摘要
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.
出处
《兰州理工大学学报》
CAS
北大核心
2017年第4期42-46,共5页
Journal of Lanzhou University of Technology
基金
国家自然科学基金(11372198)
河北省高等学校创新团队领军人才计划(LJRC018)
关键词
时延自相关
变模态分解
故障诊断
delayed autocorrelation
variational modality decomposition
fault diagnosis