LCD和SWT在滚动轴承故障诊断中的应用

THE APPLICATION OF LCD AND SWT IN FAULT DIAGNOSIS OF ROLLING BEARING

针对强噪声背景下滚动轴承故障诊断中存在的非平稳非线性信号特征提取这一难题,提出一种基于局部特征尺度分解(Local Characteristic-scale Decomposition,LCD)和同步压缩小波变换的方法(Synchrosqueezing Wavelet Transform, SWT),该方法首先对信号进行LCD分解,将其分解成多个內禀尺度函数(Intrinsic Scale Component,ISC),选取包含有效频率成分的ISC作为SWT的输入信号,使用SWT对其作进一步分析,从而提取有效频率特征。对强噪声背景下提取的滚动轴承外圈故障信号、内圈故障信号以及滚动体故障信号进行分析的结果表明,相比局部特征尺度分解、同步压缩小波变换等方法,该方法能够有效抑制噪声,从强噪声背景下提取出有效信号频率特征,从而能够有效判断滚动轴承的运转状况。同时该方法能够有效重构信号。

In order to overcome the difficulty of feature extraction of non-stationary faulty signals in rolling bearing fault diagnosis under strong noise background,a method based on local characteristic-scale decomposition and synchrosqueezing wavelet transform is proposed.Firstly,the measured vibration signals are processed with LCD and decomposes into a series of intrinsic scale component(ISC).Then a number of ISCs containing valid information components are selected for SWT and processing them by SWT so that we can extract the effective frequency characteristics.The analysis results from rolling bearing signals with out ring,inner ring and rolling body faults which in a strong noise background shows that comparing with LCD and SWT,the approach of synchrosqueezing wavelet transform based on LCD can effectively suppress the noise and extract the effective signal frequency characteristics.It also has a high time-frequency resolution for accurately determining the operation of rolling bearings.While the method can also effectively reconstruct the signal.