基于MCKD-Teager能量算子的滚动轴承故障特征增强

发布时间：2018-01-20 22:37

本文关键词： 盲解卷积最大相关峭度 Teager能量算子故障诊断　出处：《仪表技术与传感器》2017年06期 　论文类型：期刊论文

【摘要】：针对最大相关峭度解卷积在对低信噪比信号处理时带内噪声较大的问题,提出一种结合MCKD和Teager能量算子的滚动轴承故障增强方法。首先对采样信号MCKD盲解卷积处理清除传递路径的影响,使故障特征成分得到一次增强,然后通过Teager能量算子估计整个滤波信号产生动态信号时所需的总能量,二次增强故障的瞬态特征,最后对瞬时能量包络谱分析得到诊断结果。滚动轴承仿真数据、实验数据及工程实例分析表明,该方法能够有效增强故障特征信息,提取出故障特征频率。
[Abstract]:In order to solve the problem of large in-band noise in the processing of low SNR signals, the maximum correlation kurtosis deconvolution is discussed. A fault enhancement method for rolling bearing combined with MCKD and Teager energy operator is proposed. Firstly, the effect of removing transfer path on blind deconvolution processing of sampled signal MCKD is proposed. The fault feature components are enhanced once, then the total energy needed for the whole filtered signal to generate dynamic signal is estimated by Teager energy operator, and the transient feature of the fault is enhanced twice. The simulation data, experimental data and engineering examples show that this method can effectively enhance the fault feature information and extract the fault feature frequency.
【作者单位】：华东交通大学交通运输和物流学院;
【基金】：国家自然科学基金资助项目(51165007)
【分类号】：TH133.33
【正文快照】： 0引言滚动轴承是回转机械系统中应用最广泛的零部件之一,其工作环境常处于高速高温状态,极易出现表面损伤类故障。因机械结构内部零件联系紧密,轴承故障常会引起整个机械系统出现一系列问题。在实际工程中,受工作环境噪声、振动传输路径、多振动源共同激励及相互之间的共振耦

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