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Abstract

Owing to the problem of the incipient fault characteristics being difficult to be extracted from the raw vibration signal of rolling element bearing, based on the empirical mode decomposition and kurtosis criteria, a fault diagnosis method for rolling element bearing is proposed by reducing rolling element bearing foundation vibration and noise-assisted vibration signal analysis. Firstly, rolling element bearing vibration signal is decomposed into a set of intrinsic mode functions using empirical mode decomposition and the intrinsic mode function component with the maximal kurtosis value is selected. Afterwards, zero mean normalization is applied to the selected intrinsic mode function component, and then the intrinsic mode function’s foundation vibration components within $\pm 2 X_{rms}$ are removed to minimize the interference. In order to eliminate interruption and intermittency after removal of the foundation vibration components, white noise is added to the newly generated signal. The noise-added signal is decomposed via empirical mode decomposition, and later on, IMF1 with the highest frequency band is selected and demodulated using envelope analysis. The resulting envelope spectrum can show more significant fault pulse characteristics, which are highly helpful to diagnose the rolling element bearing incipient faults. The proposed method in this paper was applied to the fault diagnosis for low noise REB 6203 and the testing results showed that the method could identify the rolling element bearing incipient faults accurately and quickly.

Keywords

Rolling element bearing fault diagnosis empirical mode decomposition rolling element bearing foundation vibration noise-assisted vibration signal analysis

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References

Caesarendra

Kosasih

Tieu

et al.

Application of the largest Lyapunov exponent algorithm for feature extraction in low speed slew bearing condition monitoring. Mech Syst Signal Process 2015; 50–51: 116–138.

Cong

Chen

Dong

et al.

Vibration model of rolling element bearings in a rotor-bearing system for fault diagnosis. J Sound Vib 2013; 332: 2081–2097.

Tse

Wang

. The design of a new sparsogram for fast bearing fault diagnosis: Part 1 of the two related manuscripts that have a joint title as “Two automatic vibration-based fault diagnostic methods using the novel sparsity measurement–Parts 1 and 2”. Mech Syst Signal Process 2013; 40: 499–519.

Lin

Zhang

et al.

Faults diagnostics of railway axle bearings based on IMF’s confidence index algorithm for ensemble EMD. Sensors 2015; 15: 10991–11011.

Saidi

Ben Ali

Fnaiech

et al.

Bi-spectrum based-EMD applied to the non-stationary vibration signals for bearing faults diagnosis. ISA Trans 2014; 53: 1650–1660.

Alwodai

Wang

Chen

et al.

A study of motor bearing fault diagnosis using modulation signal bispectrum analysis of motor current signals. J Signal Inform Process 2013; 4: 72–79.

Guo

et al.

Envelope extraction based dimension reduction for independent component analysis in fault diagnosis of rolling element bearing. J Sound Vib 2014; 333: 2983–2994.

Ben Ali

Fnaiech

Saidi

et al.

Application of empirical mode decomposition and artificial neural network for automatic bearing fault diagnosis based on vibration signals. Appl Acoust 2015; 89: 16–27.

Singh

Kumar

. Thrust bearing groove race defect measurement by wavelet decomposition of pre-processed vibration signal. Measurement 2013; 46: 3508–3515.

10.

Meng

Xiang

Wang

et al.

A hybrid fault diagnosis method using morphological filter–translation invariant wavelet and improved ensemble empirical mode decomposition. Mech Syst Signal Process 2015; 50–51: 101–115.

11.

Ocak

Loparo

. Estimation of the running speed and bearing defect frequencies of an induction motor from vibration data. Mech Syst Signal Process 2004; 18: 515–533.

12.

Xiong

Chen

et al.

Rolling bearing fault impact feature enhancement based on spectral kurtosis and non-local means. Chinese J Scient Instrum 2016; 37: 2176–2184.

13.

Muhammad

Iqbal

Campbell

. Vibration spectrum imaging: A novel bearing fault classification approach. IEEE Trans Ind Electron 2015; 62: 494–502.

14.

Randall

Antoni

. Rolling element bearing diagnostics—a tutorial. Mech Syst Signal Process 2011; 25: 485–520.

15.

Lee

J-Y

. Variable short-time Fourier transform for vibration signals with transients. J Vib Control 2015; 21: 1383–1397.

16.

Cai

J-H

Chen

Q-Y

. Bearing fault diagnosis method based on local mean decomposition and Wigner higher moment spectrum. Exp Tech 2016; 40: 1437–1446.

17.

Djebala

Babouri

Ouelaa

. Rolling bearing fault detection using a hybrid method based on Empirical Mode Decomposition and optimized wavelet multi-resolution analysis. Int J Adv Manuf Technol 2015; 79: 2093–2105.

18.

Huang

Shen

Long

et al.

The empirical mode decomposition and the Hilbert spectrum for nonlinear and non-stationary time series analysis. Proc R Soc Ser A 1998; 454: 903–995.

19.

Huang

MLC

Long

et al.

A confidence limit for the empirical mode decomposition and Hilbert spectral analysis. Proc R Soc, Ser A 2003; 459: 2317–2345.

20.

Lei

Lin

et al.

A review on empirical mode decomposition in fault diagnosis of rotating machinery. Mech Syst Signal Process 2013; 35: 108–126.

21.

Tang

. Rolling bearing fault feature extraction method based on ensemble empirical mode decomposition and kurtosis criterion. Proc CSEE 2012; 32: 106–111.

22.

Zhang

Tang

Xiao

. Time–frequency interpretation of multi-frequency signal from rotating machinery using an improved Hilbert–Huang transform. Measurement 2016; 82: 221–239.

23.

Deng

Wang

. Resonance demodulation technique based on IHT for rolling bearings fault diagnosis. J Aerosp Power 2012; 27: 69–74.

24.

Mathew J, Szczepanik A, Kuhnell BT, et al. Incipient damage detection in low speed bearings using the demodulation resonance analysis technique. In: International tribology conference, Melbourne, Australia, 1987, pp.366–369.

25.

Cao

Fan

Zhou

et al.

Wheel-bearing fault diagnosis of trains using empirical wavelet transform. Measurement 2016; 82: 439–449.

26.

Donoho

Johnstone

. Adapting to unknown smoothness via wavelet shrinkage. Publ Am Stat Assoc 1995; 90: 1200–1224.

27.

Sun

. Multiwavelet denoising with adaptive block thresholding and its application in gearbox diagnosis of rolling mills. J Vib Eng 2013; 26: 127–134.

28.

S-b

Yang

S-z

. Applied of 3σ-rule in reducing noise in signal by wavelet analysis. J Chongqing Univ 2002; 25: 58–61.

29.

Kou

Yuan

. Vibration signal processing in fault diagnosis, Beijing: Metallurgical Industry Press, 1989, pp. 55–64.

30.

Huang

. Ensemble empirical mode decomposition: A noise assisted data analysis method. Adv Adapt Data Anal 2009; 1: 1–41.

31.

Gaci

. A new ensemble empirical mode decomposition (EEMD) denoising method for seismic signals. Energy Procedia 2016; 97: 84–91.

32.

Žvokelj

Zupan

Prebil

. EEMD-based multiscale ICA method for slewing bearing fault detection and diagnosis. J Sound Vib 2016; 370: 394–423.

A fault diagnosis method for rolling element bearing (REB) based on reducing REB foundation vibration and noise-assisted vibration signal analysis

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