基于形态滤波和Laplace小波的轴承故障诊断

Fault Diagnosis of Bearings Based on Morphological Filter and Laplace Wavelet

针对强噪声背景下,轴承故障冲击响应的提取易被周围噪声干扰的问题,提出了一种基于数学形态学滤波和Laplace小波的包络谱分析方法。首先通过形态学滤波来滤除信号中的复杂噪声,增强信号的冲击特征,然后采用Laplace小波相关滤波法提取信号的冲击响应,最后对提取的冲击相关系数进行包络谱分析,即可诊断出故障。该方法结合了数学形态滤波和Laplace小波两者的优点,可以准确地捕捉到强噪声下的故障脉冲。将该方法应用于轴承内圈、外圈的故障诊断,与传统包络谱分析方法的对比结果很好地验证了所提方法的有效性。

Under stronger noise background,the extraction of the impulse responses appearing in fault vibration signals was usually affected by noise around. Aiming at this issue,an envelopment analysis method was proposed to diagnose localized defects in bearings based on the mathematical morphological filtering and Laplace wavelet. Firstly,to filter the complex noises in the signals with morphological filter and to enhance the impact features of signals,then,the impulse responses were extracted by Laplace wavelet correlation filtering method. At last,the envelope spectrum analysis was carried out on the correlation coefficient of impulse responses. The advantages of morphological filter and Laplace wavelet were combined,which might accurately capture the fault pulses under strong noises. This method was applied to bearings fault diagnosis of the inner rings and outer rings. It is nice to verify the effectiveness of the proposed method by the results of the fault detection comparing with the traditional envelope spectrum analysis.