基于参数优化变分模态分解和多尺度熵偏均值的行星变速箱故障特征提取

doi:10.3969/j.issn.1000-1093.2018.09.003

兵工学报 ›› 2018, Vol. 39 ›› Issue (9): 1683-1691.doi: 10.3969/j.issn.1000-1093.2018.09.003

基于参数优化变分模态分解和多尺度熵偏均值的行星变速箱故障特征提取

杨大为，赵永东，冯辅周，江鹏程，丁闯

(陆军装甲兵学院机械工程系，北京 100072)

收稿日期:2018-01-05 修回日期:2018-01-05 上线日期:2018-10-25
通讯作者: 冯辅周(1971—),男,教授，博士生导师 E-mail:fengfuzhou@tsinghua.org.cn
作者简介:杨大为(1995—),男,硕士研究生。E-mail: davidyang1995@foxmail.com

Planetary Gearbox Fault Feature Extraction Based on Parameter Optimized Variational Mode Decomposition and Partial Mean ofMulti-scale Entropy

YANG Da-wei， ZHAO Yong-dong， FENG Fu-zhou， JIANG Peng-cheng， DING Chuang

(Department of Mechanical Engineering， Academy of Army Armored Force， Beijing 100072, China)

Received:2018-01-05 Revised:2018-01-05 Online:2018-10-25

摘要/Abstract

摘要： 针对某型装甲车辆行星变速箱行星轮故障特征难以提取的问题，提出了结合参数优化变分模态分解(VMD)和多尺度熵偏均值的故障特征提取方法。为克服VMD算法参数选取依赖经验的弊端，采用粒子群优化算法对VMD参数进行优化。使用参数优化后的VMD算法对信号进行分解，并依据互信息选取有效分量对信号进行重构。多尺度熵能反映信号在多尺度下的复杂度，偏均值可以反映多尺度熵的均值和变化趋势。采用基于多尺度熵的综合指标多尺度偏均值，以全面反映振动信号在多尺度下的特性，用于衡量行星变速箱运行状态，从而进行故障特征提取。行星变速箱实验数据处理结果表明，新方法可以更加有效的提取行星变速箱故障。

关键词: 行星变速箱, 故障特征提取, 变分模态分解, 多尺度熵偏均值

Abstract: A fault feature extraction method based on parameter optimized variational mode decomposition（VMD）and partial mean of multi-scale entropy is proposed for the extraction of planetary gearbox fault features. The particle swarm optimization（PSO） is used to optimize VMD parameters to overcome the drawback of subjectively selecting the parameters, and the signal is processed by parameter-optimized VMD. The effective components are selected by mutual information to reconstruct signal. Multi-scale entropy can be used to analyze the complexity of signal under different scale factors, and partial mean can be used to reflect the mean value and variation trend of data. Based on multi-scale entropy, the partial mean of multi-scale entropy is used to measure the operating states of planetary gearbox, thus extracting the fault features. The processed results of planetary gearbox experimental data show that the proposed method can be more effective for planetary gearbox fault extraction.Key

Key words: planetarygearbox, faultfeatureextraction, variationalmodedecomposition, partialmeanofmulti-scaleentropy

中图分类号:

杨大为，赵永东，冯辅周，江鹏程，丁闯. 基于参数优化变分模态分解和多尺度熵偏均值的行星变速箱故障特征提取[J]. 兵工学报, 2018, 39(9): 1683-1691.

YANG Da-wei， ZHAO Yong-dong， FENG Fu-zhou， JIANG Peng-cheng， DING Chuang. Planetary Gearbox Fault Feature Extraction Based on Parameter Optimized Variational Mode Decomposition and Partial Mean ofMulti-scale Entropy[J]. Acta Armamentarii, 2018, 39(9): 1683-1691.

参考文献

［1］ Li M H. Recent advances in planetary gearbox fault diagnosis［J］. Applied Mechanics and Materials, 2014, 615:140-144.
［2］雷亚国,何正嘉,林京,等. 行星齿轮箱故障诊断技术的研究进展［J］. 机械工程学报,2011,47(19):59-67.
LEI Ya-guo,HE Zheng-jia,LIN Jing,et al. Research advances of fault diagnosis technique for planetary gearboxes ［J］. Journal of Mechanical Engineering,2011,47(19):59-67. (in Chinese)
［3］邱星辉, 韩勤锴, 褚福磊.风力机行星齿轮传动系统动力学研究综述［J］.机械工程学报,2014,50(11):23-36.
QIU Xing-hui, HAN Qin-kai, CHU Fu-lei. Review on dynamic analysis of wind turbine geared transmission systems［J］. Journal of Mechanical Engineering,2014,50(11):23-36.(in Chinese)
［4］ Dragomiretskiy K, Zosso D.Variational mode decomposition［J］.IEEE Transactions on Signal Processing, 2014,62(3):531-544.
［5］ Ram R, Mohanty M N. Comparative analysis of EMD and VMD algorithm in speech enhancement［J］. International Journal of Natural Computing Research,2017,6(1):17-35.
［6］ Tang G, Luo G G, Zhang W H,et al. Underdetermined blind source separation with variational mode decomposition for compound roller bearing fault signals［J］. Sensors, 2016,16(6):897-914.
［7］姜万录,王浩楠,朱勇,等.变分模态分解消噪与核模糊C均值聚类相结合的滚动轴承故障识别方法［J］.中国机械工程,2017,28(10):1215-1220，1226.
JIANG Wan-lu, WANG Hao-nan, ZHU Yong，et al. Integrated VMD denoising and KFCM clustering fault identification method on rolling bearings［J］.China Mechanical Engineering,2017,28(10): 1215-1220，1226. (in Chinese)
［8］唐贵基,王晓龙. 参数优化变分模态分解方法在滚动轴承早期故障诊断中的应用［J］. 西安交通大学学报,2015,49(5): 73-81.
TANG Gui-ji, WANG Xiao-long. Parameter optimized variational mode decomposition method with application to incipient fault diagnosis of rolling bearing［J］. Journal of Xi'an Jiaotong University,2015,49(5):73-81. (in Chinese)
［9］ Zurek S, Guzik P, Pawlak S，et al. On the relation between correlation dimension，approximate entropy and sample entropy parameters，and a fast algorithm for their calculation［J］. Physica A：Statistical Mechanics and Its Applications,2012,391(24):6601-6610.

［10］ Costa M, Goldberger A L, Peng C K. Multi scale entropy analysis of biological signals ［J］.Physical Review Letters,2005,71（2）: 1-18.
［11］ Pan S，Han T，Andy C，et al. Fault diagnosis system of induction motors based on multiscale entropy and support vector machine with mutual information algorithm［J］. Shock and Vibration,2016 (7):1-12.
［12］ Zheng J D, Pan H Y, Cheng J S. Rolling bearing fault detection and diagnosis based on composite multiscale fuzzy entropy and ensemble support vector machines［J］. Mechanical Systems and Signal Processing,2017,85:746-759.
［13］张龙,黄文艺,熊国良.基于多尺度熵的滚动轴承故障程度评估［J］.振动与冲击,2014,33(9):185-189.
ZHANG Long，HUANG Wen-yi，XIONG Guo-liang. Assessment of rolling element bearing fault severity using multi-scale entropy［J］.Journal of Vibration and Shock,2014,33(9):185-189. (in Chinese)
［14］张亢,吴家腾,廖力达.局部波动特征分解及其在齿轮包络分析中的应用［J］.振动工程学报,2015,28(5):846-854.
ZHANG Kang, WU Jia-teng, LIAO Li-da. Local oscillatory characteristic decomposition and its application to gear envelope analysis ［J］. Journal of Vibrationl Engineering,2015,28(5): 846-854. (in Chinese)
［15］钱林,康敏,傅秀清,等. 基于VMD的自适应形态学在轴承故障诊断中的应用［J］. 振动与冲击,2017,36(3):227-233.
QIAN Lin, KANG Min, FU Xiu-qing, et al. Application of adaptive morphology in bearing fault diagnosis based on VMD［J］. Journal of Vibration and Shock,2017,36(3):227-233. (in Chinese)
［16］ Verma A, Sarangi S, Kolekar M H. Stator winding fault prediction of induction motors using multiscale entropy and grey fuzzy optimization methods［J］. Computers and Electrical Engineering,2014,40(7):2246-2258.
［17］ Aouabdi S, Taibi M, Bouras S,et al. Using multi-scale entropy and principal component analysis to monitor gears degradation via the motor current signature analysis［J］. Mechanical Systems and Signal Processing,2017,90:298-316.
［18］ Liu X Z, Yang Y H, Zhang J. Effects of tooth-crack-induced mesh stiffness on fault signals of a planetary gear train［J］. Procedia Computer Science,2017, 109：785-792.

第39卷
第9期2018 年9月兵工学报ACTA
ARMAMENTARIIVol.39No.9Sep.2018

[1]	吴春志，冯辅周，吴守军，陈汤，江鹏程. 一种有效的不均衡样本生成方法及其在行星变速箱故障诊断中的应用[J]. 兵工学报, 2019, 40(7): 1349-1357.
[2]	丁闯，冯辅周，张兵志，吴守军. 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.
[3]	李志农，朱明. 基于变分模态分解的机械故障诊断方法研究[J]. 兵工学报, 2017, 38(3): 593-599.
[4]	!:杨绍奎，张福田;2:孟惠荣. 履带车辆行星变速装置复合框架强度可靠度计算的智能仿真算法[J]. 兵工学报, 1999, 20(3): 198-203.

基于参数优化变分模态分解和多尺度熵偏均值的行星变速箱故障特征提取

Planetary Gearbox Fault Feature Extraction Based on Parameter Optimized Variational Mode Decomposition and Partial Mean ofMulti-scale Entropy

PDF

可视化

摘要/Abstract

引用本文

使用本文

参考文献

相关文章 4

编辑推荐

Metrics

本文评价