基于小波相关滤波法的滚动轴承早期故障诊断方法研究

On Incipient Fault Diagnosis of Rolling Bearings with Wavelet Correlation Filter

目前基于小波分析的滚动轴承故障诊断方法研究已经很多,但是这些方法对于强噪声背景下的早期故障微弱信号特征提取效果并不理想。为此,提出了适用于强噪声背景的小波相关滤波滚动轴承早期故障诊断方法。该方法将小波相关滤波降噪方法和Hilbert包络细化谱分析相结合:对被测信号进行小波相关滤波降噪处理,对降噪处理后的高频段尺度域的小波系数进行Hilbert包络细化谱分析。该方法在滚动轴承的早期故障诊断中的试验结果表明,该方法与直接小波系数包络谱诊断方法相比,较大地增强了对滚动轴承早期故障诊断的能力,在强噪声背景下有效地提取出滚动轴承的早期故障频率。

Existing methods for diagnosing rolling bearing faults by wavelet analysis are usually not very ideal for picking up weak signal characteristics of incipient fault under the condition of strong noise. We present a wavelet correlation filter for diagnosing the incipient faults of a rolling bearing. Measured signal is first de-noised by a wavelet correlation filter. Then we carry out Hilbert envelope spectrum analysis of de-noised wavelet coefficients of high scales which represent high frequency signal. The experimental results reveal that this method considerably improves the capability of feature extraction and incipient fault diagnosis for rolling bearing compared with the method of wavelet coefficients envelope spectrum.