Multivariate multiscale fuzzy entropy based planetary gearbox fault diagnosis

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Journal of Vibration and Shock ›› 2019, Vol. 38 ›› Issue (6) : 187-193.

ZHENG Jinde1,PAN Haiyang1,ZHANG Jun2,LIU Tao1,LIU Qingyun1

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Abstract

Planetary gearbox has been widely used in wind power, helicopter, construction machinery and other large complex equipments. The vibration signals of a planetary gearbox are often nonlinear and nonstationary when the gearbox works with failure. The multiscale entropy theory has been widely used to measure the complexity of mechanical vibration signals. Meanwhile, the multivariate multiscale entropy (MMSE) that evaluates the multivariate complexity of synchronous multi-channel data is introduced to the fault diagnosis of planetary gearbox for using the multi-channel vibration information for improving the efficiency of fault diagnosis as much as possible. Aiming at the poor statistical stability of MMSE, the multivariate multiscale fuzzy entropy (MMFE) was proposed and based on this, a new fault diagnosis method for planetary gearboxes was proposed. Finally, the proposed method was applied to the experimental data analysis of a practical planetary gearbox and the results show its effectiveness and superiority.

Key words

multiscale fuzzy entropy / multivariate multiscale fuzzy entropy / planetary gearbox / fault diagnosis

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ZHENG Jinde1,PAN Haiyang1,ZHANG Jun2,LIU Tao1,LIU Qingyun1. Multivariate multiscale fuzzy entropy based planetary gearbox fault diagnosis[J]. Journal of Vibration and Shock, 2019, 38(6): 187-193

References

[1] 李鹏, 刘澄玉, 李丽萍,等. 多尺度多变量模糊熵分析[J]. 物理学报, 2013, 62(12): 120512（1-9）.
LI Peng, LIU Cheng-Yu, LI Li-Ping. Multiscale multivariate fuzzy entropy analysis[J]. Acta Phys. Sin.2013, 62(12):120512(1-9).
[2] Yan R, Gao R X. Approximate Entropy as a diagnostic tool for machine health monitoring[J]. Mechanical Systems and Signal Processing, 2007, 21(2): 824-839.
[3] Lake D E, Richman J S, Griffin M P, et al. Sample entropy analysis of neonatal heart rate variability[J]. American Journal of Physiology Regulatory Integrative and Comparative Physiology, 2002, 283(3): R789-R797.
[4] Zhang L, Xiong G, Liu H, et al. Bearing fault diagnosis using multi-scale entropy and adaptive neuro-fuzzy inference[J]. Expert Systems with Applications, 2010, 37(8):6077–6085.
[5] 郑近德, 程军圣, 杨宇. 基于多尺度熵的滚动轴承故障诊断方法[J]. 湖南大学学报:自然科学版, 2012, 39(5):38-41.
ZHENG Jin-de, CHENG Jun-sheng, YANG Yu. A Rolling Bearing Fault Diagnosis Approach Based on Multiscale Entro-py[J]. Journal of Hunan University (Natural Sciences), 2012, 39(5):38-41.
[6] 郑近德, 程军圣, 胡思宇. 多尺度熵在转子故障诊断中的应用[J]. 振动、测试与诊断, 2013, 33(2):294-297.
ZHENG Jin-de, CHENG Jun-sheng, HU Siyu. Multiscale Entropy Based Rotor Fault Diagnosis[J]. Journal of Vibration Measurement & Diagnosis, 2013, 33(2):294-297.
[7] 郑近德, 陈敏均, 程军圣,等. 多尺度模糊熵及其在滚动轴承故障诊断中的应用[J]. 振动工程学报, 2014, 27(1):145-151.
ZHENG Jin-de, CHEN Min-jun, CHENG Jun-sheng, etal. Multiscale fuzzy entropy and its application in rolling bearing fault diagnosiss [J]. Journal of Vibration Engineering, 2014, 27(1):145-151.
[8] Ahmed M U, Mandic D P. Multivariate multiscale entropy: A tool for complexity analysis of multichannel data[J]. Physical Review E, 2011, 84(1): 061918(1-10).
[9] Ahmed M U, Mandic D P. Multivariate Multiscale Entropy Analysis[J]. Signal Processing Letters IEEE, 2012, 19(2):91-94.
[10] Wei Q, Liu D H, Wang K H, et al. Multivariate Multiscale Entropy Applied to Center of Pressure Signals Analysis: An Effect of Vibration Stimulation of Shoes [J]. Entropy, 2012, 14(11):2157-2172.
[11] Ahmed M U, Rehman N, Looney D, et al. Dynamical complexity of human responses: a multivariate data-adaptive frame-work [J]. Bulletin of the Polish Academy of Sciences Technical Sciences, 2012, 60(3):433-445.
[12] Li P, Liu C, Wang X, et al. Testing pattern synchronization in coupled systems through different entropy-based measures[J]. Medical and Biological Engineering and Computing, 2013, 51(5):581-591.
[13] 张小龙, 张氢, 秦仙蓉,等. 基于 ITD 复杂度和 PSO-SVM的滚动轴承故障诊断[J]. 振动与冲击, 2016, 35(24):102-107.
ZHANG Xiao-long ZHANG Qing QIN Xian-rong, etal. Rolling bearing fault diagnosis based on ITD Lempel-Ziv com-plexity and PSO-SVM [J]. Journal of Vibration and Shock, 2016, 35(24):102-107.
[14] 姜战伟，郑近德，潘海洋，潘紫微. 基于多尺度时不可逆与t-SNE流形学习的滚动轴承故障诊断[J]. 振动与冲击, 2017, 36(17): 61-68.
JIANG Zhan-wei, ZHENG Jin-de, PAN Hai-yang, etal. Rolling bearing fault diagnosis method based on multiscale time ir-reversibility and t-SNE manifold learning. Journal of Vibration and Shock, 2017, 36(17): 61-68.
[15] Costa M. Multiscale entropy analysis of complex physiologic time series [J]. Physical Review Letters, 2002, 89(6):705-708.
[16] Zheng J, Cheng J, Yang Y, et al. A rolling bearing fault diagnosis method based on multi-scale fuzzy entropy and variable predictive model-based class discrimination [J]. Mechanism and Machine Theory, 2014, 78(16):187-200.