Multi-stages helical gearbox fault detection using vibration signal and Morlet wavelet transform adapted by information entropy difference

Although the wavelet analysis is a powerful tool and has been widely used for the vibration signal based gearbox fault diagnosis, there are some limitations that undermine its application. The results of the wavelet transform do not possess time invariant property, which may result in the loss of useful information and decrease the accuracy of fault diagnosis. Other limitations in wavelet transform are the selection of the suitable threshold and the wavelet function. A main challenge of wavelet analysis is the adaptability of the parameters of the mother wavelet to the time variance of the given signal. To overcome this deficiency, an adaptive Morlet wavelet transform method based on the information entropy optimization is proposed in this study. The proposed wavelet transform method is applied for analyzing the vibration signals to detect and diagnose the faults of a helical gearbox. A comparative study which used the kurtosis maximization to adapt the wavelet parameters are also carried out to evaluate the proposed method.

[1]  Fengshou Gu,et al.  Two Stage Helical Gearbox Fault Detection and Diagnosis based on Continuous Wavelet Transformation of Time Synchronous Averaged Vibration Signals , 2012 .

[2]  Shaobo Han,et al.  Time domain averaging based on fractional delay filter , 2009 .

[3]  Giorgio Dalpiaz,et al.  Effectiveness and Sensitivity of Vibration Processing Techniques for Local Fault Detection in Gears , 2000 .

[4]  Patrick S. K. Chua,et al.  Adaptive wavelet transform for vibration signal modelling and application in fault diagnosis of water hydraulic motor , 2006 .

[5]  Barbara Hubbard,et al.  The World According to Wavelets , 1996 .

[6]  Wiesław J Staszewski,et al.  Time–frequency and time–scale analyses for structural health monitoring , 2007, Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences.

[7]  Ioannis Antoniadis,et al.  Demodulation of Vibration Signals Generated by Defects in Rolling Element Bearings Using Complex Shifted Morlet Wavelets , 2002 .

[8]  Khaled H. Hamed,et al.  Time-frequency analysis , 2003 .

[9]  Leonid M. Gelman,et al.  An automated methodology for performing time synchronous averaging of a gearbox signal without speed sensor , 2007 .

[10]  Joerg F. Hipp,et al.  Time-Frequency Analysis , 2014, Encyclopedia of Computational Neuroscience.

[11]  Yi Qin,et al.  Feature extraction method of wind turbine based on adaptive Morlet wavelet and SVD , 2011 .

[12]  Dennis P. Townsend,et al.  An Analysis of Gear Fault Detection Methods as Applied to Pitting Fatigue Failure Data , 1993 .

[13]  Ming J. Zuo,et al.  GEARBOX FAULT DIAGNOSIS USING ADAPTIVE WAVELET FILTER , 2003 .

[14]  Christos Yiakopoulos,et al.  Wavelet Based Demodulation of Vibration Signals Generated by Defects in Rolling Element Bearings , 2002 .

[15]  James D. Smith,et al.  Gears and Their Vibration, A Basic Approach to Understanding Gear Noise , 1983 .

[16]  John Debenham,et al.  Knowledge Engineering , 1998, Artificial Intelligence.

[17]  P. W. Stevens,et al.  A Multidisciplinary Research Approach to Rotorcraft Health and Usage Monitoring , 1996 .

[18]  Yan Li,et al.  Wind Turbine Gearbox Fault Diagnosis Using Adaptive Morlet Wavelet Spectrum , 2009, 2009 Second International Conference on Intelligent Computation Technology and Automation.