Noise Removal from the Vibration Signals of the Rotating Machinery Using the Empirical Wavelet Transform and the Conventional Thresholding Methods

Authors

1 Ph.D. Student, Mech. Eng., Guilan Univ., Rasht.

2 MSc. Student, Ahrar Institute of Technology and Higher Education, Rasht, Iran.

3 Prof., Mech. Eng., Guilan Univ., Rasht, Iran.

4 Assoc. Prof., Mech. Eng., Guilan Univ., Rasht, Iran.

Abstract

In this paper, a new method is presented for removing the noise from the vibration signals of the rotating machinery based on the empirical wavelet transform (EWT) and the soft thresholding function. The EWT is a new signal processing method that decomposes each signal into its constituent components based on its frequency information. After decomposing each signal, the soft thresholding method is performed to empirical modes and the denoised signal is reconstructed. For evaluating the proposed denoising approach, this technique is used for detecting the bearing fault. For this purpose, the kurtosis factor and the envelope spectrum of each denoised signal are calculated for detecting the presence of fault and diagnosing the fault type, respectively. The results illustrate that the proposed technique increases the quality of the vibration signals so that the obtained kurtosis value is more sensitive to the presence of fault in the inner ring and the outer ring. On the other hand, the type of fault is diagnosed by observing the appeared frequencies in the envelope spectrum of signals denoised with EWT. The results show that the EWT-based denoising approach is superior to the empirical mode decomposition-based denoising method in the rotating machinery fault diagnosis procedure.

Keywords

Main Subjects

References

[1]  He D, Wang X, Li S, Lin J, Zhao M (2016) Identification of multiple faults in rotating machinery based on minimum entropy deconvolution combined with spectral kurtosis. Mech Syst Signal Process 81: 235-249.
[2]  McFadden PD, Smith JD (1984) Vibration monitoring of rolling element bearings by the high-frequency resonance technique—a review. Tribol Int 17: 3-10.
[3]  Shi DF, Wang WJ, Qu LS (2004) Defect detection for bearings using envelope spectra of wavelet transform. J Vib Acoust 126: 567-573.
[4]  Wang D, Miao Q, Fan X, Huang HZ (2009) Rolling element bearing fault detection using an improved combination of Hilbert and wavelet transforms. J MECH SCI TECHNOL 23: 3292-3301.
[5]  Donoho DL, Johnstone JM (1994) Ideal spatial adaptation by wavelet shrinkage. Biometrika 81: 425-455.
[6]  Al-Raheem KF, Roy A, Ramachandran KP, Harrison DK, Grainger S (2009) Rolling element bearing faults diagnosis based on autocorrelation of optimized: wavelet de-noising technique. Int J Adv Manuf Technol 40: 393-402.
[7]  Sadooghi MS, Khadem SE (2016) A new performance evaluation scheme for jet engine vibration signal denoising. Mech Syst Signal Process 76: 201-212.
[8]  Mishra C, Samantaray AK, Chakraborty G (2017) Rolling element bearing fault diagnosis under slow speed operation using wavelet de-noising. Measurement 103: 77-86.
[9]  Huang NE, Shen Z, Long SR, Wu MC, Shih HH, Zheng Q, Yen NC, Tung CC, Liu HH (1998) The empirical mode decomposition and the Hilbert spectrum for nonlinear and non-stationary time series analysis. Proc R Soc Lond A Math Phys Sci 454: 903-995.
[10]  باقری ا، نظامیوند چگینی س، رمضانی دشتمیان م، فرید نجفی (1397) عیب­یابی چرخ­دنده بر پایه انتخاب مناسب­ترین ویژگی به کمک الگوریتم بهینه­سازی ازدحام ذرات. مکانیک سازه­ها و شاره­ها (3)8، 64-49.
[11]   Zhao DZ, Li JY, Cheng WD, Wang TY, Wen WG (2016) Rolling element bearing instantaneous rotational frequency estimation based on EMD soft-thresholding denoising and instantaneous fault characteristic frequency. J CENT SOUTH UNIV 23: 1682-1689.
[12]   Abdelkader R, Kaddour A, Derouiche Z (2018) Enhancement of rolling bearing fault diagnosis based on improvement of empirical mode decomposition denoising method. Int J Adv Manuf Technol 97: 1-19.
[13]  Wang R, Sun S, Guo X, Yan D (2018) EMD threshold denoising algorithm based on variance estimation. CIRCUITS SYST SIGNAL PROCESS 1-20.
[14]   Nguyen P, Kang M, Kim JM, Ahn BH, Ha JM, Choi BK (2015) Robust condition monitoring of rolling element bearings using de-noising and envelope analysis with signal decomposition techniques. Expert Syst Appl 42: 9024-9032.
[15]   Gilles J (2013) Empirical wavelet transform. IEEE Trans Signal Processing 61: 3999-4010.
[16]   Kedadouche M, Thomas M, Tahan A (2016) A comparative study between Empirical Wavelet Transforms and Empirical Mode Decomposition Methods: Application to bearing defect diagnosis. Mech Syst Signal Process 81: 88-107.
[17]   Pan J, Chen J, Zi Y, Li Y, He Z (2016) Mono-component feature extraction for mechanical fault diagnosis using modified empirical wavelet transform via data-driven adaptive Fourier spectrum segment. Mech Syst Signal Process 72: 160-183.
[18]   Kedadouche M, Liu Z, Vu VH (2016) A new approach based on OMA-empirical wavelet transforms for bearing fault diagnosis. Measurement 90: 292-308.
[19]   Singh O, Sunkaria RK (2017) ECG signal denoising via empirical wavelet transform. Australas Phys Eng Sci Med 40: 219-229.
[20]   Li J, Li Y, Li Y, Qian Z (2018) Downhole microseismic signal denoising via empirical wavelet transform and adaptive thresholding. J Geophys Eng 15: 2469.
[21]   Bearing Data Center-Case Western Reserve University.http://csegroups.case.edu/bearingdatacenter/pages/welcome case western-reserve-university-bearing-data center-website
[22]   He D, Wang X, Li S, Lin J, Zhao M (2017) An improved EMD method based on the multi-objective optimization and its application to fault feature extraction of rolling bearing. Appl Acoust 127: 46-62.
[23]   Dong S, Sun D, Tang B, Gao Z, Yu W, Xia M (2014) A fault diagnosis method for rotating machinery based on PCA and Morlet kernel SVM. MATH PROBL ENG.
[24]   Ziani R, Felkaoui A, Zegadi R (2017) Bearing fault diagnosis using multiclass support vector machines with binary particle swarm optimization and regularized Fisher’s criterion. J INTELL MANUF 28 (2): 405-417.
[25]   Zhang X, Zhang Q, Chen M, Sun Y, Qin X, Li H (2018) A two-stage feature selection and intelligent fault diagnosis method for rotating machinery using hybrid filter and wrapper method. Neurocomputing 275: 2426-2439.
Journal of Solid and Fluid Mechanics
Volume 9, Issue 1
April 2019
Pages 111-124

Files

Share

How to cite

Statistics