基于ITD和改进形态滤波的滚动轴承故障诊断

为从受谐波和随机噪声干扰的振动信号中提取出故障冲击成分，融合四大基本形态学算子提出了改进形态滤波方法--平均组合差值形态滤波（ACDIF）方法，同时与固有时间尺度分解（ITD）相结合，并将ITD-ACDIF方法应用到滚动轴承的故障诊断中。首先，对轴承振动信号进行ITD分解得到一系列旋转分量（PRC）；然后，以峭度为准则筛选出含故障信息丰富的有效PRC，对每个有效分量进行ACDIF滤波提取冲击成分进行信号重构；最后，利用频谱分析提取重构信号中的故障特征。数值仿真和轴承故障振动信号的试验结果表明，本文方法可有效滤除谐波干扰，提取强背景噪声下的冲击故障特征，实现设备的故障诊断。

Fault diagnosis for rolling bearing based on ITD and improved morphological filter

For extracting fault impulse components embedded in the vibration signal with much noise and harmonics,an improved morphological filter algorithm called average combination difference morphological fil-ter(ACDIF)is proposed on the basis of the four basic morphological operators.Then ACDIF is combined with the intrinsic time scale decomposition(ITD)and the ITD-ACDIF method is employed in the fault diagnosis for rolling bearing.In the ITD-ACDIF fault diagnosis method,ITD is applied to the original vibration signal and a series of proper rotation components(PRC)are obtained,and then the kurtosis is regarded as criterion to se-lect effective PR components which contain much fault-related information.After that,ACDIF filtering is per-formed on each effective PR in order to pick up bidirectional impulses,and filtered PRs are combined into a signal.Finally,the fault feature is extracted from reconstructed signal by amplitude spectrum.The experimen-tal results on simulated signal and actual bearing vibration signal demonstrate that the proposed method can ef -fectively suppress noise interference and extracting the characteristics of impact fault under the strong back -ground noise to realize fault diagnosis of equipment.