基于VMD和自适应谱线增强技术的滚动轴承故障诊断

Fault Diagnosis for Rolling Bearings Based on VMD and Adaptive Line Enhancement Techniques

为了在噪声干扰下准确提取滚动轴承振动信号的故障特征,提出了一种将变分模态分解与自适应谱线增强技术相结合的轴承故障特征频率提取方法。首先采用VMD对原始振动信号进行分解和重构,然后通过自适应谱线增强技术对重构信号进行降噪处理,最后对降噪信号进行包络解调分析得到故障特征频率。利用滚动轴承仿真信号和实测信号检验了所提出的方法,并与VMD及小波分析+ALE方法进行对比分析,结果表明,VMD+ALE方法的滤波效果及检测精度更好,能够更加有效的提取轴承故障特征。

In order to extract accurately fault features in vibration signals of rolling bearings under noise interference,a fault feature frequency extraction method for the bearings is proposed based on variational mode decomposition(VMD)and adaptive line enhancement techniques.Firstly,the original vibration signals are decomposed and reconstructed by VMD.Then the adaptive line enhancement techniques are used to reduce noise of reconstructed signals.Finally,the envelope demodulation analysis is carried out for denoised signal to obtain fault feature frequency.The method presented is tested with the simulation signals and measured signals and compared with VMD and wavelet analysis plus ALE methods.The results show that the filtering effect and detection accuracy of VMD plus ALE method is better,and the fault features of the bearings are extracted more effectively.