基于小波去噪和Hilbert-Huang变换在齿轮箱故障诊断中的应用

The Application of Fault Diagnosis on Gearbox Based on Wavelet Denoising and Hilbert-Huang Transform

为了从混有大量噪声的信号中准确地提取故障信息,利用小波去噪和Hilbert-Huang变换相结合的方法。首先利用小波去噪作为信号的预处理,通过经验模式分解和Hilbert变换求得边际谱,确定故障的特征。该方法克服了直接运用经验模式分解方法由于大量噪声带来的不必要的干扰,更好地辨识出故障的局部特征信息。通过实例分析表明了该方法的有效性和实用性。

In order to extract fault information accurately from the signal with lots of noise,this paper utilizes a method with wavelet denoising and Hilbert-Huang transformation together.Firstly,we pre-process the signal using wavelet denoising,and then account the margin spectrum base on the empirical mode decomposition and Hilbert transformation,so the fault character information would be determined.Compared with that using empirical mode decomposition(EMD) directly,the method decreases the unnecessary noise influence upon later decomposition and identifies the local fault information better.According to the example analysis,it shows that the method is effective and practicability.