Acoustic signal analysis of the resonance frequency region for planetary gearbox fault diagnosis based on high-order synchrosqueezing transform

ZHANG Zheng-wu; FENG Zhi-peng; CHEN Xiao-wang

doi:10.13374/j.issn2095-9389.2019.07.18.002

Volume 42 Issue 8

Aug. 2020

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Article Navigation > Chinese Journal of Engineering > 2020 > 42(8): 1048-1054

ZHANG Zheng-wu, FENG Zhi-peng, CHEN Xiao-wang. Acoustic signal analysis of the resonance frequency region for planetary gearbox fault diagnosis based on high-order synchrosqueezing transform[J]. Chinese Journal of Engineering, 2020, 42(8): 1048-1054. doi: 10.13374/j.issn2095-9389.2019.07.18.002

Citation:

ZHANG Zheng-wu, FENG Zhi-peng, CHEN Xiao-wang. Acoustic signal analysis of the resonance frequency region for planetary gearbox fault diagnosis based on high-order synchrosqueezing transform[J]. Chinese Journal of Engineering, 2020, 42(8): 1048-1054. doi: 10.13374/j.issn2095-9389.2019.07.18.002

Citation:

ZHANG Zheng-wu, FENG Zhi-peng, CHEN Xiao-wang. Acoustic signal analysis of the resonance frequency region for planetary gearbox fault diagnosis based on high-order synchrosqueezing transform[J]. Chinese Journal of Engineering, 2020, 42(8): 1048-1054. doi: 10.13374/j.issn2095-9389.2019.07.18.002

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Acoustic signal analysis of the resonance frequency region for planetary gearbox fault diagnosis based on high-order synchrosqueezing transform

doi: 10.13374/j.issn2095-9389.2019.07.18.002

School of Mechanical Engineering, University of Science and Technology Beijing, Beijing 100083, China

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Corresponding author: E-mail: chenxw@ustb.edu.cn
Received Date: 2019-11-19
Publish Date: 2020-09-11

Abstract

Abstract

Planetary gearboxes have one or several planet gears rotating around the sun gear while revolving along their axle. This unique gear structure results in the simultaneous meshing of the planet gear with both sun and ring gears. Because of the high transmission ratio and large bearing capacity of its compact structure, planetary gearboxes have been extensively used in a variety of industrial applications. Therefore, planetary gearbox fault diagnosis is essential to ensure safe and efficient industrial manufacturing. Acoustic signal analysis provides an effective and noninvasive method for detecting potential faults in the planetary gearbox. However, the theoretical foundation of planetary gearbox fault signatures in acoustic signals is ambiguous. In this work, the planetary gearbox acoustic signal model of the resonance frequency region under the nonstationary state is structured by amplitude and frequency modulation, and the gear fault characteristics of the acoustic signals are explicitly derived. Given that resonance frequency is independent of rotational speed, the resonance frequency can be distinguished from speed-related frequency components. This lays the foundation for extracting the gear fault characteristics of the resonance frequency region. Moreover, the planetary gearbox often runs under time-varying speed conditions, and the fault frequency components are time-varying. To overcome the limitations of the traditional time–frequency analysis method in limited time–frequency resolution or cross-term interferences, the appropriate time–frequency analysis method is essential. In this work, the high-order synchrosqueezing transform is exploited to identify the time-varying fault characteristics of the planetary gearbox acoustic signal. Owing to the step of squeezing the energy distributed along instantaneous frequency in frequency direction, time–frequency representation by synchrosqueezing transform achieves a high time–frequency resolution. The high-order interpretation of instantaneous frequency further improves the capability to capture the time–frequency details. The acoustic signal model and corresponding fault characteristics of the planetary gearbox in the resonance frequency region are verified by both numerical simulations and laboratory experiments. The gear defect within the planetary gearbox is successfully diagnosed via the high-order synchrosqueezing transform.
- resonance,
- synchrosqueezing transform,
- nonstationary,
- planetary gearbox,
- fault diagnosis

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References(25)

References

[1]	張建宇, 吳帥剛, 黃勝軍, 等. 單齒裂紋行星輪系合成剛度的劣化特性. 北京工業大學學報, 2018, 44(2):170 Zhang J Y, Wu S G, Huang S J, et al. Synthetic stiffness degradation behavior of planetary gear set with crack on single tooth. <italic>J Beijing Univ Technol</italic>, 2018, 44(2): 170
[2]	張東, 馮志鵬. 迭代廣義短時Fourier變換在行星齒輪箱故障診斷中的應用. 工程科學學報, 2017, 39(4):604 Zhang D, Feng Z P. Application of iterative generalized short-time Fourier transform to fault diagnosis of planetary gearboxes. <italic>Chin J Eng</italic>, 2017, 39(4): 604
[3]	趙川, 馮志鵬. 基于多域流形的行星齒輪箱局部故障識別. 工程科學學報, 2017, 39(5):769 Zhao C, Feng Z P. Localized fault identification of planetary gearboxes based on multiple-domain manifold. <italic>Chin J Eng</italic>, 2017, 39(5): 769
[4]	陳小旺, 馮志鵬, Liang Ming. 基于迭代廣義同步壓縮變換的時變工況行星齒輪箱故障診斷. 機械工程學報, 2015, 51(1):131 doi: 10.3901/JME.2015.01.131 Chen X W, Feng Z P, Liang M. Planetary gearbox fault diagnosis under time-variant conditions based on iterative generalized synchrosqueezing transform. <italic>J Mech Eng</italic>, 2015, 51(1): 131 doi: 10.3901/JME.2015.01.131
[5]	楊通強, 唐力偉, 鄭海起, 等. 基于聲測法的齒輪箱齒輪故障診斷研究. 軍械工程學院學報, 2000, 12(4):24 Yang T Q, Tang L W, Zheng H Q, et al. Research on diagnosing gear fault of gearbox by acoustic method. <italic>J Ordnance Eng College</italic>, 2000, 12(4): 24
[6]	Lin J. Feature extraction of machine sound using wavelet and its application in fault diagnosis. <italic>NDT </italic>&<italic>E Int</italic>, 2001, 34(1): 25
[7]	Rezaei A, Dadouche A, Wickramasinghe V, et al. A comparison study between acoustic sensors for bearing fault detection under different speed and load using a variety of signal processing techniques. <italic>Tribol Trans</italic>, 2011, 54(2): 179 doi: 10.1080/10402004.2010.533817
[8]	Metwalley S M, Hammad N, Abouel-Seoud S A. Vehicle gearbox fault diagnosis using noise measurements. <italic>Int J Energy Environ</italic>, 2011, 2(2): 357
[9]	Baydar N, Ball A. A comparative study of acoustic and vibration signals in detection of gear failures using Wigner–Ville distribution. <italic>Mech Syst Signal Process</italic>, 2001, 15(6): 1091 doi: 10.1006/mssp.2000.1338
[10]	Feng Z P, Chen X W, Liang M, et al. Time-frequency demodulation analysis based on iterative generalized demodulation for fault diagnosis of planetary gearbox under nonstationary conditions. <italic>Mech Syst Signal Process</italic>, 2015, 62-63: 54 doi: 10.1016/j.ymssp.2015.03.014
[11]	Chen X W, Feng Z P. Application of reassigned wavelet scalogram in wind turbine planetary gearbox fault diagnosis under nonstationary conditions. <italic>Shock Vib</italic>, 2016: 6723267
[12]	Chen X W, Feng Z P. Time-frequency analysis of torsional vibration signals in resonance region for planetary gearbox fault diagnosis under variable speed conditions. <italic>IEEE Access</italic>, 2017, 5: 21918 doi: 10.1109/ACCESS.2017.2763172
[13]	Wang W Y. Early detection of gear tooth cracking using the resonance demodulation technique. <italic>Mech Syst Signal Process</italic>, 2001, 15(5): 887 doi: 10.1006/mssp.2001.1416
[14]	Feng Z P, Liang M, Chu F L. Recent advances in time-frequency analysis methods for machinery fault diagnosis: a review with application examples. <italic>Mech Syst Signal Process</italic>, 2013, 38(1): 165 doi: 10.1016/j.ymssp.2013.01.017
[15]	Hlawatsch F, Auger F. Time-Frequency Analysis: Concepts and Methods. Hoboken: John Wiley & Sons, 2008
[16]	Feng Z P, Chen X W Liang M. Iterative generalized synchrosqueezing transform for fault diagnosis of wind turbine planetary gearbox under nonstationary conditions. <italic>Mech Syst Signal Process</italic>, 2015, 52-53: 360 doi: 10.1016/j.ymssp.2014.07.009
[17]	Martin W, Flandrin P. Wigner–Ville spectral analysis of nonstationary processes. <italic>IEEE Trans Acoust Speech Signal Process</italic>, 1985, 33(6): 1461 doi: 10.1109/TASSP.1985.1164760
[18]	Auger F, Flandrin P. Improving the readability of time-frequency and time-scale representations by the reassignment method. <italic>IEEE Trans Signal Process</italic>, 1995, 43(5): 1068 doi: 10.1109/78.382394
[19]	Daubechies I, Lu J F, Wu H T. Synchrosqueezed wavelet transforms: An empirical mode decomposition-like tool. <italic>Appl Comput Harmon Anal</italic>, 2011, 30(2): 243 doi: 10.1016/j.acha.2010.08.002
[20]	Yang H Z. Statistical analysis of synchrosqueezed transforms. <italic>Appl Comput Harmon Anal</italic>, 2018, 45(3): 526 doi: 10.1016/j.acha.2017.01.001
[21]	Thakur G, Brevdo E, Fuckar N S, et al. The Synchrosqueezing algorithm for time-varying spectral analysis: robustness properties and new paleoclimate applications. <italic>Signal Process</italic>, 2013, 93(5): 1079 doi: 10.1016/j.sigpro.2012.11.029
[22]	Yang H Z. Robustness analysis of synchrosqueezed transforms. <italic>Statistics</italic>, 2014: 1
[23]	Latsenko D, McClintock P V E, Stefanovska A. Extraction of instantaneous frequencies from ridges in time–frequency representations of signals. <italic>Signal Process</italic>, 2016, 125: 290 doi: 10.1016/j.sigpro.2016.01.024
[24]	Pham D H, Meignen S. High-order synchrosqueezing transform for multicomponent signals analysis—with an application to gravitational-wave signal. <italic>IEEE Trans Signal Process</italic>, 2017, 65(12): 3168 doi: 10.1109/TSP.2017.2686355
[25]	Oberlin T, Meignen S, Perrier V. Second-order synchrosqueezing transform or invertible reassignment? towards ideal time-frequency representations. <italic>IEEE Trans Signal Process</italic>, 2015, 63(5): 1335 doi: 10.1109/TSP.2015.2391077