留言板

尊敬的读者、作者、审稿人, 关于本刊的投稿、审稿、编辑和出版的任何问题, 您可以本页添加留言。我们将尽快给您答复。谢谢您的支持!

姓名
邮箱
手机号码
标题
留言内容
验证码

MCKD最佳故障周期搜索的齿轮箱故障特征提取

冷军发 荆双喜 王志阳 华伟

冷军发, 荆双喜, 王志阳, 华伟. MCKD最佳故障周期搜索的齿轮箱故障特征提取[J]. 机械科学与技术, 2018, 37(1): 36-42. doi: 10.13433/j.cnki.1003-8728.2018.0107
引用本文: 冷军发, 荆双喜, 王志阳, 华伟. MCKD最佳故障周期搜索的齿轮箱故障特征提取[J]. 机械科学与技术, 2018, 37(1): 36-42. doi: 10.13433/j.cnki.1003-8728.2018.0107
Leng Junfa, Jing Shuangxi, Wang Zhiyang, Hua Wei. Optimum Fault Period Searching of MCKD Algorithm for Fault Feature Extraction of Gearbox[J]. Mechanical Science and Technology for Aerospace Engineering, 2018, 37(1): 36-42. doi: 10.13433/j.cnki.1003-8728.2018.0107
Citation: Leng Junfa, Jing Shuangxi, Wang Zhiyang, Hua Wei. Optimum Fault Period Searching of MCKD Algorithm for Fault Feature Extraction of Gearbox[J]. Mechanical Science and Technology for Aerospace Engineering, 2018, 37(1): 36-42. doi: 10.13433/j.cnki.1003-8728.2018.0107

MCKD最佳故障周期搜索的齿轮箱故障特征提取

doi: 10.13433/j.cnki.1003-8728.2018.0107
基金项目: 

国家自然科学基金项目(U1304523)、中国煤炭工业协会项目(MTKJ2015-261)及河南理工大学博士基金项目(B2017-28)资助

详细信息
    作者简介:

    冷军发(1974-),副教授,博士,研究方向为机械振动及故障诊断,lengjf@hpu.edu.cn

    通讯作者:

    荆双喜,教授,博士,jsx@hpu.edu.cn

Optimum Fault Period Searching of MCKD Algorithm for Fault Feature Extraction of Gearbox

  • 摘要: 针对最小解熵解卷积(Minimum entropy deconvolution,MED)算法易受强噪声和野值的影响,引出了最大相关峭度解卷积(Maximum correlated kurtosis deconvolution,MCKD)的齿轮箱故障特征提取方法,克服了MED算法的不足。然而凭先验信息选取的故障周期,可能导致MCKD解卷积效果很差,因此提出了MCKD算法的最佳故障周期搜索思路,即在合适的滤波器阶数L下,最佳故障周期的搜索可以限定于理论计算周期左右的某一范围内,使不同步距M关于最佳周期的最大相关峭度达到全局最优,以确保了MCKD算法具有良好的解卷积效果。断齿与局部断齿故障特征提取试验结果佐证了最佳故障周期搜索思路的可行性及其效果。
  • [1] 叶红仙,杨世锡,杨将新.多振源卷积混合的时域盲源分离算法[J].机械工程学报,2009,45(1):189-194,199 Ye H X, Yang S X, Yang J X. Temporal blind source separation algorithm for convolution mixtures with muti vibration sources[J]. Journal of Mechanical Engineering, 2009,45(1):189-194,199(in Chinese)
    [2] 明阳.基于循环平稳和盲源分离的滚动轴承故障特征提取方法研究[D].上海:上海交通大学,2013 Ming Y. Study on cyclostationarity and blind source separation-based rolling element bearing fault feature extraction[D]. Shanghai:Shanghai Jiaotong University, 2013(in Chinese)
    [3] Rhabi M E, Fenniri H, Keziou A, et al. A robust algorithm for convolutive blind source separation in presence of noise[J]. Signal Processing, 2013,93(4):818-827
    [4] Endo H, Randall R B. Enhancement of autoregressive model based gear tooth fault detection technique by the use of minimum entropy deconvolution filter[J]. Mechanical Systems and Signal Processing, 2007,21(2):906-919
    [5] Barszcz T, Sawalhi N. Fault detection enhancement in rolling element bearings using the minimum entropy deconvolution[J]. Archives of Acoustics, 2012,37(2):132-141
    [6] 冷军发,荆双喜,禹建功.基于最小熵解卷积的齿轮箱早期故障诊断[J].机械科学与技术,2015,34(3):445-448 Leng J F, Jing S X, Yu J G. Incipient fault diagnosis of the gear box based on the minimum entropy deconvolution[J]. Mechanical Science and Technology for Aerospace Engineering, 2015,34(3):445-448(in Chinese)
    [7] McDonald G L. Vibration signal-based fault detection for rotating machines[D]. Edmonton, Alberta:University of Alberta, 2011
    [8] McDonald G L, 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
    [9] 唐贵基,王晓龙.最大相关峭度解卷积结合1.5维谱的滚动轴承早期故障特征提取方法[J].振动与冲击,2015,34(12):79-84 Tang G J, Wang X L. Feature extraction for rolling bearing incipient fault based on maximum correlated kurtosis deconvolution and 1.5 dimension spectrum[J]. Journal of Vibration And Shock, 2015,34(12):79-84(in Chinese)
    [10] 王志坚.齿轮箱复合故障诊断特征提取的若干方法研究[D].太原:太原理工大学,2015 Wang Z J. Research on some new methods in fault diagnosis of gearbox with compound faults[D]. Taiyuan:Taiyuan Polytechnic University, 2015(in Chinese)
    [11] 冷军发,王志阳,荆双喜.基于最大相关峭度解卷积的炼胶机齿轮箱早期故障诊断[J].机械强度,2016,38(5):927-932 Leng J F, Wang Z Y, Jing S X. Incipient fault diagnosis of rubber refiner gearbox based on maximum correlated kurtosis deconvolution[J]. Journal of Mechanical Strength, 2016,38(5):927-932(in Chinese)
    [12] Hyvärinen A, Karhunen J, Oja E. Independent component analysis[M]. Helsinki:John Wiley & Sons, Inc., 2001
    [13] Feng Z P, Zuo M J. Vibration signal models for fault diagnosis of planetary gearboxes[J]. Journal of Sound and Vibration, 2012,331(22):4919-4939
    [14] 冯志鹏,赵镭镭,褚福磊.行星齿轮箱齿轮局部故障振动频谱特征[J].中国电机工程学报,2013,33(5):118-125 Feng Z P, Zhao L L, Chu F L. Vibration spectral characteristics of localized gear fault of planetary gearboxes[J]. Proceedings of the CSEE, 2013,33(5):118-125(in Chinese)
    [15] Lei Y G, Lin J, Zuo M J, et al. Condition monitoring and fault diagnosis of planetary gearboxes:A review[J]. Measurement, 2014,48:292-305
  • 加载中
计量
  • 文章访问数:  166
  • HTML全文浏览量:  29
  • PDF下载量:  4
  • 被引次数: 0
出版历程
  • 收稿日期:  2016-10-14
  • 刊出日期:  2018-01-15

目录

    /

    返回文章
    返回