PMA-ASTFA及其在齿轮裂纹定量诊断中的应用
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摘要
目前对齿轮裂纹的诊断研究多采用定性诊断,而工程实际中往往更关注定量诊断。由于齿轮裂纹信号往往表现出非线性非平稳特征,处理这类信号通常采用时频分析。自适应最稀疏时频分析(Adaptive and Sparsest TimeFrequency Analysis,简称ASTFA)是一种新的时频分析方法,相比于经验模态分解(Empirical Mode Decomposition,简称EMD)方法,ASTFA方法能更好地抑制端点效应和模态混淆,但ASTFA方法也存在分解得到的分量排列不规律的缺陷,从而给特征提取时分量的选择带来困难。针对这一问题,提出了一种改进ASTFA算法,即基于主模态分析(Principle Mode Analysis,简称PMA)的自适应最稀疏时频分析(PMA-ASTFA)方法,该方法可以根据所选择的故障特征参数(一个或多个)对内禀模态函数(Intrinsic Mode Function,简称IMF)分量进行排序。根据齿轮故障实验台建立齿轮动力学模型,选择对齿轮裂纹敏感的故障特征参数,再把PMA-ASTFA方法用于实测的齿轮裂纹故障信号处理。实验信号的分析结果表明,提出的方法可以有效地实现齿轮裂纹故障的定量诊断。
Qualitative diagnosis methods is presently mostly adopted in gear tooth crack diagnose study,while quantitative diagnosis methods are usually paid more attention in engineering practice.For the nonlinear and non-stationary features in the gear tooth crack signal,the time-frequency analysis method is usually applied to deal with this kind of signal.The Adaptive and Sparsest Time-Frequency Analysis( ASTFA) is a new method of time-frequency analysis.Compared with Empirical Mode Decomposition( EMD) method,ASTFA method has the advantage in reducing mode mixing and end effect.However,irregular component arrangement defect appears in ASTFA method,making it difficult to select components during feature extraction.In order to solve this problem,an improved ASTFA method is proposed in this paper,which is the Principle Mode Analysis based ASTFA method( PMA-ASTFA).The PMA-ASTFA method can reorder the Intrinsic Mode Functions( IMFs) according to fault eigenvalues( one or more).For purpose of screening out fault eigenvalues which are sensitive to gear tooth cracks,a gear dynamic model based on gear fault test bench is established in this paper.Then the PMA-ASTFA method is used to process the measured gear tooth crack signals.The result of experimental signal analysis shows that the proposed method can realize quantitative diagnosis of gear tooth cracks effectively.
Qualitative diagnosis methods is presently mostly adopted in gear tooth crack diagnose study,while quantitative diagnosis methods are usually paid more attention in engineering practice.For the nonlinear and non-stationary features in the gear tooth crack signal,the time-frequency analysis method is usually applied to deal with this kind of signal.The Adaptive and Sparsest Time-Frequency Analysis( ASTFA) is a new method of time-frequency analysis.Compared with Empirical Mode Decomposition( EMD) method,ASTFA method has the advantage in reducing mode mixing and end effect.However,irregular component arrangement defect appears in ASTFA method,making it difficult to select components during feature extraction.In order to solve this problem,an improved ASTFA method is proposed in this paper,which is the Principle Mode Analysis based ASTFA method( PMA-ASTFA).The PMA-ASTFA method can reorder the Intrinsic Mode Functions( IMFs) according to fault eigenvalues( one or more).For purpose of screening out fault eigenvalues which are sensitive to gear tooth cracks,a gear dynamic model based on gear fault test bench is established in this paper.Then the PMA-ASTFA method is used to process the measured gear tooth crack signals.The result of experimental signal analysis shows that the proposed method can realize quantitative diagnosis of gear tooth cracks effectively.
引文
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[2]Hui Ma,Xu Pang,Ranjiao Feng,et al.Fault features analysis of cracked gear considering the effects of the extended tooth contact[J].Engineering Failure Analysis,2015,48:105—120.
[3]Amarnath M,Praveen Krishna I R.Detection and diagnosis of surface wear failure in a spur geared system using EEMD based vibration signalanalysis[J].Tribology International,2013,61:224—234.
[4]Yang C Y,Wu T Y.Diagnostics of gear deterioration using EEMD approach and PCA process[J].Measurement,2015,61:75—87.
[5]Jinde Zheng,Junsheng Cheng,Yu Yang.Partly ensemble empirical mode decomposition:An improved noise-assisted method for eliminating mode mixing[J].Signal Processing,2014,96:362—374.
[6]袁静,訾艳阳,倪修华,等.改进集成噪声重构经验模式分解的微弱时频特征增强方法及应用[J].机械工程学报,2016,52(19):88—94.YUAN Jing,ZI Yanyang,NI Xiuhua,et al.Weak timefrequent feature enhancement method using improved ensemble noise-reconstructed empirical mode decomposition and its application[J].Journal of Mechanical Engineering,2016,52(19):88—94.
[7]THOMAS Y Hou,Zuoqiang SHI.Adaptive data analysis via sparse time-frequency representation[J].Advances in Adaptive Data Analysis,2011,3(1-2):1—28.
[8]Thomas Y Hou,Zuoqiang SHI.Data driven time-frequency analysis[J].Applied and Computational Harmonic Analysis,2013,35(2):284—308.
[9]杨宇,何知义,李紫珠,等.ASTFA-BSS方法及其在齿轮箱复合故障诊断中的应用[J].中国机械工程,2015,(15):2051—2061.Yang Yu,He Zhiyi,Li Zizhu,et al.ASTFA-BSS method and its applications in composite fault diagnosis for gearbox[J].China Mechanical Engineering,2015,(15):2051—2061.
[10]Zhiguo Wan,Hongrui Cao,Yanyang Zi,et al.An improved time-varying mesh stiffness algorithm and dynamic modeling of gear-rotor system with tooth root crack[J].Engineering Failure Analysis,2014,42:157—177.