徐元博,蔡宗琰.三点对称差分能量算子与经验小波变换在轴承故障诊断中的应用[J].电子测量与仪器学报,2017,31(8):1247-1256 |
三点对称差分能量算子与经验小波变换在轴承故障诊断中的应用 |
Application of demodulation energy operator of symmetrical differencing and empirical wavelet transform in bearing fault diagnosis |
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DOI:10.13382/j.jemi.2017.08.013 |
中文关键词: 轴承故障诊断 经验小波变换 三点对称差分能量算子 瞬时频率 |
英文关键词:bearing fault diagnosis empirical wavelet transform demodulation energy operator of symmetrical differencing instantaneous frequency |
基金项目:中央高校教育教学改革专项经费建设项目(jgy16049,0012 310600161000)资助 |
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中文摘要: |
实际应用中研究机械系统的工作状态时,通常会对其所产的信号进行研究分析,从而得出相关结论。这些由机械系统产生的信号一般含有多种不同波动的混合成分,为了得出可靠的结论,必须从复合信号和背景噪声中分离出有物理意义的成分。因此引入一种新的故障提取方法,首先利用一种较新的模态分解算法——经验小波变换,将一组信号分解成多个具有紧支撑傅里叶频谱的调幅 调频(AM FM)分量;然后利用K L散度值挑选出具有物理意义的分量;最后将挑选出的分量通过三点对称差分能量算子运算,得到其能量谱的同时也能得到瞬时频率,从而提取出故障特征。将该方法用于模拟信号和实际轴承故障信号,并且同之前的方法进行对比。结论表明,该方法不仅能很好的提取轴承故障特征,而且证明该方法具有更好的优越性。 |
英文摘要: |
The working conditions of mechanic system in real life are generally studied by analysis of signals so that the exact conclusions will be drawn. These signals emanating from mechanic system commonly contain a mixture of different oscillations. For a reliable conclusion, it is necessary to separate a set of physically meaningful modes from the mixture and background noise. Based on that, a new method for bearing fault extraction is proposed in this paper. At first, a novel decomposition algorithm named empirical wavelet transform (EWT) is employed to decompose the fault signal into a set of AM FM components that have a compact support Fourier spectrum. And then, K L divergence method is used to select the sensitive component. Finally, the fault characteristic frequency is extracted by a new demodulation method called energy operator of symmetrical differencing (DEO3S) that can restrain the end effect, and the instantaneous frequency is obtained at the same time. The results of the simulation and bearing fault diagnosis experiments indicate that the method can effectively extract fault characteristic frequency, certifying its feasibility and superiority in comparison with the previous methods. |
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