In this paper, the empirical mode decomposition as a signal processing method has been studied to overcome one of its shortcomings. In the previous studies, some improvements have been made on the empirical mode decomposition and it has been applied for condition monitoring of mechanical systems. These improvements include elimination of mode mixing and restraining of end effect in empirical mode decomposition method. In this research, to increase the accuracy of empirical mode decomposition, a new local mean has been proposed in the sifting process. Through the proposed local mean, the overshoot and undershoot problems in defining the local mean of common algorithm are alleviated. Meanwhile, it is capable to separate the components with close frequencies. Through the analysis of simulated signals via the new algorithm, it is shown that the accuracy is improved. Finally, empirical mode decomposition-based fault diagnosis approach has been applied to a vibration signal obtained from a faulty gearbox. The results show that the proposed method can resolve the effects of damage in vibration signals better than the common empirical mode decomposition method and helps for the isolation and localization of the fault.
FengZZaoMJ. Vibration signal models for fault diagnosis of planetary gearboxes. J Sound Vib2012; 331: 4919–4939.
2.
JenaDPPanigrahiSNKumarR. Gear fault identification and localization using analytic wavelet transform of vibration signal. Measurement2013; 46: 1115–1124.
3.
LeiYZuoMJHoseiniM. The use of ensemble empirical mode decomposition to improve bispectral analysis for fault detection in rotating machinery. Proc IMechE, Part C: J Mechanical Engineering Science2009; 224: 1759–1769.
4.
EntezamiMHillmansenSWestonP. Fault detection and diagnosis within a wing turbine mechanical braking system using condition monitoring. Renewable Energy2012; 47: 175–182.
5.
BartelmusV. Mathematical modelling and computer simulations as an aid to gearbox diagnostics. Mech Syst Signal Process2001; 15: 855–871.
6.
FakhfakhTChaariFHadderM. Numerical and experimental analysis of a gear system with teeth defects. Int J Adv Manuf Technol2005; 25: 542–550.
7.
YangMMakisV. ARX model-based gearbox fault detection and localization under varying load conditions. J Sound Vib2010; 329: 5209–5221.
8.
LiHZhangYZhengH. Application of Hermitian Wavelet to crack fault detection in gearbox. Mech Syst Signal Process2011; 25: 1353–1363.
9.
KarCMhantyAR. Vibration and current transient monitoring for gearbox fault detection using multiresolution Fourier transform. J Sound Vib2008; 311: 109–132.
10.
Al-badourFSunarMChededL. Vibration analysis of rotating machinery using time frequency analysis and wavelet techniques. Mech Syst Signal Process2011; 25: 2083–2101.
11.
LiBZhangPLiuD. Classification of time-frequency representation based on two-direction 2DLDA for gear fault diagnosis. Appl Soft Comput2011; 11: 5299–5305.
12.
HuangNEShenZLongSR. The empirical mode decomposition and the Hilbert spectrum for nonlinear and non-stationary time series analysis. Proc R Soc London A1998; 454: 903–995.
13.
AndradeAONasutoSKyberdP. EMG signal filtering based on empirical mode decomposition. Biomed Signal Process Control2006; 1: 44–55.
14.
HuangYSchmittFGLuZ. Analysis of daily river flow fluctuations using empirical mode decomposition and arbitrary order Hilbert spectral analysis. J Hydrol2009; 373: 103–111.
15.
TangBDongSSongT. Method for eliminating mode mixing of empirical mode decomposition based on the revised blind source separation. Signal Process2012; 92: 248–258.
16.
WangTZhangMYuQ. Comparing the application of EMD and EEMD on time-frequency analysis of seismic signal. J Appl Geophys2012; 83: 29–34.
17.
PareyAPachoriRB. Variable cosine windowing of intrinsic mode functions: application to gear fault diagnosis. Measurement2012; 45: 415–426.
18.
LinDGuoZAnF. Elimination of end effect in empirical mode decomposition by mirror image coupled with support vector regression. Mech Syst Signal Process2012; 31: 13–28.
19.
LeeSMChoiYS. Fault diagnosis of partial rub and looseness in rotating machinery using Hilbert-Huang transform. J Mech Sci Technol2008; 22: 2151–2162.
20.
FeldmanM. Hilbert transform in vibration analysis. Mech Syst Signal Process2011; 25: 735–802.
21.
BraunSFeldmanM. Decomposition of non-stationary signals into varying time scales: some aspects of EMD and HVD methods. Mech Syst Signal Process2011; 25: 2608–2630.
22.
ChenQHHuangNRiemenschneiderS. A B-spline approach for empirical mode decompositions. Adv Comput Math2006; 24: 171–195.
23.
HawleySDAtlasLEChizeckHJ. Some properties of an empirical mode type signal decomposition algorithm. IEEE Signal Process Lett2010; 17: 24–27.
24.
MeignenSPerrierV. A new formulation for empirical mode decomposition based on constrained optimization. IEEE Signal Process Lett2007; 14: 932–935.
25.
DamervalCMeignenSPerrierV. A fast algorithm for bidimensional EMD. IEEE Signal Process Lett2005; 12: 701–704.
RoyADohertyJF. Improved signal analysis performance at low sampling rates using raised cosine empirical mode decomposition. Electron Lett2010; 46: 176–177.
28.
ChengGChengYLShenLH. Gear fault identification based on Hilbert–Huang transform and SOM neural network. Measurement2013; 46: 1137–1146.
29.
YangYYuDChengJ. A fault diagnosis approach for roller bearing based on IMF envelope spectrum and SVM. Measurement2007; 40: 943–950.
30.
WuFQuL. An improved method for restraining the end effect in empirical mode decomposition and its applications to the fault diagnosis of large rotating machinery. J Sound Vib2008; 314: 586–602.
31.
LinCLiuPYehP. Application of empirical mode decomposition in the impact-echo test. NDT&E Int2009; 42: 589–598.
32.
HuangNEWuZ. A review on Hilbert-Huang Transform: method and its applications to geophysical studies. Rev Geophys2008; 46: 1–23.
33.
TsaoWCLiYFLeDD. An insight concept to select appropriate IMFs for envelope analysis of bearing fault diagnosis. Measurement2012; 45: 1489–1498.
34.
LoutridisSJ. Damage detection in gear systems using empirical mode decomposition. Eng Struct2004; 26: 1833–1841.
