Spectral negentropy based sidebands and demodulation analysis for planet bearing fault diagnosis

Abstract Planet bearing vibration signals are highly complex due to intricate kinematics (involving both revolution and spinning) and strong multiple modulations (including not only the fault induced amplitude modulation and frequency modulation, but also additional amplitude modulations due to load zone passing, time-varying vibration transfer path, and time-varying angle between the gear pair mesh lines of action and fault impact force vector), leading to difficulty in fault feature extraction. Rolling element bearing fault diagnosis essentially relies on detection of fault induced repetitive impulses carried by resonance vibration, but they are usually contaminated by noise and therefor are hard to be detected. This further adds complexity to planet bearing diagnostics. Spectral negentropy is able to reveal the frequency distribution of repetitive transients, thus providing an approach to identify the optimal frequency band of a filter for separating repetitive impulses. In this paper, we find the informative frequency band (including the center frequency and bandwidth) of bearing fault induced repetitive impulses using the spectral negentropy based infogram. In Fourier spectrum, we identify planet bearing faults according to sideband characteristics around the center frequency. For demodulation analysis, we filter out the sensitive component based on the informative frequency band revealed by the infogram. In amplitude demodulated spectrum (squared envelope spectrum) of the sensitive component, we diagnose planet bearing faults by matching the present peaks with the theoretical fault characteristic frequencies. We further decompose the sensitive component into mono-component intrinsic mode functions (IMFs) to estimate their instantaneous frequencies, and select a sensitive IMF with an instantaneous frequency fluctuating around the center frequency for frequency demodulation analysis. In the frequency demodulated spectrum (Fourier spectrum of instantaneous frequency) of selected IMF, we discern planet bearing fault reasons according to the present peaks. The proposed spectral negentropy infogram based spectrum and demodulation analysis method is illustrated via a numerical simulated signal analysis. Considering the unique load bearing feature of planet bearings, experimental validations under both no-load and loading conditions are done to verify the derived fault symptoms and the proposed method. The localized faults on outer race, rolling element and inner race are successfully diagnosed.

[1]  Dong Wang,et al.  Dynamic Bayesian wavelet transform: New methodology for extraction of repetitive transients , 2017 .

[2]  Ming J. Zuo,et al.  Vibration signal models for fault diagnosis of planetary gearboxes , 2012 .

[3]  Darryll J. Pines,et al.  Vibration separation methodology for planetary gear health monitoring , 2000, Smart Structures.

[4]  Ming J. Zuo,et al.  Amplitude and frequency demodulation analysis for fault diagnosis of planet bearings , 2016 .

[5]  Pe Whiteley,et al.  Detection of planet bearing faults in wind turbine gearboxes , 2012 .

[6]  William D. Mark.  Stationary transducer response to planetary-gear vibration excitation II: Effects of torque modulations , 2009 .

[7]  Yaguo Lei,et al.  Condition monitoring and fault diagnosis of planetary gearboxes: A review , 2014 .

[8]  Ahmet Kahraman,et al.  A dynamic model to predict modulation sidebands of a planetary gear set having manufacturing errors , 2010 .

[9]  Diego Cabrera,et al.  Extracting repetitive transients for rotating machinery diagnosis using multiscale clustered grey infogram , 2016 .

[10]  Ming J. Zuo,et al.  Feature selection for damage degree classification of planetary gearboxes using support vector machine , 2011 .

[11]  Darryll J. Pines,et al.  A review of vibration-based techniques for helicopter transmission diagnostics , 2005 .

[12]  Ming Liang,et al.  Spectral kurtosis for fault detection, diagnosis and prognostics of rotating machines: A review with applications , 2016 .

[13]  Yaguo Lei,et al.  Fault detection of planetary gearboxes using new diagnostic parameters , 2012 .

[14]  Zhipeng Feng,et al.  Iterative generalized synchrosqueezing transform for fault diagnosis of wind turbine planetary gearbox under nonstationary conditions , 2015 .

[15]  Dong Han,et al.  Planetary gearbox fault diagnosis using an adaptive stochastic resonance method , 2013 .

[16]  Darryll J. Pines,et al.  Constrained adaptive lifting and the CAL4 metric for helicopter transmission diagnostics , 2009 .

[17]  Ahmet Kahraman,et al.  A theoretical and experimental investigation of modulation sidebands of planetary gear sets , 2009 .

[18]  Dong Wang,et al.  An extension of the infograms to novel Bayesian inference for bearing fault feature identification , 2016 .

[19]  Ming J. Zuo,et al.  Vibration signal models for fault diagnosis of planet bearings , 2016 .

[20]  P. D. McFadden,et al.  A technique for calculating the time domain averages of the vibration of the individual planet gears and the sun gear in an epicyclic gearbox , 1991 .

[21]  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.

[22]  Xiaomin Zhao,et al.  Diagnosis of artificially created surface damage levels of planet gear teeth using ordinal ranking , 2013 .

[23]  Yaguo Lei,et al.  Health condition identification of multi-stage planetary gearboxes using a mRVM-based method , 2015 .

[24]  P. D. McFadden,et al.  Window functions for the calculation of the time domain averages of the vibration of the individual planet gears and sun gear in an epicyclic gearbox , 1994 .

[25]  Fakher Chaari,et al.  Influence of manufacturing errors on the dynamic behavior of planetary gears , 2006 .

[26]  Radoslaw Zimroz,et al.  A new feature for monitoring the condition of gearboxes in non-stationary operating conditions , 2009 .

[27]  Zhiqi Fan,et al.  A hybrid approach for fault diagnosis of planetary bearings using an internal vibration sensor , 2015 .

[28]  Jérôme Antoni,et al.  The infogram: Entropic evidence of the signature of repetitive transients , 2016 .

[29]  Fakher Chaari,et al.  Dynamic analysis of a planetary gear failure caused by tooth pitting and cracking , 2006 .

[30]  Radoslaw Zimroz,et al.  Vibration condition monitoring of planetary gearbox under varying external load , 2009 .

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

[32]  Robert B. Randall,et al.  Application of spectral kurtosis for detection of a tooth crack in the planetary gear of a wind turbine , 2009 .

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

[34]  William D. Mark,et al.  Stationary transducer response to planetary-gear vibration excitation with non-uniform planet loading , 2009 .

[35]  Hugh Hunt,et al.  Vibration Response of a Wind-Turbine Planetary Gear Set in the Presence of a Localized Planet Bearing Defect , 2011 .