摩擦振动信号的经验模式分解和多重分形研究

发布时间：2017-12-26 19:05

本文关键词：摩擦振动信号的经验模式分解和多重分形研究　出处：《振动与冲击》2016年03期 　论文类型：期刊论文

【摘要】：为提取摩擦振动的特征信号和实现摩擦振动特征信号的定量表征,在摩擦磨损试验机上进行了船用柴油机缸套—活塞环摩擦副摩擦磨损模拟试验,应用经验模式分解(Empirical Mode Decomposition,EMD)对非线性、非平稳的摩擦振动信号进行分解,获得若干个本征模式分量(Intrinsic Mode Function,IMF)。根据振幅范围和互相关系数,从EMD的所有IMF中选择反映摩擦振动特征的IMF分量重新合成摩擦振动特征信号,利用改进的多重分形算法对摩擦振动特征信号进行多重分形分析,得到摩擦振动信号的多重分形谱,进一步求取摩擦振动信号多重分形谱的宽度、极大值、维差以及非对称指数。研究结果表明,经验模式分解能够实现微弱摩擦振动特征信号的提取,多重分形谱参数可以作为摩擦振动信号的特征值。
[Abstract]:For the quantitative characterization and friction vibration signals of extracting characteristic signals of friction and vibration, the friction and wear test machine of friction and wear testing friction with diesel cylinder piston ring ship machine, use of empirical mode decomposition (Empirical Mode Decomposition, EMD) to decompose the nonlinear and non stationary friction vibration signals, obtained several intrinsic mode components (Intrinsic, Mode Function, IMF). According to the amplitude range and correlation coefficient, reflecting friction vibration signal from EMD IMF IMF in all components of the new friction vibration signals, by multifractal analysis of friction vibration signals using the multi fractal algorithm, get the multifractal spectrum of vibration signals of friction, and further to obtain the friction vibration signal of multifractal spectrum width, maximum dimension, difference and asymmetric index. The results show that empirical mode decomposition can achieve the extraction of weak friction vibration characteristic signals, and the multifractal spectrum parameters can be used as the eigenvalues of friction vibration signals.
【作者单位】：大连海事大学轮机工程学院;上海海事大学商船学院;
【基金】：国家高技术研究发展计划(863计划;2013AA040203)
【分类号】：U664.121
【正文快照】： 摩擦振动是机械运动接触副在摩擦磨损过程中产生的现象,蕴含着许多反映摩擦学系统特征和状态的信息[1]。摩擦振动信号是微弱信号,往往堙没于背景噪声之中,未经处理的摩擦振动信号不能真实反映摩擦振动特征[2]。因此,如何从强噪声中提取有用信号,以及根据此有用信号找出摩擦振

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