Abstract：In order to accurately extract gear fault feature of planetary gearboxes from current signals, a fault diagnosis method was proposed based on improved complete ensemble empirical mode decomposition with adaptive noise (ICEEMDAN) and frequency demodulation analysis.Firstly, the complex multi-component induction motor current signal was adaptively decomposed into a series of intrinsic mode functions (IMF) by ICEEMDAN.Then, based on the characteristics of fault frequency modulating supply frequency and the principle of the instantaneous frequency fluctuation around supply frequency, the IMF containing fault information was preferably selected as the sensitive component.Finally, the planetary gearbox fault was diagnosed according to the frequency demodulated spectrum of the sensitive components.The gearbox experiment collected the induction motor current signal with sun, planet and ring partial fault, respectively.The analysis result shows that this method can not only reduce the influence of the noise in stator current, but can also extract the fault frequency feature in complex signals effectively.
管一臣，童攀，冯志鹏. 基于ICEEMDAN方法和频率解调的行星齿轮箱故障电流信号特征分析[J]. 振动与冲击, 2019, 38(24): 41-47.
GUAN Yichen,TONG Pan,FENG Zhipeng. Planetary gearbox fault diagnosis via current signature analysis based on ICEEMDAN and frequency demodulation. JOURNAL OF VIBRATION AND SHOCK, 2019, 38(24): 41-47.
[1] 饶振纲. 行星齿轮箱设计[M]. 北京：化学工业出版社，2007.
RAO Zhen-gang. Design of planetary gearbox[M]. Beijing: Chemical Industry Press, 2007.
[2] 冯志鹏，褚福磊，左明键. 行星齿轮箱振动故障诊断方法[M]. 北京：科学出版社，2015.
FENG Zhi-peng, CHU Fu-lei, ZUO Ming-jian. Method for vibration fault diagnosis of planetary gearboxes[M]. Beijing: Science Press, 2015.
[3] Kia S H, Henao H, Capolino G A. A comparative study of acoustic, vibration and stator current signatures for gear tooth fault diagnosis[C]//Electrical Machines (ICEM), 2012 XXth International Conference on. IEEE, 2012: 1514-1519.
[4] Bellini A, Filippetti F, Tassoni C, et al. Advances in diagnostic techniques for induction machines[J]. IEEE Transactions on industrial electronics, 2008, 55(12): 4109-4126.
[5] Rajagopalan S, Habetler T G, Harley R G, et al. Current/voltage-based detection of faults in gears coupled to electric motors[J]. IEEE Transactions on industry applications, 2006, 42(6): 1412-1420.
[6] 陈峙，王铁，谷丰收，等. 基于电机电流信号双频谱分析的齿轮传动故障诊断[J]. 机械工程学报，2012,48(21):84-90.
CHEN Zhi, WANG Tie, GU Feng-shou, et al. Gear transmission fault diagnosis based on the bispectrum analysis of induction motor current signatures[J]. Journal Mechanical Engineering, 2012, 48(21): 84-90.
[7] Huang N E, Shen Z, Long S R, et al. The empirical mode decomposition and the Hilbert spectrum for nonlinear and non-stationary time series analysis[C]//Proceedings of the Royal Society of London A: mathematical, physical and engineering sciences. The Royal Society, 1998, 454(1971): 903-995.
[8] Wu Z, Huang N E. Ensemble empirical mode decomposition: a noise-assisted data analysis method[J]. Advances in adaptive data analysis, 2009, 1(01): 1-41.
[9] Colominas M A, Schlotthauer G, Torres M E. Improved complete ensemble EMD: A suitable tool for biomedical signal processing[J]. Biomedical Signal Processing and Control, 2014, 14: 19-29..
[10] Blodt M, Chabert M, Regnier J, et al. Mechanical load fault detection in induction motors by stator current time-frequency analysis[J]. IEEE Transactions on Industry Applications, 2006, 42(6): 1454-1463.
[11] Kia S H, Henao H, Capolino G A. Analytical and experimental study of gearbox mechanical effect on the induction machine stator current signature[J]. IEEE Transactions on Industry Applications, 2009, 45(4): 1405-1415.
[12] Lu D, Qiao W, Gong X. Current-based gear fault detection for wind turbine gearboxes[J]. IEEE Transactions on Sustainable Energy, 2017, 8(4): 1453-1462.
