An improvement EMD method based on the optimized rational Hermite interpolation approach and its application to gear fault diagnosis

Abstract A demodulation technique based on improvement empirical mode decomposition (EMD) is investigated in this paper. Firstly, the problem of the envelope line in EMD is introduced and the drawbacks of two classic interpolation methods, cubic spline interpolation method and cubic Hermite interpolation method are discussed; then a new envelope interpolation method called optimized rational Hermite interpolation method (O-EMD) is proposed, which has a shape controlling parameter compared with the cubic Hermite interpolation algorithm. At the same time, in order to improve the envelope approximation accuracy of local mean, the parameter determining criterion is put forward and an optimization with Genetic Algorithm (GA) is applied to automatic select the suitable shape controlling parameter in each sifting process. The effectiveness of O-EMD method is validated by the numerical simulations and an application to gear fault diagnosis. Results demonstrate that O-EMD method can improve the reliability and accuracy significantly compared with traditional EMD method.

[1]  W. J. Staszewski,et al.  Application of the Wavelet Transform to Fault Detection in a Spur Gear , 1994 .

[2]  A Yoshida,et al.  Application of the wavelet transform to health monitoring and evaluation of dynamic characteristics in gear sets , 2004 .

[3]  Yu Yang,et al.  A fault diagnosis approach for roller bearing based on IMF envelope spectrum and SVM , 2007 .

[4]  Minqiang Xu,et al.  Diagnostics of reciprocating compressor fault based on a new envelope algorithm of empirical mode decomposition , 2014 .

[5]  T. A. McMahon,et al.  Empirical Mode Decomposition : Improvement and Application , 2007 .

[6]  Zhijing Yang,et al.  A method to eliminate riding waves appearing in the empirical AM/FM demodulation , 2008, Digit. Signal Process..

[7]  Raghunathan Rengaswamy,et al.  A modified empirical mode decomposition (EMD) process for oscillation characterization in control loops , 2007 .

[8]  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.

[9]  I. Osorio,et al.  Intrinsic time-scale decomposition: time–frequency–energy analysis and real-time filtering of non-stationary signals , 2007, Proceedings of the Royal Society A: Mathematical, Physical and Engineering Sciences.

[10]  Hualou Liang,et al.  Application of the empirical mode decomposition to the analysis of esophageal manometric data in gastroesophageal reflux disease , 2005, IEEE Transactions on Biomedical Engineering.

[11]  Howard Jay Chizeck,et al.  Some Properties of an Empirical Mode Type Signal Decomposition Algorithm , 2010, IEEE Signal Process. Lett..

[12]  Jinde Zheng,et al.  Generalized empirical mode decomposition and its applications to rolling element bearing fault diagnosis , 2013 .

[13]  Paolo Pennacchi,et al.  Diagnostics of gear faults based on EMD and automatic selection of intrinsic mode functions , 2011 .

[14]  Junsheng Cheng.  Empirical Envelope Demodulation Approach Based on Local Characteristic-scale Decomposition and Its applications to Mechanical Fault Diagnosis , 2012 .

[15]  Yuesheng Xu,et al.  A B-spline approach for empirical mode decompositions , 2006, Adv. Comput. Math..

[16]  Yu Yang,et al.  Application of time–frequency entropy method based on Hilbert–Huang transform to gear fault diagnosis , 2007 .

[17]  Hai Qiu,et al.  Wavelet filter-based weak signature detection method and its application on rolling element bearing prognostics , 2006 .

[18]  L. Cohen,et al.  Time-frequency distributions-a review , 1989, Proc. IEEE.

[19]  河村 千里.  On the Frequency , 1902 .

[20]  Shuren Qin,et al.  A new envelope algorithm of Hilbert-Huang Transform , 2006 .

[21]  Gabriel Rilling,et al.  On empirical mode decomposition and its algorithms , 2003 .

[22]  Peter W. Tse,et al.  Wavelet Analysis and Envelope Detection For Rolling Element Bearing Fault Diagnosis—Their Effectiveness and Flexibilities , 2001 .

[23]  Steve McLaughlin,et al.  Investigation and Performance Enhancement of the Empirical Mode Decomposition Method Based on a Heuristic Search Optimization Approach , 2008, IEEE Transactions on Signal Processing.

[24]  Sam Kwong,et al.  Genetic algorithms and their applications , 1996, IEEE Signal Process. Mag..

[25]  Norden E. Huang,et al.  On Instantaneous Frequency , 2009, Adv. Data Sci. Adapt. Anal..

[26]  Li Lin,et al.  Signal feature extraction based on an improved EMD method , 2009 .