A New Method for Weak Fault Feature Extraction Based on Improved MED

Because of the characteristics of weak signal and strong noise, the low-speed vibration signal fault feature extraction has been a hot spot and difficult problem in the field of equipment fault diagnosis. Moreover, the traditional minimum entropy deconvolution (MED) method has been proved to be used to detect such fault signals. The MED uses objective function method to design the filter coefficient, and the appropriate threshold value should be set in the calculation process to achieve the optimal iteration effect. It should be pointed out that the improper setting of the threshold will cause the target function to be recalculated, and the resulting error will eventually affect the distortion of the target function in the background of strong noise. This paper presents an improved MED based method of fault feature extraction from rolling bearing vibration signals that originate in high noise environments. The method uses the shuffled frog leaping algorithm (SFLA), finds the set of optimal filter coefficients, and eventually avoids the artificial error influence of selecting threshold parameter. Therefore, the fault bearing under the two rotating speeds of 60 rpm and 70 rpm is selected for verification with typical low-speed fault bearing as the research object; the results show that SFLA-MED extracts more obvious bearings and has a higher signal-to-noise ratio than the prior MED method.

[1]  P. Tse,et al.  A comparison study of improved Hilbert–Huang transform and wavelet transform: Application to fault diagnosis for rolling bearing , 2005 .

[2]  Lei Hu,et al.  Enhanced detection of rolling element bearing fault based on stochastic resonance , 2012 .

[3]  Jin Chen,et al.  Spectral kurtosis based on AR model for fault diagnosis and condition monitoring of rolling bearing , 2012 .

[4]  Zhang Chunmei,et al.  Signal overcomplete representation and sparse decomposition based on redundant dictionaries , 2005 .

[5]  P. D. McFadden,et al.  Vibration monitoring of rolling element bearings by the high-frequency resonance technique — a review , 1984 .

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

[7]  Robert B. Randall,et al.  The enhancement of fault detection and diagnosis in rolling element bearings using minimum entropy deconvolution combined with spectral kurtosis , 2007 .

[8]  David W. Eaton,et al.  Improving seismic resolution of outermost core structure by multichannel analysis and deconvolution of broadband SmKS phases , 2006 .

[9]  A. Walden,et al.  PRINCIPLES AND APPLICATION OF MAXIMUM KURTOSIS PHASE ESTIMATION1 , 1988 .

[10]  R. Wiggins.  Minimum entropy deconvolution , 1978 .

[11]  F. Combet,et al.  Optimal filtering of gear signals for early damage detection based on the spectral kurtosis , 2009 .

[12]  Gholamreza Anbarjafari,et al.  Satellite Image Contrast Enhancement Using Discrete Wavelet Transform and Singular Value Decomposition , 2010, IEEE Geoscience and Remote Sensing Letters.

[13]  N. Huang,et al.  A study of the characteristics of white noise using the empirical mode decomposition method , 2004, Proceedings of the Royal Society of London. Series A: Mathematical, Physical and Engineering Sciences.

[14]  Jin Chen,et al.  Feature extraction of rolling bearing’s early weak fault based on EEMD and tunable Q-factor wavelet transform , 2014 .

[15]  Gang Yu,et al.  Machine fault diagnosis using a cluster-based wavelet feature extraction and probabilistic neural networks , 2009 .

[16]  K. Loparo,et al.  Bearing fault diagnosis based on wavelet transform and fuzzy inference , 2004 .

[17]  N. Tandon,et al.  A review of vibration and acoustic measurement methods for the detection of defects in rolling element bearings , 1999 .

[18]  Jing Wang,et al.  Improved Complexity Based on Time-Frequency Analysis in Bearing Quantitative Diagnosis , 2013 .

[19]  Maksim Bano.  Modeling and inverse Q imaging of ground penetrating radar waves in 1 and 2d , 1996 .

[20]  G. González,et al.  Period estimation using minimum entropy deconvolution (MED) , 1995, Signal Process..

[21]  R. Randall,et al.  OPTIMISATION OF BEARING DIAGNOSTIC TECHNIQUES USING SIMULATED AND ACTUAL BEARING FAULT SIGNALS , 2000 .

[22]  David C. Henley,et al.  Gabor deconvolution: Estimating reflectivity by nonstationary deconvolution of seismic data , 2011 .

[23]  Harish Sharma,et al.  Elitism based Shuffled Frog Leaping algorithm , 2016, 2016 International Conference on Advances in Computing, Communications and Informatics (ICACCI).

[24]  Peng Chen,et al.  Sequential Fuzzy Diagnosis for Plant Machinery , 2003 .

[25]  Asoke K. Nandi,et al.  EXTRACTION OF IMPACTING SIGNALS USING BLIND DECONVOLUTION , 2000 .

[26]  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.

[27]  Bing He,et al.  Feature fusion using kernel joint approximate diagonalization of eigen-matrices for rolling bearing fault identification , 2016 .

[28]  Hui Sun,et al.  Shuffled frog leaping algorithm based on enhanced learning , 2016, Int. J. Intell. Syst. Technol. Appl..

[29]  Min Chen,et al.  Extended stochastic resonance (SR) and its applications in weak mechanical signal processing , 2009 .

[30]  Robert B. Randall,et al.  Rolling element bearing diagnostics—A tutorial , 2011 .

[31]  Jihong Yan,et al.  Improved Hilbert-Huang transform based weak signal detection methodology and its application on incipient fault diagnosis and ECG signal analysis , 2014, Signal Process..

[32]  Robert B. Randall,et al.  Enhancement of autoregressive model based gear tooth fault detection technique by the use of minimum entropy deconvolution filter , 2007 .

[33]  Danilo P. Mandic,et al.  Emd via mEMD: multivariate noise-Aided Computation of Standard EMD , 2013, Adv. Data Sci. Adapt. Anal..