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基于仿真试验和BOA-VMD的轴箱轴承故障特征提取算法研究
Research on axle-box bearing fault feature extraction algorithm based on simulation test and BOA-VMD
- 机车电传动 2022年第2期 页码：105-112
- 作者机构：
1.西南交通大学 机械工程学院，四川 成都 610031
2.西南交通大学 唐山研究生院，河北 唐山 063000
3.中车青岛四方机车车辆股份有限公司，山东 青岛 266111
- 作者简介：
杨 岗（1973—），男，博士，讲师，研究方向为交通设备故障诊断、健康预测与控制工程；E-mail：yanggang@swjtu.cn
- 基金信息：国家重点研发计划项目(2020YFB1200300ZL);国家重大技术装备攻关工程项目（系列化中国标准地铁列车研制及试验）
DOI：10.13890/j.issn.1000-128X.2022.02.015
中图分类号： U231;U270.331+.2纸质出版日期：2022-03-10，
收稿日期：2021-12-10，
修回日期：2022-02-10，
- 稿件说明：
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张东兴, 杨岗, 周奥, 等. 基于仿真试验和BOA-VMD的轴箱轴承故障特征提取算法研究[J]. 机车电传动, 2022,(2):105-112.
ZHANG Dongxing, YANG Gang, ZHOU Ao, et al. Research on axle-box bearing fault feature extraction algorithm based on simulation test and BOA-VMD[J]. Electric drive for locomotives, 2022,(2):105-112.
张东兴, 杨岗, 周奥, 等. 基于仿真试验和BOA-VMD的轴箱轴承故障特征提取算法研究[J]. 机车电传动, 2022,(2):105-112. DOI： 10.13890/j.issn.1000-128X.2022.02.015.
ZHANG Dongxing, YANG Gang, ZHOU Ao, et al. Research on axle-box bearing fault feature extraction algorithm based on simulation test and BOA-VMD[J]. Electric drive for locomotives, 2022,(2):105-112. DOI： 10.13890/j.issn.1000-128X.2022.02.015.
摘要
针对城轨列车运行过程中轴箱轴承故障难以发现的问题，提出一种利用蝴蝶优化算法(Butterfly Optimization Algorithm
BOA)对变分模态分解(Variational Mode Decomposition
VMD）参数进行优化的轴承故障特征提取方法。首先构建基于轴承-车辆刚柔耦合的轴承故障动力学模型，提取轮轨激扰和轴承故障情况下的轴箱振动信号；然后利用蝴蝶优化算法对轴箱振动信号的VMD模态分量数和二次惩罚系数进行寻优，确定最佳参数组合；最后利用已确定的最佳参数对轴承振动信号进行VMD分解，得到不同本征模态分量(Intrinsic Mode Function
IMF)，并对最佳模态分量信号进行包络分析，识别到轴承故障时的特征频率。试验分析表明，基于优化参数的VMD分析方法能够有效提取轴承故障特征频率，通过经验模态分解(Empirical Mode Decomposition
EMD)分析方法对比，可以发现文章提出的分析方法效果更加有效。
Abstract
Aiming at the problem that axle-box bearing faults are difficult to find during the operation of urban rail trains
a bearing fault feature extraction based on variational mode decomposition (VMD) parameter optimization using butterfly optimization algorithm (BOA) was proposed. Firstly
a bearing fault dynamic model based on the rigid-flexible coupling of bearing-vehicle was constructed
and the vibration signal of the axle box under the wheel-rail disturbance and the faulty bearing was extracted. Then
the BOA algorithm is used to optimize the VMD modal component number and the second penalty coefficient of the axle box vibration signal
so as to determine the best parameter combination. Finally
by using the determined optimal parameters
the vibration signal of the bearing was decomposed by VMD to obtain different intrinsic mode components (intrinsic mode function
IMF)
and an envelope analysis was performed to find the eigen frequencies of bearing failures. Through the experimental analysis
it can be seen that the VMD analysis method of optimizing parameters can effectively find the characteristic frequency of bearing faults
and by comparing the EMD analysis method
it can be found that the analysis method proposed in this paper is more effective.
关键词
列车轴承故障特征提取变分模态分解蝴蝶优化算法故障诊断仿真
Keywords
train bearingfault feature extractionvariational mode decompositionBOAfault diagnosissimulation
references
李涛. 铁路货车轮对轴承故障动力学仿真分析[D]. 石家庄: 石家庄铁道大学, 2016：1-55.
LI Tao. Dynamic simulation analysis of wheelset bearing with faults in railway freight vehicle[D]. Shijiazhuang: Shijiazhuang Tiedao University, 2016：1-55.
李长健, 向立明, 刘永强, 等. 基于ADAMS的高速机车双列圆锥滚子轴承典型故障仿真分析[J]. 轴承, 2018(6): 55-59.
LI Changjian, XIANG Liming, LIU Yongqiang, et al. Simulation analysis on double row tapered roller bearings with typical faults in high-speed locomotive based on ADAMS[J]. Bearing, 2018(6): 55-59.
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 Society A-Mathematical Physical and Engineering Sciences, 1998, 454(1971): 903-995.
DRAGOMIRETSKIY K, ZOSSO D. Variational mode decomposition[J]. IEEE Transactions on Signal Processing, 2014, 62(3): 531-544.
张超, 朱腾飞, 王大勇. 基于VMD能量熵与支持向量机的齿轮故障诊断[J]. 机械设计与研究, 2018, 34(2): 81-84.
ZHANG Chao, ZHU Tengfei, WANG Dayong. Fault diagnosis method of gear based on VMD energy entropy and LS-SVM[J]. Machine Design & Research, 2018, 34(2): 81-84.
黄大荣, 柯兰艳, 林梦婷, 等. 一种参数优化VMD多尺度熵的轴承故障诊断新方法[J]. 控制与决策, 2020, 35(7): 1631-1638.
HUANG Darong, KE Lanyan, LIN Mengting, et al. A new fault diagnosis approach for bearing based on multi-scale entropy of the optimized VMD[J]. Control and Decision, 2020, 35(7): 1631-1638.
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 Guiji, WANG Xiaolong. 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.
张树, 刘德平. BFA优化VMD参数的轴承故障诊断[J]. 组合机床与自动化加工技术, 2020(5): 45-47.
ZHANG Shu, LIU Deping. Bearing fault diagnosis based on BFA optimized VMD parameters[J]. Modular Machine Tool & Automatic Manufacturing Technique, 2020(5): 45-47.
张萍, 张文海, 赵新贺, 等. WOA-VMD算法在轴承故障诊断中的应用[J]. 噪声与振动控制, 2021, 41(4): 86-93.
ZHANG Ping, ZHANG Wenhai, ZHAO Xinhe, et al. Application of WOA-VMD algorithm in bearing fault diagnosis[J]. Noise and Vibration Control, 2021, 41(4): 86-93.
逯全波, 王海宝, 陈根, 等. 基于LabVIEW与MATLAB的改进VMD轴承故障诊断方法研究[J]. 制造技术与机床, 2018(6): 63-66.
LU Quanbo, WANG Haibao, CHEN Gen, et al. Research on improved VMD bearing fault diagnosis method based on LabVIEW and MATLAB[J]. Manufacturing Technology & Machine Tool, 2018(6): 63-66.
ARORA S, SINGH S. Butterfly optimization algorithm: a novel approach for global optimization[J]. Soft Computing, 2019, 23(3): 715-734.
ARORA S, SINGH S. Butterfly algorithm with Lèvy flights for global optimization[C]//IEEE. 2015 International Conference on Signal Processing, Computing and Control (ISPCC). Waknaghat: IEEE, 2015: 220-224.
何勇, 王红, 谷穗. 一种基于遗传算法的VMD参数优化轴承故障诊断新方法[J]. 振动与冲击, 2021, 40(6): 184-189.
HE Yong, WANG Hong, GU Sui. New fault diagnosis approach for bearings based on parameter optimized VMD and genetic algorithm[J]. Journal of Vibration and Shock, 2021, 40(6): 184-189.
张阿中, 刘建新, 蔡久凤. 高速列车齿轮箱轴承剥离故障振动分析[J]. 机械传动, 2020, 44(12): 132-136.
ZHANG Azhong, LIU Jianxin, CAI Jiufeng. Vibration analysis on spalling failure of gearbox bearing of high-speed train[J]. Journal of Mechanical Transmission, 2020, 44(12): 132-136.
翟婉明. 车辆-轨道耦合动力学[M]. 4版. 北京: 科学出版社, 2015：1-272.
ZHAI Wanming. Vehicle-track coupled dynamics[M]. 4th ed. Beijing: Science Press, 2015：1-272.
WANG Zhiwei, ZHANG Weihua, YIN Zhonghui, et al. Effect of vehicle vibration environment of high-speed train on dynamic performance of axle box bearing[J]. Vehicle System Dynamics, 2019, 57(4): 543-563.
张帅. 圆锥滚子轴承打滑特性研究[D]. 洛阳: 河南科技大学, 2014：1-56.
ZHANG Shuai. The analysis of tapered roller bearing's skidding characteristics[D]. Luoyang: Henan University of Science and Technology, 2014：1-56.
张俊, 张建群, 钟敏, 等. 基于PSO-VMD-MCKD方法的风机轴承微弱故障诊断[J]. 振动、测试与诊断, 2020, 40(2): 287-296.
ZHANG Jun, ZHANG Jianqun, ZHONG Min, et al. PSO-VMD-MCKD based fault diagnosis for incipient damage in wind turbine rolling bearing[J]. Journal of Vibration, Measurement & Diagnosis, 2020, 40(2): 287-296.
张超, 陈建军. 基于EMD降噪和谱峭度的轴承故障诊断方法[J]. 机械科学与技术, 2015, 34(2): 252-256.
ZHANG Chao, CHEN Jianjun. A fault diagnosis method of roller bearing based on EMD de-noising and spectral kurtosis[J]. Mechanical Science and Technology for Aerospace Engineering, 2015, 34(2): 252-256.
周宏元, 张广才, 王小娟, 等. 基于互相关函数的聚类蝴蝶优化算法的结构损伤识别[J]. 振动与冲击, 2021, 40(17): 189-196.
ZHOU Hongyuan, ZHANG Guangcai, WANG Xiaojuan, et al. Structural damage identification based on clustering butterfly optimization algorithm of cross-correlation function[J]. Journal of Vibration and Shock, 2021, 40(17): 189-196.
李斌, 柳超, 吴华宁, 等. 基于蝴蝶交配优化算法的阵列方向函数峰值计算[J]. 海军工程大学学报, 2017, 29(3): 105-108.
LI Bin, LIU Chao, WU Huaning, et al. Direction function peak calculation of array based on butterfly mating optimization[J]. Journal of Naval University of Engineering, 2017, 29(3): 105-108.
CHEN Bingyan, ZHANG Weihua, SONG Dongli, et al. Blind deconvolution assisted with periodicity detection techniques and its application to bearing fault feature enhancement[J]. Measurement, 2020, 159：1-21.
CHENG Yao, WANG Zhiwei, ZHANG Weihua, et al. Particle swarm optimization algorithm to solve the deconvolution problem for rolling element bearing fault diagnosis[J]. ISA Transactions, 2019, 90: 244-267.
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