An adaptively fast ensemble empirical mode decomposition method and its applications to rolling element bearing fault diagnosis

Ensemble empirical mode decomposition (EEMD) represents a significant improvement over the original empirical mode decomposition (EMD) method for eliminating the mode mixing problem. However, the added white noises generate some tough problems including the high computational cost, the determination of the two critical parameters (the amplitude of the added white noise and the number of ensemble trials), and the contamination of the residue noise in the signal reconstruction. To solve these problems, an adaptively fast EEMD (AFEEMD) method combined with complementary EEMD (CEEMD) is proposed in this paper. In the proposed method, the two critical parameters are respectively fixed as 0.01 times standard deviation of the original signal and two ensemble trials. Instead, the upper frequency limit of the added white noise is the key parameter which needs to be prescribed beforehand. Unlike the original EEMD method, only two high-frequency white noises are added to the signal to be investigated with anti-phase in AFEEMD. Furthermore, an index termed relative root-mean-square error is employed for the adaptive selection of the proper upper frequency limit of the added white noises. Simulation test and vibration signals based fault diagnosis of rolling element bearing under different fault types are utilized to demonstrate the feasibility and effectiveness of the proposed method. The analysis results indicate that the AFEEMD method represents a sound improvement over the original EEMD method, and has strong practicability.

[1]  Yuesheng Xu,et al.  Gearbox fault diagnosis using empirical mode decomposition and Hilbert spectrum , 2006 .

[2]  Yang Yu,et al.  A roller bearing fault diagnosis method based on EMD energy entropy and ANN , 2006 .

[3]  Min-Hung Yeh.  The complex bidimensional empirical mode decomposition , 2012, Signal Process..

[4]  Yaguo Lei,et al.  A review on empirical mode decomposition in fault diagnosis of rotating machinery , 2013 .

[5]  Raïs El'hadi Bekka,et al.  Improvement of Ensemble Empirical Mode Decomposition by over-Sampling , 2013, Adv. Data Sci. Adapt. Anal..

[6]  Guanghong Gai.  The processing of rotor startup signals based on empirical mode decomposition , 2006 .

[7]  Junsheng Cheng,et al.  Application of support vector regression machines to the processing of end effects of Hilbert Huang transform , 2007 .

[8]  Peter W. Tse,et al.  Faulty bearing signal recovery from large noise using a hybrid method based on spectral kurtosis and ensemble empirical mode decomposition , 2012 .

[9]  Ling He,et al.  Study of empirical mode decomposition and spectral analysis for stress and emotion classification in natural speech , 2011, Biomed. Signal Process. Control..

[10]  Dongxiang Jiang,et al.  Application of the ensemble empirical mode decomposition and Hilbert transform to pedestal looseness , 2011 .

[11]  Rami K. Niazy,et al.  Performance Evaluation of Ensemble Empirical Mode Decomposition , 2009, Adv. Data Sci. Adapt. Anal..

[12]  Yaguo Lei,et al.  Fault Diagnosis of Rotating Machinery Based on an Adaptive Ensemble Empirical Mode Decomposition , 2013, Sensors.

[13]  Peter W. Tse,et al.  A novel signal compression method based on optimal ensemble empirical mode decomposition for bearing vibration signals , 2013 .

[14]  Norden E. Huang,et al.  Complementary Ensemble Empirical Mode Decomposition: a Novel Noise Enhanced Data Analysis Method , 2010, Adv. Data Sci. Adapt. Anal..

[15]  N. Huang,et al.  The empirical mode decomposition and the Hilbert spectrum for nonlinear and non-stationary time series analysis , 1998, Proceedings of the Royal Society of London. Series A: Mathematical, Physical and Engineering Sciences.

[16]  Norden E. Huang,et al.  A review on Hilbert‐Huang transform: Method and its applications to geophysical studies , 2008 .

[17]  Da-Chao Lin,et al.  Elimination of end effects in empirical mode decomposition by mirror image coupled with support vector regression , 2012 .

[18]  Hongkai Jiang,et al.  An improved EEMD with multiwavelet packet for rotating machinery multi-fault diagnosis , 2013 .

[19]  Norden E. Huang,et al.  Ensemble Empirical Mode Decomposition: a Noise-Assisted Data Analysis Method , 2009, Adv. Data Sci. Adapt. Anal..

[20]  Yujing Wang,et al.  Classification of fault location and the degree of performance degradation of a rolling bearing based on an improved hyper-sphere-structured multi-class support vector machine , 2012 .

[21]  Yu Yang,et al.  Partly ensemble empirical mode decomposition: An improved noise-assisted method for eliminating mode mixing , 2014, Signal Process..

[22]  Ivan Prebil,et al.  Non-linear multivariate and multiscale monitoring and signal denoising strategy using Kernel Principal Component Analysis combined with Ensemble Empirical Mode Decomposition method , 2011 .

[23]  Robert X. Gao,et al.  Performance enhancement of ensemble empirical mode decomposition , 2010 .