基于NeighCoeff和Hilbert包络分析的行星齿轮箱太阳轮故障诊断

PDF(3049 KB)

振动与冲击 ›› 2018, Vol. 37 ›› Issue (22) : 151-157.

论文

程宝安 1,2，庞新宇 1,2，杨兆建 1,2，邵杰 1,2

作者信息 +

Fault diagnosis of the sun wheel of planetary gearboes based on the NeighCoeff and Hilbert envelope analysis

CHENG Baoan1,2,PANG Xinyu1,2,YANG Zhaojian1,2,SHAO Jie1,2

Author information +

文章历史 +

摘要

针对行星齿轮箱的振动信号易受噪声干扰、信号成分复杂以及太阳轮易发生故障的问题，提出了基于小波相邻系数（NeighCoeff）和Hilbert包络相结合的行星齿轮箱太阳轮故障诊断方法。建立了太阳轮局部故障的振动信号仿真模型，模拟噪声干扰下的太阳轮故障，利用NeighCoeff对含噪信号降噪，对降噪后的信号进行Hilbert包络解调处理。仿真结果可得到更为清晰的频谱，证明该方法能够有效提取受噪声影响的故障特征频率。将该方法应用到行星齿轮箱太阳轮的故障诊断实验中，通过对太阳轮正常、断齿和磨损三种故障下振动信号进行实验，准确识别太阳轮故障，结果表明应用该方法能够有效地提高太阳轮故障诊断的准确性。

Abstract

Aiming at the problem that the vibration signals of planetary gear boes are susceptible to noise interference, signal components complication and sun wheel troubles, a fault diagnosis method for the planetary gearbox sun gears based on the Neighborhood Coefficient (NeighCoeff) and Hilbert envelope was proposed.A vibration signal simulation model for sun gear local faults under noise disturbance was established.The NeighCoeff was used to denoise the noisy signal, and the signal was reconstructed by the Hilbert envelope.The simulation results provide a clearer spectrum, which proves that the method can effectively extract the fault characteristic frequency affected by noise.The method was applied to the fault diagnosis experiments of a practical planetary gearbox sun gear.The vibration signals of the sun gear processed by the method can classify clearly the three common working states: normal, broken teeth and wear and the faults of the sun gear can be accurately identified.The results show that the method can effectively improve the accuracy of fault diagnosis of sun gears.

导出引用

程宝安 1,2，庞新宇 1,2，杨兆建 1,2，邵杰 1,2. 基于NeighCoeff和Hilbert包络分析的行星齿轮箱太阳轮故障诊断[J]. 振动与冲击, 2018, 37(22): 151-157

CHENG Baoan1,2,PANG Xinyu1,2,YANG Zhaojian1,2,SHAO Jie1,2 . Fault diagnosis of the sun wheel of planetary gearboes based on the NeighCoeff and Hilbert envelope analysis[J]. Journal of Vibration and Shock, 2018, 37(22): 151-157

参考文献

[1] 李海平, 赵建民, 宋文渊,等. 基于EMD-EDT的行星齿轮箱特征提取及状态识别方法研究[J]. 振动与冲击, 2016, 35(3): 48-54.
LI Hai-ping, ZHAO Jian-min, SONG Wen-yuan. Method of planetary gearbox feature extraction and condition recognition based on EMD and EDT[J]. Journal of Vibration and Shock, 2016, 35(3): 48-54.
[2] 冯志鹏, 赵镭镭, 褚福磊. 行星齿轮箱齿轮局部故障振动频谱特征[J]. 中国电机工程学报, 2013, 33(5): 118-125.
FENG Zhi-peng, ZHAO Lei-lei, CHU Fu-lei. Vibration spectral characteristics of localized gear fault of planetary gearboxes[J]. Proceeding of the CSEE, 2013, 33(5): 118-125.
[3] 冯志鹏, 秦嗣峰. 基于Hilbert振动分解和高阶能量算子的行星齿轮箱故障诊断研究[J]. 振动与冲击, 2016, 35(5): 47-54.
FENG Zhi-peng, QIN Si-feng. Planetary gearbox fault diagnosis based on Hilbert vibration decomposition and higher order differential energy operator[J]. Journal of Vibration and Shock, 2016, 35(5): 47-54.
[4] 梁晓玉, 徐玉秀, 邢钢,等. 行星齿轮传动系统复杂微弱故障的非线性特性[J]. 机械科学与技术, 2015, 34(4): 538-543.
LIANG Xiao-yu, XU Yu-xiu, XING Gang, et al. Nonlinear characteristics of complex and weak faults of planetary gear transmission system[J]. Mechanical science and technology for aerospace engineering, 2015, 34(4): 538-543.
[5] Barszcz T, Randall R B. Application of spectral kurtosis for detection of a tooth crack in the planetary gear of a wind turbine[J]. Mechanical Systems and Signal Processing, 2009, 23(4): 1352-1365.
[6] 桂勇, 韩勤锴, 李峥, 等. 变速行星齿轮系统故障诊断方法[J]. 振动、测试与诊断, 2016, 36(2): 220-226.
Gui Yong, Han Qin-kai, Li Zheng, et al. Fault diagnosis of planetary gear system under time-varying speed conditions[J]. Journal of vibration, measurement and diagnosis, 2016, 36(2): 220-226.
[7] 毕静伟, 潘宏侠, 葛航奇, 等. 基于频率切片小波变换的行星齿轮箱故障诊断[J]. 机械设计与制造, 2016， 1(1): 29-32.
BI Jing-wei, PAN Hong-xia, GE Hang-qi, et al. Fault diagnosis of planetary gearbox based o wavelet transform of frequency slice[J]. Machinery design and manufacture, 2016, 1(1): 29-32.
[8] 祝文颖, 冯志鹏. 基于改进经验小波变换的行星齿轮箱故障诊断[J]. 仪器仪表学报, 2016, 37(10): 2193-2201.
ZHU Wen-ying, FENG Zhi-peng. Fault diagnosis of planetary gearbox based on improved empirical wavelet transform[J]. Chinese journal of scientific instrument, 2016, 37(10): 2193-2201.
[9] DONOHO D J, JOHNSTONE I M. Adapting to unknown smoothness via wavelet shrinkage[J]. Journal of the
American Statistical Association，1995，90(432): 1200-1224.
[10] NASON G P. Wavelet shrinkage using cross-validation[J]. Journal of the Royal Statistical Society，2010，46(2): 28-33.
[11] Cai T T, Silverman B W. Incorporating Information on Neighbouring Coefficients into Wavelet Estimation[J]. Sankhya Series B, 2001, 63(2): 127-148.
[12] 袁静, 何正嘉, 王晓东,等. 平移不变多小波相邻系数降噪方法及其在监测诊断中的应用[J]. 机械工程学报, 2009, 45(4): 155-160.
YUAN Jing, HE Zhengjia, WANG Xiaodong, et al. Translation-invariant multiwavelets denoising using neighboring coefficients and its application to monitoring and diagnosis[J]. Journal of Mechanical Engineering, 2009, 45(4): 155-160.
[13] Wang X, Zi Y, He Z. Multiwavelet denoising with improved neighboring coefficients for application on rolling bearing fault diagnosis[J]. Mechanical Systems and Signal Processing, 2011, 25(1): 285-304.
[14] 王洪超, 郭志强, 向国权, 等. 基于小波相邻系数降噪的滚动轴承早期微弱故障时频特征提取[J]. 航空动力学报, 2017, 05(32): 1266-1272.
WANG Hong-chao, GUO Zhi-qiang, XIANG Zhi-quan, et al. Time-frequency feature extraction of rolling hearing’s early weak fault based on wavelet de-noising using neighboring coefficients[J].Journal of aerospace power, 2017, 05(32): 1266-1272.
[15] 杨绍普, 赵志宏. 改进的小波相邻系数降噪方法及其在机械故障诊断中的应用[J]. 机械工程学报, 2013, 49(17): 137-141.
YANG Shao-pu, ZHAO Zhi-hong. Improved wavelet denoising using neighboring coefficients and its application to machinery fault diagnosis[J]. Journal of Mechanical Engineering, 2013, 49(17): 137-141.
[16] Feng Z, Liang M. Complex signal analysis for wind turbine planetary gearbox fault diagnosis via iterative atomic decomposition thresholding[J]. Journal of Sound & Vibration, 2014, 333(20): 5196-5211.