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A rolling element bearing fault feature extraction method based on the EWT and an arctangent threshold function

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Abstract

Hard and soft threshold functions are discontinuous at the threshold and deviate at the wavelet estimation coefficient, respectively. Aiming at this problem, a rolling element bearing (REB) fault feature extraction method is proposed based on the empirical wavelet transform (EWT) and an arctangent threshold function (ATF). First, the input signal is decomposed with the EWT, and intrinsic mode functions (IMFs) containing fault information are selected according to their cross-correlation coefficients and kurtosis values. Second, the selected IMFs are denoised by the ATF. Finally, to extract the fault characteristic frequency and determine the fault type, the denoised IMFs are added to form a reconstructed signal for envelope analysis. The superiority of the proposed method is verified on simulation signals and actual fault signals (including two cases); the developed approach has strong denoising and fault feature extraction effects.

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Acknowledgments

This work was supported by the National Natural Science Foundation of China (51975117), and Jiangsu Provincial Key Research and Development Program (BE2019086).

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Authors and Affiliations

  1. School of Mechanical Engineering, Southeast University, Nan**g, 211189, China

    Chao Li, Feiyun Xu, Hongxin Yang & Lei Zou

Authors
  1. Chao Li
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  2. Feiyun Xu
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  3. Hongxin Yang
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  4. Lei Zou
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Corresponding author

Correspondence to Feiyun Xu.

Additional information

Chao Li received the B.S. degree in Mechanical Engineering from Southeast University, Nan**g, China, in 2020. He is currently pursuing M.S. degree from Southeast University, Nan**g, China. His main research interests include signal processing and fault diagnosis.

Feiyun Xu received the Ph.D. degree in Precision Instrumentation and Machinery from Southeast University, Nan**g, China, in 1996. Currently, he is a Professor of Mechanical Engineering, Southeast University, China. His main research interests include artificial intelligence theory and application, measurement and control technology, time series analysis and nonlinear system identification.

Hongxin Yang received the B.S. degree in Mechanical Engineering from Chongqing University, Chongqing, China, in 2020. He is currently pursuing M.S. degree from Southeast University, Nan**g, China. His main research interests include signal processing, FPGA and fault diagnosis.

Lei Zou received the M.S. degree in Mechanical Engineering from Southeast University, Nan**g, China, in 2021. His main research interests include signal processing, electromechanical equipment intelligent monitoring and fault diagnosis.

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Li, C., Xu, F., Yang, H. et al. A rolling element bearing fault feature extraction method based on the EWT and an arctangent threshold function. J Mech Sci Technol 36, 1693–1708 (2022). https://doi.org/10.1007/s12206-022-0306-4

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  • DOI: https://doi.org/10.1007/s12206-022-0306-4

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