基于时延自相关与变模态分解的故障诊断方法

Fault diagnosis procedure based on delayed autocorrelation and variational modality decomposition

在故障诊断领域,时延自相关已是一种重要的信号处理工具,而变模态分解则是新兴起的信号处理方法.文中利用时延自相关函数对信号进行降噪处理,再对提取的时延自相相关函数进行变模态分解,选择有效本征模态函数提取出故障频率.模拟仿真与故障实验结果表明:该方法更能有效地抑制噪声,凸显故障特征信息,在旋转机械故障诊断领域具有广泛的应用前景.

In the field of rotating machinery fault diagnosis, the delayed autocorrelation has been a kind of important while the variational processing means modality decomposition is a new and developing method for signal processing. The delayed autocorrelation function is used in this paper to conduct the denoising processing of the signal and then the variational modality decomposition of the extracted delayed autocorre-lation function is performed to select the effective eigen-modality function and extract the failure frequen- cy. It is shown by simulation and actual test of the failure that the method of fault diagnosis based on delayed autocorrelation function and variational modality decomposition will be able to restrain the noise effectively and display the characteristic information of the failure prominently, so that will have a wide- spread prospects of application in failure diagnosis of rotating machinery.