Research on Gearbox Fault Diagnosis based on Improved LMD Algorithm

Haibo Xing; Yue Chen; Jinghao Li

doi:10.16578/j.issn.1004.2539.2020.12.009

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Research on Gearbox Fault Diagnosis based on Improved LMD Algorithm

Theory·Reserach | 更新时间：2022-11-10

- Research on Gearbox Fault Diagnosis based on Improved LMD Algorithm
- Journal of Mechanical Transmission Vol. 44, Issue 12, Pages: 55-60(2020)
- 作者机构：
  
  1.中国大唐集团科学技术研究院有限公司华东电力试验研究院，安徽合肥 230031
- 作者简介：
- 基金信息：
- DOI：10.16578/j.issn.1004.2539.2020.12.009
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邢海波,陈悦,李敬豪.基于改进LMD算法的齿轮箱故障诊断研究[J].机械传动,2020,44(12):55-60.

Xing Haibo,Chen Yue,Li Jinghao.Research on Gearbox Fault Diagnosis based on Improved LMD Algorithm[J].Journal of Mechanical Transmission,2020,44(12):55-60.
邢海波,陈悦,李敬豪.基于改进LMD算法的齿轮箱故障诊断研究[J].机械传动,2020,44(12):55-60. DOI： 10.16578/j.issn.1004.2539.2020.12.009.

Xing Haibo,Chen Yue,Li Jinghao.Research on Gearbox Fault Diagnosis based on Improved LMD Algorithm[J].Journal of Mechanical Transmission,2020,44(12):55-60. DOI： 10.16578/j.issn.1004.2539.2020.12.009.

摘要

齿轮箱是机械传动系统中最关键，也是最易发生故障的零部件之一。针对齿轮箱的故障诊断问题，提出了一种改进局部均值分解算法（Improved Local mean decomposition，ILMD），并将其应用于齿轮箱微弱故障特征的提取。首先，为了降低局部均值分解（Local mean decomposition，LMD）算法的模态混叠效应，将一种优化的有理样条插值算法应用于ILMD包络线的构造；然后，采用ILMD算法将原始振动信号分解为一系列乘积函数分量（Product function，PF），并根据峭度值筛选出分解结果中包含故障信息最多的有效分量；最后，通过对有效分量的包络分析实现齿轮箱故障的有效诊断。实验结果表明，所提出方法能有效抑制LMD的模态混叠现象，并且能准确地识别出齿轮磨损故障。

Abstract

Gearbox is one of the most critical and fault-prone components in a mechanical transmission system. Aiming at the fault diagnosis of gearbox，an Improved Local Mean Decomposition （ILMD） algorithm is proposed and applied to the extraction of fault features of gearbox. Firstly，in order to reduce the modal aliasing problem of the Local Mean Decomposition （LMD） algorithm，an optimized rational spline interpolation algorithm is introduced to the construction of the envelope curve. Then，the ILMD algorithm is used to decompose the measured vibration signal into a series of product functions （PF）. After that，the effective component which contains the most fault information among the decomposition results is selected based on the kurtosis. Finally，through the analysis of the envelope spectrum of the effective component，the diagnosis of gearbox fault is realized. The experimental results demonstrate that the proposed method can effectively suppress the mode mixing，and identify the gear wear failure precisely.

关键词

齿轮箱故障诊断有理样条插值改进局部均值分解

Keywords

GearboxFault diagnosisRational spline interpolationImproved local mean decomposition

references

钟先友，田红亮，赵春华，等.基于迭代滤波和快速峭度图的滚动轴承微弱故障特征提取［J］.振动与冲击，2018，37（9）：190-195．

ZHONG Xianyou，TIAN Hongliang，ZHAO Chunhua，et al.Weak fault feature extraction of rolling bearing based on iterative filtering and fast kurtosis diagram［J］.Journal of Vibration and Shock，2018，37（9）：190-195.

万书亭，张雄，南冰，等.基于PPCA-1.5维能量谱的滚动轴承故障诊断［J］.电力自动化设备，2018，38（6）：172-176．

WAN Shuting，ZHANG Xiong，NAN Bing，et al.Fault diagnosis of rolling bearing based on PPCA-1.5D energy spectrum［J］.Electric Power Automation Equipment，2018，38（6）：172-176.

李海平，赵建民，张鑫，等.行星齿轮箱齿轮磨损故障诊断［J］.振动与冲击，2019，38（23）：84-90．

LI Haiping，ZHAO Jianmin，ZHANG Xin，et al.Gear wear diagnosis of planetary gearbox［J］.Journal of Vibration and Shock，2019，38（23）：84-90.

丁闯，冯辅周，张兵志，等.行星变速箱振动信号的线性量子信息熵特征［J］.兵工学报，2018，39（12）：2306-2312．

DING Chuang，FENG Fuzhou，ZHANG Bingzhi，et al.Linear quantum information entropy characteristics of vibration signal of planetary gearbox［J］.Journal of Military Engineering，2018，39（12）：2306-2312.

何小高，张庆，贾林山，等.变速重载设备齿轮箱轴承故障的改进阶次包络分析方法［J］.西安交通大学学报，2019，53（8）：98-106．

HE Xiaogao，ZHANG Qing，JIA Linshan，et al.Improved order envelope analysis method for gearbox bearing failure of variable speed and heavy load equipment［J］.Journal of Xi'an Jiaotong University，2019，53（8）：98-106.

王栋璀，丁云飞，朱晨烜.基于中智KNN的齿轮箱故障诊断方法［J］.振动与冲击，2019，38（20）：148-153．

WANG DongCui，DING Yunfei，ZHU Chenxuan.Gearbox fault diagnosis method based on KNN［J］.Journal of Vibration and Shock，2019，38（20）：148-153.

古莹奎，曾磊，张敏，等.基于CEEMDAN-SQI-SVD的齿轮箱局部故障特征提取［J］.仪器仪表学报，2019，40（5）：78-88．

GU Yingkui，ZENG Lei，ZHANG Min，et al.Local fault feature extraction of gearbox based on CEEMDAN-SQI-Svd［J］.Journal of Instrumentation，2019，40（5）：78-88.

LIAN J，LIU Z，WANG H，et al.Adaptive variational mode decomposition method for signal processing based on mode characteristic［J］.Mechanical Systems and Signal Processing，2018，107：53-77.

李道伦，吴刚．圆形剖分下的特殊形式的多元有理样条插值［J］.工程数学学报，2000（4）：96-98．

LI Daolun，WU Gang.The special form of multivariate rational spline interpolation under circular subdivision［J］.Journal of Engineering Mathematics，2000（4）：96-98.

李永波.滚动轴承故障特征提取与早期诊断方法研究［D］.哈尔滨：哈尔滨工业大学，2017：82-91.

LI Yongbo.Research on fault feature extraction and early diagnosis of rolling bearing［D］.Harbin：Harbin Institute of Technology，2017：82-91.

王亚萍，匡宇麒，葛江华，等.CEEMD和小波半软阈值相结合的滚动轴承降噪［J］.振动、测试与诊断，2018，38（1）：80-86．

WANG Yaping，KUANG Yuqi，GE Jianghua，et al.Rolling bearing noise reduction by combining CEEMD and wavelet semi soft threshold［J］.Journal of Vibration，Measurement & Diagnosis，2018，38（1）：80-86.

陈伟，王友仁，孙灿飞，等.行星齿轮箱耦合调制振动信号分离与故障诊断［J］.航空动力学报，2018，33（5）：1112-1120．

CHEN Wei，WANG Youren，SUN Canfei，et al.Separation and fault diagnosis of coupled modulation vibration signal of planetary gearbox［J］.Journal of Aeronautical Power，2018，33（5）：1112-1120.

陈向民，黎琦，张亢，等.变转速齿轮箱复合故障的自适应时变滤波分析［J］.振动与冲击，2019，38（14）：138-145．

CHEN Xiangmin，LI Qi，ZHANG Kang，et al.Adaptive time-varying filtering analysis of compound fault of variable speed gearbox［J］.Journal of Vibration and Shock，2019，38（14）：138-145.

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