High-order synchrosqueezing wavelet transform and application to planetary gearbox fault diagnosis

Abstract The synchrosqueezing transform (SST) is a powerful tool for time-frequency analysis of signals with slowly varying instantaneous frequency (IF). However, the SST and its extensions provide poor time-frequency resolution for signals with wide frequency range and fast varying IF. In this paper, a new SST method called high-order synchrosqueezing wavelet transform is proposed to achieve a highly energy-concentrated time-frequency representation (TFR) for nonstationary signals with wide frequency range and fast varying IF. This method uses high-order group delay and chirp rate operators to obtain the accurate estimation of instantaneous frequency. The proposed method can effectively improve the energy concentration of the TFR and remain invertible simultaneously. The numerical simulations investigate the performance and noise robustness of the proposed method when analyzing a typical amplitude-modulated and frequency-modulated (AM-FM) multicomponent signal. Finally, the application of planetary gearbox fault diagnosis in the variable operating condition verifies the effectiveness of the proposed method.

[1]  Zhibin Zhao,et al.  Matching Synchrosqueezing Wavelet Transform and Application to Aeroengine Vibration Monitoring , 2017, IEEE Transactions on Instrumentation and Measurement.

[2]  Hau-Tieng Wu,et al.  The Synchrosqueezing algorithm for time-varying spectral analysis: Robustness properties and new paleoclimate applications , 2011, Signal Process..

[3]  Geir Kjetil Nilsen,et al.  Recursive Time-Frequency Reassignment , 2009, IEEE Transactions on Signal Processing.

[4]  Sergey Fomel,et al.  Seismic data analysis using local time‐frequency decomposition , 2013 .

[5]  Hau-Tieng Wu,et al.  Synchrosqueezing-Based Recovery of Instantaneous Frequency from Nonuniform Samples , 2010, SIAM J. Math. Anal..

[6]  Haizhao Yang,et al.  Synchrosqueezed wave packet transforms and diffeomorphism based spectral analysis for 1D general mode decompositions , 2013, 1311.4655.

[7]  Hau-Tieng Wu,et al.  ConceFT for Time-Varying Heart Rate Variability Analysis as a Measure of Noxious Stimulation During General Anesthesia , 2017, IEEE Transactions on Biomedical Engineering.

[8]  S. V. Narasimhan,et al.  Estimation of evolutionary spectrum based on short time Fourier transform and modified group delay , 2004, Signal Process..

[9]  Yaguo Lei,et al.  Instantaneous speed jitter detection via encoder signal and its application for the diagnosis of planetary gearbox , 2018 .

[10]  Guolin He,et al.  A novel order tracking method for wind turbine planetary gearbox vibration analysis based on discrete spectrum correction technique , 2016 .

[11]  Lexing Ying,et al.  Synchrosqueezed Curvelet Transform for Two-Dimensional Mode Decomposition , 2014, SIAM J. Math. Anal..

[12]  Patrick Flandrin,et al.  Improving the readability of time-frequency and time-scale representations by the reassignment method , 1995, IEEE Trans. Signal Process..

[13]  Haizhao Yang Statistical analysis of synchrosqueezed transforms , 2014, Applied and Computational Harmonic Analysis.

[14]  Sylvain Meignen,et al.  High-Order Synchrosqueezing Transform for Multicomponent Signals Analysis—With an Application to Gravitational-Wave Signal , 2017, IEEE Transactions on Signal Processing.

[15]  Sylvain Meignen,et al.  A New Algorithm for Multicomponent Signals Analysis Based on SynchroSqueezing: With an Application to Signal Sampling and Denoising , 2012, IEEE Transactions on Signal Processing.

[16]  Shibin Wang,et al.  Time-reassigned synchrosqueezing transform: The algorithm and its applications in mechanical signal processing , 2019, Mechanical Systems and Signal Processing.

[17]  Luigi Carassale,et al.  Wavelet-based identification of rotor blades in passage-through-resonance tests , 2018 .

[18]  Gang Yu,et al.  Synchroextracting Transform , 2017, IEEE Transactions on Industrial Electronics.

[19]  I. Daubechies,et al.  Synchrosqueezed wavelet transforms: An empirical mode decomposition-like tool , 2011 .

[20]  T. Oberlin,et al.  Theoretical analysis of the second-order synchrosqueezing transform , 2016, Applied and Computational Harmonic Analysis.

[21]  Jianzhong Zhang,et al.  Synchrosqueezing S-Transform and Its Application in Seismic Spectral Decomposition , 2016, IEEE Transactions on Geoscience and Remote Sensing.

[22]  Ming Liang,et al.  Time–frequency analysis based on Vold-Kalman filter and higher order energy separation for fault diagnosis of wind turbine planetary gearbox under nonstationary conditions , 2016 .

[23]  Sylvain Meignen,et al.  Time-Frequency Reassignment and Synchrosqueezing: An Overview , 2013, IEEE Signal Processing Magazine.

[24]  Yi Wang,et al.  ConceFT: concentration of frequency and time via a multitapered synchrosqueezed transform , 2015, Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences.

[25]  Danilo P. Mandic,et al.  A Class of Multivariate Denoising Algorithms Based on Synchrosqueezing , 2015, IEEE Transactions on Signal Processing.

[26]  Arun K. Samantaray,et al.  Rolling element bearing defect diagnosis under variable speed operation through angle synchronous averaging of wavelet de-noised estimate , 2016 .

[27]  Zhipeng Feng,et al.  Joint envelope and frequency order spectrum analysis based on iterative generalized demodulation for planetary gearbox fault diagnosis under nonstationary conditions , 2016 .

[28]  Shibin Wang,et al.  Matching synchrosqueezing transform: A useful tool for characterizing signals with fast varying instantaneous frequency and application to machine fault diagnosis , 2018 .

[29]  Peter V. E. McClintock,et al.  Extraction of instantaneous frequencies from ridges in time-frequency representations of signals , 2013, Signal Process..

[30]  LinLin Shen,et al.  A review on Gabor wavelets for face recognition , 2006, Pattern Analysis and Applications.

[31]  Sylvain Meignen,et al.  Second-Order Synchrosqueezing Transform or Invertible Reassignment? Towards Ideal Time-Frequency Representations , 2015, IEEE Transactions on Signal Processing.

[32]  Wenxian Yang,et al.  Wind Turbine Condition Monitoring Based on an Improved Spline-Kernelled Chirplet Transform , 2015, IEEE Transactions on Industrial Electronics.