基于ITD-SVD和MOMEDA的故障特征提取方法

为提高滚动轴承故障诊断的准确性，提出一种基于固有时间尺度分解(ITD)、奇异值分解(SVD)和多点最优最小熵反褶积(MOMEDA)相结合的故障特征提取方法。首先，采用ITD分解故障振动信号，并构建基于峭度和相关系数的组合权重指标筛选准则，从而完成分量信号的筛选与重构。其次，对其进行SVD滤波降噪。最后，利用MOMEDA提取降噪后信号中的周期性冲击成分，并通过Hilbert包络谱分析得到诊断结果。经过实验数据分析，结果表明所提出的方法不仅能滤除噪声干扰，增强故障特征信息，而且能准确提取出故障特征。

A Fault Feature Extraction Method Based on ITD-SVD and MOMEDA

In order to improve the accuracy of rolling bearing fault diagnosis, a fault feature extraction method based on the combination of intrinsic time scale decomposition (ITD), singular value decomposition (SVD) and multipoint optimal minimum entropy deconvolution adjusted (MOMEDA) is proposed. Firstly, ITD is used to decompose the fault vibration signal, and the component signal is filtered and reconstructed according to the combination of kurtosis and correlation coefficient weight index screening criteria. Then the SVD filter is used to reduce the noise. Finally, the periodic impact components in the noise reduced signal are extracted by MOMEDA, and the diagnosis results are obtained by Hilbert envelope analysis. Through the analysis of experimental data, the results show that the proposed method can not only filter out noise interference, enhancing fault feature information, but also extract fault features accurately.