基于GWO-TVF-EMD方法的行星齿轮箱齿面剥落故障诊断

曹蔚; 苟臻元; 韩昭; 郭雅泓; 杨壮壮; 王栋; 瞿金秀

doi:10.13224/j.cnki.jasp.20210226

基于GWO-TVF-EMD方法的行星齿轮箱齿面剥落故障诊断

doi: 10.13224/j.cnki.jasp.20210226

1.
西安工业大学机电工程学院，西安 710021
2.
陕西水利电力勘测设计研究院水电工程分院，西安 710001

基金项目: 国家自然科学基金（52175113，51505360，51905406）；陕西省国际科技合作计划项目（2020KW-014）；陕西省教育厅重点实验室项目（18JS044）

详细信息

作者简介:
曹蔚（1977-），女，副教授、硕士生导师，博士，主要研究方向为机械振动与故障诊断、精密机械设计技术与产品开发、摩擦学系统工程等。

通讯作者:
韩昭（1996-），男，硕士生，主要研究方向为机械振动与故障诊断、动力学建模等。E-mail： hanzhao1996@163.com

中图分类号: V263.6;TH132.46
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出版历程
- 收稿日期: 2021-05-10
- 刊出日期: 2021-11-28

Fault diagnosis of tooth surface spalling of planetary gearbox based on GWO-TVF-EMD method

1.
School of Mechatronic Engineering，Xi'an Technological University，Xi'an 710021，China
2.
Hydroelectric Engineering Branch，Shaanxi Province Institute of Water Resources and Electric Power Investigation and Design，Xi'an 710001，China

摘要

摘要: 针对时变滤波经验模态分解（TVF-EMD）方法的不足之处，将样本熵作为适应度函数，采用灰狼优化（GWO）算法对带宽阈值和B样条阶数核心参数进行寻优，得到最优组合解，对不同的故障冲击试验振动信号进行分解。对本征模态函数（IMF）分量选取过程进行优化，采用多个加权指标对所有IMF分量进行计算，最终选取最优IMF分量，再通过包络谱分析提取出行星轮齿面剥落故障特征。在行星齿轮箱故障试验中，利用方均根法对剥落故障进行初步识别，根据GWO-TVF-EMD法分解得到各剥落故障信号最优IMF分量，使用包络谱分析明显判断出行星齿轮的故障频率。该方法能够提取3种不同程度齿面剥落故障的细节特征，理论值与实际值的相对误差为1.68%。
- 行星齿轮箱 /
- 剥落 /
- 灰狼优化算法 /
- 时变滤波经验模态分解 /
- 特征提取
Abstract: In order to overcome the shortcomings of the time varying filter for empirical mode decomposition （TVF-EMD） method，the sample entropy was used as the fitness function.The gray wolf optimization （GWO） algorithm was used to optimize the bandwidth threshold and B-spline order core parameters，and the optimal combination solution was obtained to decompose the vibration signals of different fault impact tests.The selection process of intrinsic mode function （IMF） components was optimized，and multiple weighted indexes were used to calculate all the IMF components，and finally the optimal IMF component was selected.Then，the spalling fault features of planetary gear were extracted by envelope spectrum analysis.In the planetary gear box fault test，the root mean square method was used to identify the spalling fault initially，and the optimal IMF component of each spalling fault signal was obtained by GWO-TVF-EMD，and the envelope spectrum analysis was used to judge the fault frequency of the planetary gear clearly.The method can extract the detailed features of three different degrees of tooth surface spalling faults，and the relative error between the theoretical value and the actual value was 1.68%.
- planetary gearbox /
- spalling /
- grey wolf optimizer /
- time varying filter for empirical mode decomposition /
- feature extraction

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