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.
Article PDF
Similar content being viewed by others
Avoid common mistakes on your manuscript.
References
L. Ciabattoni et al., Statistical spectral analysis for fault diagnosis of rotating machines, IEEE Trans. Ind. Electron., 65(5) (2018) 4301–4310.
H.-T. Yau et al., Fractional-order chaotic self-synchronization-based tracking faults diagnosis of ball bearing systems, IEEE Trans. Ind. Electron., 63(6) (2016) 3824–3833.
C. Sun et al., Support vector machine-based Grassmann manifold distance for health monitoring of viscoelastic sandwich structure with material ageing, J. Sound Vib., 368 (2016) 249–263.
W. Qiao and D. Lu, A survey on wind turbine condition monitoring and fault diagnosis-part ii: signals and signal processing methods, IEEE Trans. Ind. Electron., 62(10) (2015) 6546–6557.
A. Wan et al., Prognostics of gas turbine: a condition-based maintenance approach based on multi-environmental time similarity, Mech. Syst. Signal Process., 109 (2018) 150–165.
B. Cai et al., A data-driven fault diagnosis methodology in three-phase inverters for PMSM drive systems, IEEE Trans. Power Electron., 32(7) (2017) 5590–5600.
D. L. Donoho, De-noising by soft-thresholding, IEEE Trans. Inf. Theory, 41(3) (1995) 613–627.
D. L. Donoho and I. M. Johnstone, Adapting to unknown smoothness via wavelet shrinkage, J. Am. Stat. Assoc., 90(432) (1995) 1200–1224.
Z. Meng and S. Li, Rolling bearing fault diagnosis based on improved wavelet threshold de-noising method and HHT, J. Vib. Eng., 32(14) (2013) 204–208+214.
B. Xie et al., Gamma spectrum denoising method based on improved wavelet threshold, Nucl. Eng. Technol., 52(8) (2020) 1771–1776.
J. Li et al., Downhole microseismic signal denoising via empirical wavelet transform and adaptive thresholding, J. Geophys. Eng., 15(6) (2018) 2469–2480.
S. N. Chegini, A. Bagheri and F. Najafi, Application of a new EWT-based denoising technique in bearing fault diagnosis, Measurement, 144 (2019) 275–297.
W. Chen et al., Fault feature extraction and diagnosis of rolling bearings based on wavelet thresholding denoising with CEEMDAN energy entropy and PSO-LSSVM, Measurement, 172 (2021) 108901.
F. He et al., Blind denoising of 3D seismic signals based on the wave atom transform, J. Vib. Shock, 38(8) (2019) 88–95.
P. Chen and Q. Zhang, Classification of heart sounds using discrete time-frequency energy feature based on S transform and the wavelet threshold denoising, Biomed. Signal Process. Control, 57 (2020) 101684.
P. Wang et al., Bearing fault signal denoising method of hierarchical adaptive wavelet threshold function, J. Vib. Eng., 32(3) (2019) 548–556.
H. Liu et al., A de-noising method using the improved wavelet threshold function based on noise variance estimation, Mech. Syst. Signal Process., 99 (2018) 30–46.
F. M. Bayer, A. J. Kozakevicius and R. J. Cintra, An iterative wavelet threshold for signal denoising, Signal Processing, 162 (2019) 10–20.
D. L. Donoho and I. M. Johnstone, Ideal spatial adaptation by wavelet shrinkage, Biometrika, 81(3) (1994) 425–455.
S. G. Chang, B. Yu and M. Vetterli, Adaptive wavelet thresholding for image denoising and compression, IEEE Trans. Image Process., 9(9) (2000) 1532–1546.
S. Poornachandra, Wavelet-based denoising using subband dependent threshold for ECG signals, Digit. Signal Process. A Rev. J., 18(1) (2008) 49–55.
J. Gilles, Empirical wavelet transform, IEEE Trans. Signal Process., 61(16) (2013) 3999–4010.
N. E. Huang et al., A confidence limit for the empirical mode decomposition and Hilbert spectral analysis, Proc. R. Soc. London. Ser. A Math. Phys. Eng. Sci., 459(2037) (2003) 2317–2345.
X. Yan and M. Jia, Application of CSA-VMD and optimal scale morphological slice bispectrum in enhancing outer race fault detection of rolling element bearings, Mech. Syst. Signal Process., 122 (2019) 56–86.
Y. Miao et al., Application of an improved maximum correlated kurtosis deconvolution method for fault diagnosis of rolling element bearings, Mech. Syst. Signal Process., 92 (2017) 173–195.
M. S. Sadooghi and S. Esmaeilzadeh Khadem, A new performance evaluation scheme for jet engine vibration signal denoising, Mech. Syst. Signal Process., 76–77 (2016) 201–212.
R. Abdelkader, A. Kaddour and Z. Derouiche, Enhancement of rolling bearing fault diagnosis based on improvement of empirical mode decomposition denoising method, Int. J. Adv. Manuf. Technol., 97(5–8) (2018) 3099–3117.
B. Wang, XJTU-SY Bearing Datasets, http://biaowang.tech/xjtu-sy-bearing-datasets/.
B. Wang et al., A hybrid prognostics approach for estimating remaining useful life of rolling element bearings, IEEE Transactions on Reliability, 69(1) (2018) 401–412.
Acknowledgments
This work was supported by the National Natural Science Foundation of China (51975117), and Jiangsu Provincial Key Research and Development Program (BE2019086).
Author information
Authors and Affiliations
Corresponding author
Additional information
Chao Li received the B.S. degree in Mechanical Engineering from Southeast University, Nanjing, China, in 2020. He is currently pursuing M.S. degree from Southeast University, Nanjing, 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, Nanjing, 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, Nanjing, 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, Nanjing, China, in 2021. His main research interests include signal processing, electromechanical equipment intelligent monitoring and fault diagnosis.
Rights and permissions
About this article
Cite this article
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
Received:
Revised:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s12206-022-0306-4