基于小波理论的滚动轴承振动信号去噪方法初探.doc
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摘 要
滚动轴承是转动设备中应用最为广泛的机械零件,也是最容易产生故障的元件。滚动轴承出现故障时,在荷载的作用下局部失稳会释放出机械波,这些机械波的表现形式、波形特征等信息间接地表达了滚动轴承的状态。在实际工程中由振动监测仪采集到的信号含有大量的噪声,从强背景噪声环境中将这些信号提取出来,对滚动轴承的状态检测有很重要的意义。
系统的学习了傅里叶变换、窗口傅里叶变换和小波变换,列举出几种常用的小波基函数并分析出其特点,对小波理论及去噪的基本原理做了分析。剖析了小波变换的特点、性质和相较于其他分析方法的优点。作为一种多分辨率的的信号处理办法,小波分析能很好的去除含噪信号中的噪声,从含噪信号中提取到有用的信息。
本文通过使用控制变量法对影响去噪效果的因素进行控制,以Matlab软件作为仿真平台,分别用不同的阈值函数和不同的阈值选取形式对滚动轴承的振动信号进行了去噪仿真,计算去噪前后信号的信噪比,对比分析去噪前后的信号波形,得出针对滚动轴承振动信号的最佳去噪方法。
关键词:滚动轴承;小波变换;去噪
Abstract
As the most widely used mechanical part, the rolling bearing is the component which the most prone to failure. The faulty rolling bearing will release mechanical wave when it is partial no stabilization under load function. The form of manifestation and wave characteristics of mechanical wave which are released by structure indirectly express the state of the rolling bearing. The signals collected by the vibration monitor in practical engineering contain a lot of noise. Extracting those signals from the strong noise background has the vital significance to the rolling bearing condition monitor.
Fourier transform, Fast wavelet transform to the Wavelet transform are studied systematically in this paper. Several wavelet basis functions are listed and its characteristics are analyzed in this paper. The theories of denoising based on wavelet transform are introduced. The characteristics, nature and advantages of the wavelet transform are analyzed by comparing other methods. As an advanced multi-resolution approach of signal processing, the noise can be removed from signal containing noise and useful information can be extracted from the signal that contains noise by wavelet analysis.
The Matlab software is used as the simulation platform in this thesis. Using the different threshold processing approaches and different methods acquiring threshold denoise the rolling bearing vibration signals. The factors affecting the de-noising effect of the rolling bearings’ vibration signals are controlled by using the method of v
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