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

旋转机械振动信号频域随机压缩与故障诊断

王江萍 段腾飞

王江萍, 段腾飞. 旋转机械振动信号频域随机压缩与故障诊断[J]. 机械科学与技术, 2018, 37(2): 293-299. doi: 10.13433/j.cnki.1003-8728.2018.0221
引用本文: 王江萍, 段腾飞. 旋转机械振动信号频域随机压缩与故障诊断[J]. 机械科学与技术, 2018, 37(2): 293-299. doi: 10.13433/j.cnki.1003-8728.2018.0221
Wang Jiangping, Duan Tengfei. Frequency Domain Random Compression of Vibration Signal and Fault Detection for Rotation Machinery[J]. Mechanical Science and Technology for Aerospace Engineering, 2018, 37(2): 293-299. doi: 10.13433/j.cnki.1003-8728.2018.0221
Citation: Wang Jiangping, Duan Tengfei. Frequency Domain Random Compression of Vibration Signal and Fault Detection for Rotation Machinery[J]. Mechanical Science and Technology for Aerospace Engineering, 2018, 37(2): 293-299. doi: 10.13433/j.cnki.1003-8728.2018.0221

旋转机械振动信号频域随机压缩与故障诊断

doi: 10.13433/j.cnki.1003-8728.2018.0221
基金项目: 

国家科技重大专项项目(2011ZX05046-04-07)与西安石油大学全日制硕士研究生优秀学位论文培育项目(2015YP140407)资助

详细信息
    作者简介:

    王江萍(1959-),教授,硕士,研究方向为工程检测与故障诊断技术,jpwang@xsyu.edu.cn

Frequency Domain Random Compression of Vibration Signal and Fault Detection for Rotation Machinery

  • 摘要: 提出一种旋转机械故障诊断方法,该方法由频域随机压缩和稀疏表示分类两部分组成。频域随机压缩实现了故障特征的提取,首先通过傅里叶变换得到振动信号的幅值序列,然后构造随机测量矩阵对幅值序列进行压缩测量,压缩测量值作为故障特征向量。在稀疏表示分类中,以有故障标签的特征向量构成故障特征库,将待测特征向量的分类问题转化为稀疏优化问题,应用正交匹配追踪求得待测特征在故障特征库上的表示系数,然后利用表示系数求出待测特征的类重构偏差,根据类重构偏差可以得到诊断结果。齿轮和轴承故障诊断实验证实了本文所提方法的有效性。
  • [1] 王江萍.机械设备故障诊断技术及应用[M].西安:西北工业大学出版社,2001:170-171 Wang J P. The fault diagnosis technology of mechanical equipment and its application[M]. Xi'an:Northwestern Polytechnical University Press, 2001:170-171(in Chinese)
    [2] 屈梁生.机械故障的全息诊断原理[M].北京:科学出版社, 2007:1 Qu L S. Holospectrum and holobalancing technique in machinery diagnosis[M]. Beijing:Science Press, 2007:1(in Chinese)
    [3] Cheng J S, Yu D J, Tang J S, et al. Application of SVM and SVD technique based on EMD to the fault diagnosis of the rotating machinery[J]. Shock and Vibration, 2009,16(1):89-98
    [4] 杨一舟,蒋东翔.概率神经网络用于机匣振动故障诊断[J].机械科学与技术,2016,35(12):1805-1810 Yang Y Z, Jiang D X. Casing vibration fault diagnosis based on probabilistic neural networks[J]. Mechanical Science and Technology for Aerospace Engineering, 2016,35(12):1805-1810(in Chinese)
    [5] 王波,刘树林,张宏利,等.相关向量机及其在机械故障诊断中的应用研究进展[J].振动与冲击,2015,34(5):145-153,167 Wang B, Liu S L, Zhang H L, et al. Advances about relevance vector machine and its applications in machine fault diagnosis[J]. Journal of Vibration and Shock, 2015,34(5):145-153,167(in Chinese)
    [6] Donoho D L. Compressed sensing[J]. IEEE Transactions on Information Theory, 2006,52(4):1289-1306
    [7] Kutyniok G. Theory and applications of compressed sensing[J]. GAMM-Mitteilungen, 2013,36(1):79-101
    [8] Candès E J. The restricted isometry property and its implications for compressed sensing[J]. Comptes Rendus Mathematique, 2008,346(9-10):589-592
    [9] Baraniuk R, Davenport M, DeVore R, et al. A simple proof of the restricted isometry property for random matrices[J]. Constructive Approximation, 2008,28(3):253-263
    [10] 佟路,王华,洪荣晶.基于压缩感知的回转支承振动监测信号采集方法[J].南京工业大学学报(自然科学版),2015,37(5):48-52,60 Tong L, Wang H, Hong R J. Slewing bearing vibration signal acquisition based on compressed sensing[J]. Journal of Nanjing Tech University (Natural Science Edition), 2015,37(5):48-52,60(in Chinese)
    [11] 刘畅,伍星,毛剑琳,等.基于压缩感知的滚动轴承振动信号压缩方法[J].昆明理工大学学报(自然科学版),2015,40(4):46-50 Liu C, Wu X, Mao J L, et al. Rolling bearing signal compression using compressive sensing[J]. Journal of Kunming University of Science and Technology (Natural Science Edition), 2015,40(4):46-50(in Chinese)
    [12] Davenport M A, Wakin M B, Baraniuk R G. Detection and estimation with compressive measurements[R]. Houston, Texas:Department ECE, Rice University, 2006
    [13] Candes E J, Tao T. Near-optimal signal recovery from random projections:universal encoding strategies?[J]. IEEE Transactions on Information Theory, 2006,52(12):5406-5425
    [14] Bajwa W U, Haupt J D, Raz G M, et al. Toeplitz-structured compressed sensing matrices[C]//2007 IEEE/SP 14th Workshop on Statistical Signal Processing, 26-29 August, 2007, Madison, WI, USA. Madison, WI:IEEE, 2007:294-298
    [15] Wright J, Yang A Y, Ganesh A, et al. Robust face recognition via sparse representation[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2009,31(2):210-227
    [16] Tropp J A, Gilbert A C. Signal recovery from random measurements via orthogonal matching pursuit[J]. IEEE Transactions on Information Theory, 2007,53(12):4655-4666
    [17] Yang H Y, Mathew J, Ma L. Fault diagnosis of rolling element bearings using basis pursuit[J]. Mechanical Systems and Signal Processing, 2005,19(2):341-356
    [18] Zhang L J, Xu J W, Yang J H, et al. Multiscale morphology analysis and its application to fault diagnosis[J]. Mechanical Systems and Signal Processing, 2008,22(3):597-610
  • 加载中
计量
  • 文章访问数:  75
  • HTML全文浏览量:  10
  • PDF下载量:  3
  • 被引次数: 0
出版历程
  • 收稿日期:  2016-12-19
  • 刊出日期:  2018-02-25

目录

    /

    返回文章
    返回