Compound Fault Diagnosis for Gearbox based on Variational ModeDecomposition and Correlated Kurtosis

Zhang Xin; Zhao Jianmin; Tong Xiaofan; Song Weixing; Ni Xianglong

doi:10.16578/j.issn.1004.2539.2019.10.029

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Compound Fault Diagnosis for Gearbox based on Variational ModeDecomposition and Correlated Kurtosis

更新时间：2022-10-25

- Compound Fault Diagnosis for Gearbox based on Variational ModeDecomposition and Correlated Kurtosis
- Journal of Mechanical Transmission Issue 10, Pages: 158-164(2019)
- 作者机构：
  
  1.陆军工程大学石家庄校区，河北石家庄 050003
  2.石家庄扬天科技有限公司，河北石家庄 050000
  3.中国洛阳电子装备试验中心，河南洛阳 471003
- 作者简介：
- 基金信息：
- DOI：10.16578/j.issn.1004.2539.2019.10.029
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张鑫,赵建民,童晓帆等.基于变分模态分解和相关峭度的齿轮箱混合故障诊断[J].机械传动,XXXX,XX(XX):158-164.

Zhang Xin,Zhao Jianmin,Tong Xiaofan,et al.Compound Fault Diagnosis for Gearbox based on Variational ModeDecomposition and Correlated Kurtosis[J].Journal of Mechanical Transmission,XXXX,XX(XX):158-164.
张鑫,赵建民,童晓帆等.基于变分模态分解和相关峭度的齿轮箱混合故障诊断[J].机械传动,XXXX,XX(XX):158-164. DOI： 10.16578/j.issn.1004.2539.2019.10.029.

Zhang Xin,Zhao Jianmin,Tong Xiaofan,et al.Compound Fault Diagnosis for Gearbox based on Variational ModeDecomposition and Correlated Kurtosis[J].Journal of Mechanical Transmission,XXXX,XX(XX):158-164. DOI： 10.16578/j.issn.1004.2539.2019.10.029.

摘要

针对齿轮箱混合故障信号非线性、非平稳、噪声成分多以及多故障信号相互干扰的特点，提出了基于变分模态分解和相关峭度的齿轮箱混合故障诊断方法。利用变分模态分解能够有效地将信号分解为若干个具有紧致性模态分量的特点，对混合故障信号进行分解处理，将包含不同故障的模态分量分离出来；然后，根据相关峭度能够识别不同周期的冲击性信号的特点，利用最大相关峭度原则提取出包含不同故障的模态分量；最后，对这些模态分量进行包络分析，实现混合故障诊断的目的。通过实验信号的分析验证，证明了所提方法应用在混合故障诊断中的有效性。另外，分析了模态分量个数变化对方法结果的影响，为后续研究提供了经验性的结论。

Abstract

Aiming at the characteristics of non-linear, non-stationary, multi-noise and multi-fault signals of the compound fault signal in gearboxes, a compound fault diagnosis method for gearboxes based on variational mode decomposition and correlated kurtosis is proposed. According to the characteristic that the signal can be effectively decomposed into several compact intrinsic mode functions by variational mode decomposition, the compound fault signal are decomposed into several intrinsic mode functions which contains different faults. Then，according to the characteristics that correlated kurtosis can identify the impact signals with different periods, the intrinsic mode functions containing different faults are extracted by using the maximum correlated kurtosis principle. Finally, the envelop analysis of these intrinsic mode function is carried out to achieve the purpose of compound fault diagnosis. The validity of the proposed method is proved by the analysis of the experimental signal. In addition, the influence of the number of intrinsic mode functions on the result of the proposed method is analyzed, which provides an empirical conclusion for the follow-up study.

关键词

齿轮箱混合故障变分模态分解相关峭度包络分析

Keywords

GearboxCompound faultVariational mode decompositionCorrelated kurtosisEnvelop analysis

references

LEI Y G,ZUO M J,HE Z J,et al.A multidimensional hybrid intelligent method for gear fault diagnosis[J].Expert Systems with Applications,2010,37(2):1419-1430.

TENG W,DING X,ZHANG X L,et al.Multi-fault detection and failure analysis of wind turbine gearbox using complex wavelet transform[J].Renewable Energy,2016,93:591-598.

胥永刚，孟志鹏，陆明.双树复小波包和ICA用于滚动轴承复合故障诊断[J].振动、测试与诊断，2015，35(3):513-518.

PLAZA G E,LOPEZ J N P.Application of the wavelet packet transform to vibration signals for surface roughness monitoring in cnc turning operations[J].Mechanical Systems and Signal Processing,2018,98:902-919.

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[J].Proceedings of the Royal Socirtu A:Mathematical,Physical and Engineering Sciences,1998,454(1971):903-995.

YU D J,SHENG J S,YANG Y.Application of emd method and hilbert spectrum to the fault diagnosis of roller bearings[J].Mechanical Systems and Signal Processing,2005,19(2):259-270.

马新娜，杨绍普.滚动轴承复合故障诊断的自适应方法研究[J].振动与冲击，2016，35(10):145-150.

WU Z,HUANG N E.Ensemble empicrical mode decomposition a noise-assisted data analysis method[J].Advances in Adaptive Data Analysis,2009,1(1):1-41.

LI H G,LI M L,C LI,et al.Multi-faults decoupling on turbo-expander using differential-based ensemble empirical mode decomposition[J].Mechanical Systems and Signal Processing,2017,93:267-280.

张超，陈建军，郭迅.基于第2代小波和EMMD的转子系统复合故障诊断[J].振动、测试与诊断，2011，31(1):98-103.

PAN H Y,YANG Y,LI X,et al.Symplectic geometry mode decomposition and its application to rotating machinery compound fault diagnosis[J].Mechanical Systems and Signal Processing,2019,114:189-211.

SMITH,JONATHAN S.The local mean decomposition and its application to eeg perception data[J].Journal of the Royal Society Interface,2006,2(5):443-454.

JIAO W D,QIAN S X,CHANG Y P,et al.Research on vibration response of a multi-faulted rotor system using LMD-based time frequency representation[J].EURASIP Journal on Advances in Signal Process,2012,2012(1):73.

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

MCDONALD L G,ZHAO Q,ZUO M J.Maximum correlated kurtosis deconvolution and application on gear tooth chip fault detection[J].Mechanical Systems and Signal Processing,2012,33:237-255.

ZHANG X,KANG J,ZHAO J,et al.Rolling element bearings fault diagnosis based on correlated kurtosis kurtogram[J].Journal of Vibroengineering,2015,17(6):3023-3034.

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