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Preload state detection for precision spindle bearings based on multi-level classification

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Abstract

In this study, a multi-level state classification method based on support vector data description (SVDD) is proposed to detect bearing preload state. Firstly, a three-category classification support vector data description algorithm is proposed to establish the three-state non-aliasing hypersphere model, which can combine the kernel principal component analysis (KPCA) and membership degree. Then, by classifying the first-level training sample model of uniform and non-uniform preload states, the second-level training sample model of non-uniform preload states is established based on SVDD algorithm. Moreover, the balanced multi-label propagation classification criterion is defined that can be used to identify the preload state level based on the training sample model. Finally, a preload state detection system is developed, which can accurately simulate uniform/non-uniform preload states for spindle bearings. The experimental results demonstrate that the proposed algorithm can effectively classify the preload states of spindle bearings with average accuracy higher than 94%.

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Abbreviations

a1–3 :

The spherical center coordinates of three hyperspheres

C:

Penalty factors

F 1–3 :

The force applied on bearing

N 1–3 :

The number of samples of three categories, respectively

P:

Preload state feature set

R 1–3 :

The radius of three hyperspheres

S 1–3 :

Support vector set

Y:

Principal feature set

A:

Lagrange multiplier

λ:

Lagrange multiplier

xi, yj, zk :

The class I, class II and class III sample datasets

ξ:

Slack variable

θ:

Inclination angle

Δh:

The difference between the highest point and the lowest point on the bearing spacer

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Acknowledgments

This work was supported by the National Key Research and Development Program of China (No. 2018YFB2000504) and Major Technology Projects of Shaanxi province of China (No. 2018zdzx01-02-01) and Fundamental Research Funds for the Central Universities and National Science (No. xzd012019032). The authors express their gratitude for their support.

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Authors and Affiliations

  1. Key Laboratory of Education Ministry for Modern Design & Rotor-Bearings System, School of Mechanical Engineering, Xi’an Jiaotong University, Xi’an, China

    Xiaohu Li, Yuechen Han & Jun Hong

  2. School of Mechanical and Precision Instrument Engineering, Xi’an University of Technology, Xi’an, China

    Yanfei Zhang

  3. No. 32382 Unit of PLA, Wuhan, China

    Yuechen Han

Authors
  1. Xiaohu Li
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  2. Yanfei Zhang
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  3. Yuechen Han
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  4. Jun Hong
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Correspondence to Xiaohu Li.

Additional information

Xiaohu Li is an Associate Professor of the School of Mechanical Engineering, Xi’an Jiaotong University, Xi’an, China. He received his B.S. degree in Department of Mechanical Engineering in 1999. He received his M.S. degree in 2004, and Ph.D. degree in 2010 in School of Mechanical Engineering in Xi’an Jiao-tong University, respectively. His main research interests include intelligent spindle, electromechanical system optimization control and hydraulic servo control, etc.

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Li, X., Zhang, Y., Han, Y. et al. Preload state detection for precision spindle bearings based on multi-level classification. J Mech Sci Technol 34, 4393–4403 (2020). https://doi.org/10.1007/s12206-020-1004-8

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  • DOI: https://doi.org/10.1007/s12206-020-1004-8

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