiority of the proposed method, SVM classification results were compared with the results of BP neural network. It is concluded that SVM classifier achieved a better performance than BP neural network classifier in terms of the classification accuracy and time-cost.

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Authors and Affiliations

  1. School of Mechanical and Electrical Engineering, Jiangxi University of Science and Technology, Ganzhou, Jiangxi, 341000, China

    Ying-Kui Gu, Xiao-Qing Zhou, Dong-Ping Yu & Yan-Jun Shen

Authors
  1. Ying-Kui Gu
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  2. Xiao-Qing Zhou
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  3. Dong-Ping Yu
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  4. Yan-Jun Shen
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Correspondence to Ying-Kui Gu.

Additional information

This paper was presented at ICMR 2017 & QR2MSE 2017, Crowne Plaza Chengdu West, Chengdu, China, October 24–27, 2017. Recommended by Guest Editor Dong Ho Bae.

Ying-Kui Gu is a Professor of the School of Mechanical and Electrical Engineering, at Jiangxi University of Science and Technology. He received a Ph.D. in Mechanical Engineering from Dalian University of Technology in 2005. His research interests include reliability engineering, optimization design, and product development.

Xiao-Qing Zhou is currently an M.S. candidate in Mechanical Engineering at Jiangxi University of Science and Technology. He received his B.S. in Industrial Engineering from Jiangxi University of Science and Technology in 2016. His research interests include reliability engineering and fault diagnosis.

Dong-Ping Yu is currently an M.S. candidate in Mechanical Engineering at Jiangxi University of Science and Technology. He received his B.S. in Mechanical Engineering from Jiangxi University of Science and Technology in 2015. His research interests include reliability engineering and fault diagnosis.

Yan-Jun Shen is currently an M.S. candidate in Mechanical Engineering at Jiangxi University of Science and Technology. He received his B.S. in Mechanical Engineering from China University of Geosciences in 2015. His research interests include reliability engineering and fault diagnosis.

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Gu, YK., Zhou, XQ., Yu, DP. et al. Fault diagnosis method of rolling bearing using principal component analysis and support vector machine. J Mech Sci Technol 32, 5079–5088 (2018). https://doi.org/10.1007/s12206-018-1004-0

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  • DOI: https://doi.org/10.1007/s12206-018-1004-0

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