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首页> 外文期刊>International journal of comadem >Spline Wavelet based Filtering for Denoising Vibration Signals Generated by Rolling Element Bearings
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Spline Wavelet based Filtering for Denoising Vibration Signals Generated by Rolling Element Bearings

机译：基于样条小波滤波的滚动轴承产生的振动信号降噪

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摘要

In this paper, we study biorthogonal spline wavelet decomposition to extract fault features from vibration signals generated by rolling element bearings.Common challenges in the analysis of vibration measurements taken from a real industrial environment is that non-stationary components, generated by othermachine components, disturb the analysis. Vibration signals generated by non-faulty and faulty rolling element bearings are studied. As known, the Fouriertransformation does not work very well on non-stationary signals because their spectral content changes over time. In the time-frequency domain methods,signal decomposition is performed to split spectrum into sequential sub-spectral components that are processed individually. The weakness of the short-timeFourier transform is that the constant window size does not provide sufficient frequency and time resolution at the same time. Lately, the wavelet transformhas been applied on signal demodulation and optimal band-pass filter design. More flexible than basic wavelet basis are spline wavelets that are constructedwith a spline function. Spline wavelets are linear combination of B-splines and they can be defined explicitly. Biorthogonal spline wavelets are regular,compactly supported and have finite impulse response implementation. Computer simulated vibration signals and vibration signals acquired from a realworldapplication are used in our study.
机译：在本文中，我们研究了双正交样条小波分解，以从滚动轴承产生的振动信号中提取故障特征。 r n在对来自实际工业环境的振动测量进行分析时，常见的挑战是由其他因素产生的非平稳分量 r n机器组件，干扰分析。研究了由无故障和有故障的滚动轴承产生的振动信号。众所周知，傅立叶变换在非平稳信号上效果不佳，因为其频谱含量会随时间变化。在时频域方法中，执行 r n信号分解以将频谱拆分为单独处理的顺序子频谱分量。短时傅立叶变换的弱点在于，恒定的窗口大小无法同时提供足够的频率和时间分辨率。最近，小波变换已应用于信号解调和最佳带通滤波器设计。比基本小波基础更灵活的是使用样条函数构造的样条小波。样条小波是B样条的线性组合，可以明确定义。双正交样条小波是规则的， r n完全受支持的，并且具有有限的脉冲响应实现。在我们的研究中使用了计算机模拟的振动信号和从真实世界中获取的振动信号。

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