基于改进LMD和MED的滚动轴承故障诊断研究

范卓幽; 高晓蓉; 罗林

基于改进LMD和MED的滚动轴承故障诊断研究

西南交通大学物理科学与技术学院, 成都 610031

基金项目:

国家自然科学基金(61471304)

详细信息

作者简介:
范卓幽,在读硕士研究生;高晓蓉,教授。

中图分类号: U260.331.2;TP39
计量
- 文章访问数: 52
- HTML全文浏览量: 2
- PDF下载量: 9
出版历程
- 收稿日期: 2019-03-26

Fault diagnosis of rolling bearing based on improved LMD and MED

School of Physical Science and Technology, Southwest Jiaotong University, Chengdu 610031, China

摘要

摘要: 针对轨边声学轴承信号有用特征微弱、易被强噪声掩盖的问题,设计实现了一种将最小熵解卷积与改进局域均值分解相结合的方法,达到信号降噪与故障诊断目的。利用三次Hermite插值改善LMD并提高LMD分解精度。将采集到的强噪信号进行MED降噪,再利用改进LMD算法进行分解,使多分量信号分解成单分量信号,并计算各分量的峭度值,挑选出峭度值最大的分量,最后利用包络谱分析,提取滚动轴承的故障特征。计算信号的峰值信噪比(PSNR,Peak Signal to Noise Ratio),将其作为降噪指标,体现方法的降噪性能。实验结果表明,设计的方法应用于轴承故障诊断,能将信号信噪比提高5.13 dB,能精准定位并提取轴承缺陷位置和信号特征,具有较好降噪和信息分辨能力。
- 局域均值分解 /
- MED /
- 故障诊断 /
- 三次Hermite插值
Abstract: Aiming at the problem that the useful characteristics of the trackside acoustic bearing signal are weak and easy to be covered by strong noise, a method combining the minimum entropy deconvolution with the improved local mean decomposition is designed and implemented to achieve the purpose of signal noise reduction and fault diagnosis. The cubic Hermite interpolation was used to improve LMD and LMD decomposition accuracy.The collected strong noise signal was used for MED noise reduction, and then the improved LMD algorithm was used to decompose the multi-component signal into a single component signal. The kurtosis value of each component was calculated, and the component with the largest kurtosis value was selected. Finally, the envelope spectrum analysis was used to extract the fault characteristics of rolling bearing.The peak signal to noise ratio of the signal was calculated as the noise reduction index to reflect the noise reduction performance of the method.The experimental results show that the designed method can improve the signal-to-noise ratio by 5.13 dB, accurately locate and extract the location and signal characteristics of bearing defects, and has better noise reduction and information resolution ability.
- local mean decomposition /
- minimum entropy deconvolution /
- fault diagnosis /
- cubic Hermite interpolation

HTML全文

参考文献(13)

[1]	孙伟,熊邦书,黄建萍,等. 小波包降噪与LMD相结合的滚动轴承故障诊断方法[J]. 振动与冲击, 2012, 31(18):153-156.
[2]	胡飞. 列车轴承故障轨边声学检测系统关键技术研究[D]. 合肥:中国科学技术大学, 2013.
[3]	陈刚,左成,邢宗义. 基于EMD和包络分析的城轨列车滚动轴承故障诊断[J].铁路计算机应用, 2016, 25(7):57-60.
[4]	程军圣,杨怡,杨宇.基于LMD的能量算子解调机械故障诊断方法[J].振动、测试与诊断, 2012, 32(6):915-919.
[5]	侯高雁,吕勇,肖涵.基于LMD的多尺度形态学在齿轮故障诊断中的应用[J]. 振动与冲击, 2014, 33(19):69-73.
[6]	吕靖香,余建波. 基于多层混合滤噪的轴承早期弱故障特征提取方法[J]. 振动与冲击, 2018, 37(8):22-27.
[7]	任学平,李攀,王朝阁. 基于LMD和MCKD的滚动轴承早期故障诊断[J]. 现代制造工程, 2018(9):143-147, 160.
[8]	Yang Y, Cheng J, Zhang K. An ensemble local means decomposition method and its application to local rub-impact fault diagnosis of the rotor systems[J]. Measurement, 2012, 45(3):561-570.
[9]	Jinsong H, Shixi Y, Daqian R. Spline-Based Local Mean Decomposition Method for Vibration Signal[J]. Journal of Data Acquisition & Processing, 2009, 24(1):82-86.
[10]	Merrien J L, Paul Sablonnière. Rational splines for Hermite interpolation with shape constraints[J]. Computer Aided Geometric Design, 2013, 30(3):296-309.
[11]	刘尚坤,唐贵基,王晓龙. 基于MED和变分模态分解的滚动轴承早期故障诊断方法[J]. 机械传动, 2017(9):185-188.
[12]	Zhang Y, RANDALL, R. B. Rolling element bearing fault diagnosis based on the combination of genetic algorithms and fast kurtogram[J]. Mechanical Systems & Signal Processing, 2009, 23(5):1509-1517.
[13]	Deng L. Zhao R. An improved spline-local mean decomposition and its application to vibration analysis of rotating machinery with rub-impact fault[J]. Journal of Vibroengineering, 2014,16(1):414-433.