A dual path optimization ridge estimation method for condition monitoring of planetary gearbox under varying-speed operation

Abstract Tacholess order tracking method is an effective tool for condition monitoring of planetary gearbox under varying-speed operation to reduce the measurement cost and avoid the inconvenience in the installation and adjustment. The robust ridge estimation is an indispensable procedure in the tacholess order tracking. However, conventional ridge estimation methods are difficult to extract the targeted ridge from the highly nonstationary signals. In this paper, we propose a novel method, termed dual path optimization ridge estimation (DPORE), which implants the idea of local integration to adapt with the intensively oscillated ridge. An in-situ vibration signal collected from a wind turbine planetary gearbox provided by the contest during the conference CMMNO December 2014 is employed to validate the proposed method. As a result, an important time-frequency ridge with weak energy and intricate shape is successfully extracted by the proposed method, while two popular ridge estimation methods cannot well track the complete time-frequency ridge. Moreover, the results obtained by the proposed method outperform those provided by the contestants in the conference contest. Finally, the accuracy of estimated ridge is further demonstrated by order analysis and one of the planet bearings in planetary gearbox with an inner race defect can be detected based on the order spectrum.

[1]  Fanrang Kong,et al.  A new synthetic detection technique for trackside acoustic identification of railroad roller bearing defects , 2014 .

[2]  Jin Jiang,et al.  Time-frequency feature representation using energy concentration: An overview of recent advances , 2009, Digit. Signal Process..

[3]  Bruno Torrésani,et al.  Characterization of signals by the ridges of their wavelet transforms , 1997, IEEE Trans. Signal Process..

[4]  Simon Haykin,et al.  Adaptive chirplet transform: an adaptive generalization of the wavelet transform , 1992 .

[5]  Yi Qin,et al.  Adaptive signal decomposition based on wavelet ridge and its application , 2016, Signal Process..

[6]  Chuan Li,et al.  Bearing fault diagnosis under unknown variable speed via gear noise cancellation and rotational order sideband identification , 2015 .

[7]  Mengyan Nie,et al.  Review of condition monitoring and fault diagnosis technologies for wind turbine gearbox , 2013 .

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

[9]  Marianne Mosher Understanding Vibration Spectra of Planetary Gear Systems for Fault Detection , 2003 .

[10]  Jing Lin,et al.  Feature Extraction Based on Morlet Wavelet and its Application for Mechanical Fault Diagnosis , 2000 .

[11]  Yaguo Lei,et al.  Tacholess Envelope Order Analysis and Its Application to Fault Detection of Rolling Element Bearings with Varying Speeds , 2013, Sensors.

[12]  Guoyu Meng,et al.  Time–frequency data fusion technique with application to vibration signal analysis , 2012 .

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

[14]  Robert B. Randall,et al.  Single and multi-stage phase demodulation based order-tracking , 2014 .

[15]  James McNames,et al.  Fourier Series Analysis of Epicyclic Gearbox Vibration , 2002 .

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

[17]  Yaguo Lei,et al.  A tacho-less order tracking technique for large speed variations , 2013 .

[18]  Peter V. E. McClintock,et al.  Linear and synchrosqueezed time-frequency representations revisited: Overview, standards of use, resolution, reconstruction, concentration, and algorithms , 2015, Digit. Signal Process..

[19]  Radoslaw Zimroz,et al.  Periodic Autoregressive Modeling of Vibration Time Series From Planetary Gearbox Used in Bucket Wheel Excavator , 2014 .

[20]  Fanrang Kong,et al.  Wayside acoustic diagnosis of defective train bearings based on signal resampling and information enhancement , 2013 .

[21]  Quentin Leclere,et al.  A multi-order probabilistic approach for Instantaneous Angular Speed tracking debriefing of the CMMNO׳14 diagnosis contest , 2016 .

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

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

[24]  David R. Burton,et al.  Ridge extraction algorithms for one-dimensional continuous wavelet transform: a comparison , 2007 .

[25]  José Ramón Beltrán Blázquez,et al.  Estimation of the instantaneous amplitude and the instantaneous frequency of audio signals using complex wavelets , 2010, Signal Process..

[26]  Dmytro Iatsenko,et al.  Nonlinear Mode Decomposition: Theory and Applications , 2015 .

[27]  Cemal Basaran,et al.  Moiré interferogram phase extraction: a ridge detection algorithm for continuous wavelet transforms. , 2004, Applied optics.

[28]  Baoping Tang,et al.  Fault diagnosis of rolling element bearing based on S transform and gray level co-occurrence matrix , 2015, Measurement Science and Technology.

[29]  Robert B. Randall,et al.  Gear parameter identification in a wind turbine gearbox using vibration signals , 2014 .

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

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

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

[33]  Guoyu Meng,et al.  Vibration signal analysis using parameterized time–frequency method for features extraction of varying-speed rotary machinery , 2015 .

[34]  Gaigai Cai,et al.  Nonlinear squeezing time-frequency transform for weak signal detection , 2015, Signal Process..

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

[36]  Michael Feldman,et al.  Time-varying vibration decomposition and analysis based on the Hilbert transform , 2006 .

[37]  Guang Meng,et al.  Characterize highly oscillating frequency modulation using generalized Warblet transform , 2012 .

[38]  Gaigai Cai,et al.  Matching Demodulation Transform and SynchroSqueezing in Time-Frequency Analysis , 2014, IEEE Transactions on Signal Processing.

[39]  Gang Yu,et al.  General linear chirplet transform , 2016 .

[40]  W. M. Zhang,et al.  Polynomial Chirplet Transform With Application to Instantaneous Frequency Estimation , 2011, IEEE Transactions on Instrumentation and Measurement.

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

[42]  Bruno Torrésani,et al.  Multiridge detection and time-frequency reconstruction , 1999, IEEE Trans. Signal Process..

[43]  Yanyang Zi,et al.  Multiwavelet construction via an adaptive symmetric lifting scheme and its applications for rotating machinery fault diagnosis , 2009 .

[44]  Robert X. Gao,et al.  Wavelets for fault diagnosis of rotary machines: A review with applications , 2014, Signal Process..

[45]  Tomasz Barszcz,et al.  Application of Vibration Monitoring for Mining Machinery in Varying Operational Conditions , 2012 .

[46]  Darryll J. Pines,et al.  A Comparison of Stationary and Non-Stationary Metrics for Detecting Faults in Helicopter Gearboxes , 2000 .

[47]  Richard Kronland-Martinet,et al.  Asymptotic wavelet and Gabor analysis: Extraction of instantaneous frequencies , 1992, IEEE Trans. Inf. Theory.

[48]  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 .