基于IITD和奇异值差分谱的滚动轴承故障诊断

Fault diagnosis of rolling bearing based on IITD and singular value difference spectrum

基于ITD方法的线性变换和Akima插值,提出了一种改进的固有时间尺度分解方法 (Improve Intrinsic Time-scale Decomposition,简称IITD),将该方法与奇异值差分谱相结合,实现了滚动轴承故障的精确诊断。首先通过IITD方法将非平稳的原始加速度振动信号分解成若干个平稳的固有旋转分量(Proper Rotation Component,简称PRC);然后挑选包含故障特征信息最丰富的PR分量作为主PR分量;构造主PR分量的Hankel矩阵并进行奇异值分解,得到相应的奇异值差分谱,选择奇异值差分谱中的最大突变点来确定重构信号的奇异值个数,进而得到降噪后的主PR分量;最后对降噪后的主PR分量进行包络解调分析,提取滚动轴承的故障特征。实例分析表明,相比传统包络谱分析和基于经验模态分解和奇异值差分谱的方法,该方法能更有效地提取出滚动轴承的故障特征。

An improved intrinsic time-scale decomposition（IITD） was proposed based on the linear transformation of ITD method and Akima interpolation. Combining this method and singular value difference spectrum, the fault of the rolling bearing was accurately diagnosed. Firstly the original acceleration vibration signal was decomposed by IITD to several stable proper rotation components（PRC）; and then, the PRC containing the most fault characteristic information was chosen as the main PRC, Hankel matrix was constructed by the main PRC, and singular value difference spectrum was obtained after decomposition of singular values, the maximum catastrophe point was applied to identify the number of singular values of reconstructed signal, then the denoised PRC was obtained; finally, the denoised PRC was demodulated by Hilbert transformation to extract the fault features of the rolling bearing. Practical examples showed that the proposed diagnosis approach more effectively extracted the fault features of the rolling bearing compared with the traditional envelope spectrum analysis and the diagnosis method based on EMD and singular value difference spectrum.