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

Author:

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

Publisher

Springer Science and Business Media LLC

Subject

Mechanical Engineering,Mechanics of Materials

Reference40 articles.

1. X. B. Xu, Y. C. Wang and C. L. Wen, Information–fusion method for fault diagnosis based on reliability evaluation of evidence, Control Theory & Applications, 28 (4) (2011) 504–510.

2. L. Y. Guan, Y. M. Shao, F. S. Gu, B. Fazenda and A. Ball, Gearbox fault diagnosis under different operating conditions based on time synchronous average and ensemble empirical mode decomposition, Iccas–Sice (2009) 383–388.

3. M. Kang, J. Kim, L. M. Wills and J. M. Kim, Time–varying and multiresolution envelope analysis and discriminative feature analysis for bearing fault diagnosis, IEEE Transactions on Industrial Electronics, 62 (12) (2015) 7749–7761.

4. Y. Xu and S. Xiu, A new and effective method of bearing fault diagnosis using wavelet packet transform combined with support vector machine, J. of Computers, 6 (11) (2011) 2502–2509.

5. L. Meng, J. Xiang, Y. Zhong and W. Song, Fault diagnosis of rolling bearing based on second generation wavelet denoising and morphological filter, J. of Mechanical Science and Technology, 29 (8) (2015) 3121–3129.

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