时延自相关和ITD相结合的故障诊断方法

Fault Diagnosis Based on Delayed Autocorrelation Function and Intrinsic Time-Scale Decomposition

根据自相关函数与本征时间尺度分解理论及各自优点,提出了时延自相关和本征时间尺度分解相结合的故障诊断方法。该方法首先利用振动信号的自相关函数进行降噪处理,然后对降噪后自相关信号进行本征时间尺度分解,得到若干个相互独立的固有旋转分量和一个趋势项之和,从这些分量中选取表征故障特征信息的固有旋转分量进行重构,在重构信号频域中提取出故障特征频率。工程实例结果表明,该方法能抑制噪声,有效地提取故障特征信息,从而准确判别故障类型,在旋转机械故障诊断领域将有广泛的应用前景。

A new method of fault diagnosis based on autocorrelation function and intrinsic time-scale decomposition is proposed by combining the characteristics of delayed correlation function and intrinsic time-scale decomposition.The fault signal is processed by delayed autocorrelation function,and intrinsic time-scale decomposition is applied to decompose the processing signal,then the components of the fault informations are selected and refactored through the proper rolation components,the frequency spectrums are extracted by the new filtering method.The simulation demonstrates that fault feature extraction based on delayed correlation function and intrinsic time-scale decomposition is efective in signal extracting and denoising,and could provides good prospects of application in failure analysis of rotating machinery.