基于同源的旋转机械振源信号分离策略

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Abstract

In order to solve the problem that it is difficult to determine the statistical characteristics and the number of vibration sources in the vibration source separation of rotating machinery, a vibration source separation strategy based on homologous response was proposed.The strategy establishes the description of the target object according to the repetitive characteristics of the rotating mechanical equipment, and summarizes the nature of the multiple response waveforms of the same vibration source (homologous) into three points: response fragmentation, similar patterns, and certain distribution rules.Taking the three properties of the homologous response as the separation criterion of the vibration source signals, it is more versatile for the vibration source signals of the rotating machinery, thereby overcoming the problems that the statistical characteristics of the vibration source signals and the number of sources are difficult to determine.Introducing the concept of homology, the vibration sources in the mixed signal were sequentially separated, which provides a new reference for the vibration source separation of rotating machinery.Based on this strategy, a method of separating vibration source signals was given, but it is not limited to the method given.The feasibility of this strategy has been verified by experimental analysis.

Key words： fault diagnosis separation of vibration source signals homology separation strategy

Received: 06 July 2020 Published: 28 October 2021

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	Articles by authors
	HE Zhiyang
	LIU Dongdong
	CHENG Weidong

Cite this article:

HE Zhiyang,LIU Dongdong,CHENG Weidong. Vibration source signal separation strategy of rotating machinery based on homology[J]. JOURNAL OF VIBRATION AND SHOCK, 2021, 40(20): 42-49.

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http://jvs.sjtu.edu.cn/EN/ OR http://jvs.sjtu.edu.cn/EN/Y2021/V40/I20/42

［1］FORRESTER B D.Advanced vibration analysis techniques for fault detection and diagnosis in geared transmission systems［D］.Swinburne University of Technology, Australia, 1996.

［2］ZHAO D, WANG T, GAO R X, et al.Signal optimization based generalized demodulation transform for rolling bearing nonstationary fault characteristic extraction［J］.Mechanical Systems and Signal Processing, 2019,134: 106297.

［3］何正嘉, 刘雄, 屈梁生.信号时域平均原理和应用［J］.信号处理, 1986(4): 46-53.
HE Zhengjia, LIU Xiong, QU Liangsheng.The principle and application of signal time domain average［J］.Signal Processing, 1986(4): 46-53.

［4］ANTONI J, RANDALL R B.The spectral kurtosis: application to the vibratory surveillance and diagnostics of rotating machines［J］.Mechanical Systems & Signal Processing, 2006,20(2): 308-331.

［5］余建波,刘海强,郑小云,等.基于ITD与稀疏编码收缩的滚动轴承故障特征提取方法［J］.振动与冲击, 2018,37(19): 23-29.
YU Jianbo, LIU Haiqiang, ZHENG Xiaoyun, et al.Fault feature extraction method of rolling bearings based on ITD-SCS［J］.Journal of Vibration and Shock , 2018,37(19): 23-29.

［6］王天杨.齿轮噪源干扰下变转速运行滚动轴承的故障诊断研究［D］.北京：北京交通大学,2015.

［7］彭玲.基于GVMD与流形学习的滚动轴承故障诊断研究［D］.重庆：重庆大学,2017.

［8］SAWADA H, ARAKI S, MUKAI R, et al.Blind extraction of dominant target sources using ica and time-frequency masking［J］.IEEE Transactions on Audio, Speech and Language Processing, 2006,14(6): 2165-2173.

［9］JUTTEN C.Blind separation of sources, Part I： An adaptive algorithm based on neuromimetic architecture［J］.Signal Processing, 1991,24(1): 1-10.

［10］YPMA A, LESHEM A, DUIN R P W.Blind separation of rotating machine sources: bilinear forms and convolutive mixtures［J］.Neurocomputing, 2002,49(1/2/3/4): 349-368.

［11］李志农,刘卫兵,易小兵.基于局域均值分解的机械故障欠定盲源分离方法研究［J］.机械工程学报, 2011,47(7): 97-102.
LI Zhinong, LIU Weibing, YI Xiaobing.Underdetermined blind source separation method of machine faults based on local mean decomposition［J］.Journal of Mechanical Engineering, 2011,47(7):97-102.

［12］朱会杰,王新晴,芮挺,等.基于移不变稀疏编码的单通道机械信号盲源分离［J］.振动工程学报, 2015,28(4): 625-632.
ZHU Huijie, WANG Xinqing, RUI Ting, et al.Shift invariant sparse coding for blind source separation of single channel mechanical signal［J］.Journal of Vibration Engineering, 2015,28(4): 625-632.

［13］GELLE G, COLAS M.Blind souce separation: a tool for rotating machine monitoring by vibration analysis? ［J］.Sound and Vibration, 2001,248(5): 865-885.

［14］GELLE G, COLAS M, SERVIERE C.Blind source separation: a new pre-processing tool for rotating maehines monitorin［J］.Sound and Vibration, 2003,52(3): 790-795.

［15］BOFILL P, ZIBULEVSKY M.Underdetermined blind source separation using sparse representations［J］.Signal Processing, 2001,81(11): 2353-2362.

［16］GAO G, YANG J, JING X Y, et al.Learning robust and discriminative low-rank representations for face recognition with occlusion［J］.Pattern Recognition, 2017,66: 129-143.

［17］王洪俊.基于特征学习的人脸识别研究［D］.北京：北京邮电大学,2018.

［18］SHAHID N, KALOFOLIAS V, BRESSON X, et al.Robust principal component analysis on graphs［C］//IEEE International Conference on Computer Vision (ICCV), Santiago, Chile, 2015.

［19］屈梁生.机械故障诊断学［M］.上海：上海科学技术出版社，1986.

［20］屈梁生, 张西宁, 沈玉娣.机械故障诊断理论与方法［M］.西安：西安交通大学出版社, 2009.

［21］徐敏.设备故障诊断手册［M］.西安：西安交通大学出版社, 1998.

［22］SFAKIOTAKIS V G, ANIFANTIS N K.Finite element modeling of spur gearing fractures［J］.Finite Elements in Analysis and Design, 2002,39(2): 79-92.

［23］LI C J, LEE H.Gear fatigue crack prognosis using embedded model, gear dynamic model and fracture mechanics［J］.Mechanical Systems and Signal Processing, 2005,19(4): 836-846.

［24］MACKALDENER M, OLSSON M.Analysis of crack propagation during tooth interior fatigue fracture［J］.Engineering Fracture Mechanics, 2002,69(18): 2147-2162.

［25］YEH C C M, ZHU Y, ULANOVA L, et al.Matrix profile I: all pairs similarity joins for time series: a unifying view that includes motifs, discords and shapelets［C］//IEEE 16th International Conference on Data Mining (ICDM).IEEE, 2016.