Application of Self-Adaptive Wavelet Ridge Demodulation Method Based on LCD to Incipient Fault Diagnosis

When a local defect occurs in gearbox, the vibration signals present as the form of multicomponent amplitude modulation and frequency modulation (AM-FM). Demodulation analysis is an effective way for this kind of signal. A self-adaptive wavelet ridge demodulation method based on LCD is proposed in this paper. Firstly, multicomponent AM-FM signal is decomposed into series of intrinsic scale components (ISCs) and the special intrinsic scale component is selected in order to decrease the lower frequency background noise. Secondly, the genetic algorithm is employed to optimize wavelet parameters according to the inherent characteristics of signal; thirdly, self-adaptive wavelet ridge demodulation wavelet for the selected ISC component is performed to get instantaneous amplitude (IA) or instantaneous frequency (IF). Lastly, the characteristics frequency can be obtained to identify the working state or failure information from its spectrum. By two simulation signals, the proposed method was compared with various existing demodulation methods; the simulation results show that it has higher accuracy and higher noise tolerant performance than others. Furthermore, the proposed method was applied to incipient fault diagnosis for gearbox and the results show that it is simple and effective.

[1]  Joshua R. Smith,et al.  The local mean decomposition and its application to EEG perception data , 2005, Journal of The Royal Society Interface.

[2]  Yi Qin.  Demodulation Approach Based on Wavelet Ridge and Its Application in Fault Diagnosis of Rotating Machinery , 2009 .

[3]  Yi Qin,et al.  Multicomponent AM–FM demodulation based on energy separation and adaptive filtering , 2013 .

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

[5]  Robert B. Randall,et al.  A New Method of Modeling Gear Faults , 1982 .

[6]  M. Liang,et al.  Intelligent bearing fault detection by enhanced energy operator , 2014, Expert Syst. Appl..

[7]  Mustafa Demetgul,et al.  Fault diagnosis of rolling bearings using a genetic algorithm optimized neural network , 2014 .

[8]  Wensheng Su,et al.  Rolling element bearing faults diagnosis based on optimal Morlet wavelet filter and autocorrelation enhancement , 2010 .

[9]  Wei He,et al.  A joint adaptive wavelet filter and morphological signal processing method for weak mechanical impulse extraction , 2010 .

[10]  Min Li,et al.  Improved diagnostics for the incipient faults in analog circuits using LSSVM based on PSO algorithm with Mahalanobis distance , 2014, Neurocomputing.

[11]  I. R. Praveen Krishna,et al.  Local fault detection in helical gears via vibration and acoustic signals using EMD based statistical parameter analysis , 2014 .

[12]  George E. P. Box,et al.  The Royal Society of London , 2013 .

[13]  Jun Wang,et al.  Exchanged ridge demodulation of time-scale manifold for enhanced fault diagnosis of rotating machinery , 2014 .

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

[15]  Minghong Han,et al.  A fault diagnosis method based on local mean decomposition and multi-scale entropy for roller bearings , 2014 .

[16]  Tung Khac Truong,et al.  A Roller Bearing Fault Diagnosis Method Based on LCD Energy Entropy and ACROA-SVM , 2014 .

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

[18]  Yang Yu,et al.  The application of energy operator demodulation approach based on EMD in machinery fault diagnosis , 2007 .

[19]  Zhipeng Feng,et al.  Fault diagnosis for wind turbine planetary gearboxes via demodulation analysis based on ensemble empirical mode decomposition and energy separation , 2012 .

[20]  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.

[21]  Michael Feldman,et al.  Nonparametric identification of asymmetric nonlinear vibration systems with the Hilbert transform , 2012 .

[22]  Jinde Zheng,et al.  A rolling bearing fault diagnosis approach based on LCD and fuzzy entropy , 2013 .

[23]  Yi Yang,et al.  A rotating machinery fault diagnosis method based on local mean decomposition , 2012, Digit. Signal Process..