Fault Feature Extraction of Gearbox based on Adaptive VMD

Wenyao Li; Wengang Yang

doi:10.16578/j.issn.1004.2539.2019.04.006

您当前的位置：

首页 >

文章列表页 >

Fault Feature Extraction of Gearbox based on Adaptive VMD

Theory·Reserach | 更新时间：2022-10-03

- Fault Feature Extraction of Gearbox based on Adaptive VMD
- Journal of Mechanical Transmission Vol. 43, Issue 4, Pages: 27-31(2019)
- 作者机构：
  
  1.山西交通职业技术学院工程机械系，山西太原 030031
- 作者简介：
- 基金信息：
- DOI：10.16578/j.issn.1004.2539.2019.04.006
  CLC：
扫描看全文
李文耀,杨文刚.自适应变分模态分解的齿轮箱故障诊断研究[J].机械传动,2019,43(04):27-31.

Li Wenyao,Yang Wengang.Fault Feature Extraction of Gearbox based on Adaptive VMD[J].Journal of Mechanical Transmission,2019,43(04):27-31.
李文耀,杨文刚.自适应变分模态分解的齿轮箱故障诊断研究[J].机械传动,2019,43(04):27-31. DOI： 10.16578/j.issn.1004.2539.2019.04.006.

Li Wenyao,Yang Wengang.Fault Feature Extraction of Gearbox based on Adaptive VMD[J].Journal of Mechanical Transmission,2019,43(04):27-31. DOI： 10.16578/j.issn.1004.2539.2019.04.006.

摘要

强噪环境下，复合故障特征提取难度更大，VMD（Variational Mode Decomposition）被大量应用于齿轮箱故障诊断中；但是它属于参数型分解方法，,K,过大或过小都会导致过分解或欠分解现象，因此分解的层数需要自适应的确定。提出了一种多点峭度和VMD的复合故障特征提取方法。考虑到多点峭度可以提取多故障的冲击性周期的个数；周期性冲击个数决定VMD的分解层数,K,，通过VMD处理后，进一步通过FFT确定故障特征。所提出的自适应复合故障特征提取方法和EEMD（Ensemble Empirical Mode Decomposition）对比分析，验证了它可以克服模态混叠的特征，通过对实测性信号处理进一步确定了此方法的有效性。最终确定了齿轮剥落和轴承滚珠等复合故障特征。

Abstract

In the noisy environment， the composite fault feature extraction is more difficult. The VMD is widely used in gearbox fault diagnosis， but it is a parametric decomposition method. If ,K, is too large or too small， it will lead to over-decomposition or under-decomposition. The number of layers needs to be determined adaptively， a multi-point kurtosis -VMD （Variational Mode Decomposition） composite fault feature extraction method is proposed. Considering the multi-point kurtosis， the number of impact cycles of multiple faults can be extracted， the number of periodic impacts determines the number ,K, of decomposition layers of the VMD， and after VMD processing， the fault features are further determined by FFT. The proposed adaptive composite fault feature extraction method and Ensemble Empirical Mode Decomposition （EEMD）comparison analysis verify that it can overcome the characteristics of modal aliasing. The effectiveness of this method is further determined by the measured signal processing. The composite fault characteristics such as gear spalling and bearing balls are finally determined.

关键词

多点峭度变分模态分解复合故障特征提取

Keywords

Multipoint kurtosisVariational mode decompositionComposite faultFeature extraction

references

WANG Zhijian， HAN Zhennan. A novel procedure for diagnosing multiple faults in rotating machinery［J］. ISA Transactions， 2015， 55：208-218.

WANG Zhijian， WANG Junyuan， KOU Yanfei， et al. Weak fault diagnosis of wind turbine gearboxes based on MED-LMD［J］. Entropy， 2017， 19（6）： 1-11.

胥永刚，孟志鹏，陆明. 基于双树复小波包变换的滚动轴承故障诊断［J］. 农业工程学报，2013， 29（10）： 49-56.

王志坚，王俊元，赵志芳，等. 基于MKurt-MOMEDA的齿轮箱复合故障特征提取［J］. 振动.测试与诊断， 2017，37（4）：830-834.

王志坚，韩振南，刘邱祖，等. 基于MED-EEMD的滚动轴承微弱故障特征提取［J］. 农业工程学报， 2014， 30（23）：70-78.

DRAGOMIRETSKIY K， ZOSSO D. Variational mode decom position［J］. IEEE Transactions on Signal Processing， 2014， 62（3）：531-544.

WANG Jianguo， CHEN Shuai， ZHANG Chao. Fault diagnosis method of gear based on vmd and multi-feature fusion［J］. Journal of Mechanical Transmission， 2017（3）：32.

XIANG Ling， ZHANG Lijia. Rolling bearing fault feature extraction based on the VMD and 1.5-dimensional Teager energy spectrum［J］. Journal of Vibration and Shock， 2017， 35（18）： 98-104.

MCDONALD Geoff L， ZHAO Qing. Multipoint optimal minimum entropy deconvolution and convolution fix： application to vibration fault detection［J］. Mechanical Systems & Signal Processing， 2016， 82：461-477.

Views

下载量

CSCD

Alert me when the article has been cited

提交

Tools

Publicity Resources

Bearing Feature Extraction Method Based on the Time Subsequence

Bearing Fault Diagnosis Based on Parameter Optimized VMD and ELM with Improved SSA

Fault Diagnosis of Fan Bearings Based on an Improved Grey Wolf Optimization Algorithm and SVM

Fault Diagnosis of Gearboxes Based on AO-VMD and IAO-SVM

A VMD and CNN Combined Fault Diagnosis Method for Rolling Bearings

Related Author

No data

Related Institution

IOT Engineering College, Wuxi City College of Vocational Technology

School of Information Management, Wuxi Institute of Communications Technology

School of Mechatronics Engineering, Henan University of Science and Technology

SIPPR Engineering Group Co., Ltd.

Shandong Chaoyang Bearings Co., Ltd.

⁰