论文部分内容阅读
为了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改进形态滤波方法——平均组合差值形态滤波算法(Average combination difference morphological filter,ACDIF),同时与固有时间尺度分解(Intrinsic time scale decomposition,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(Proper rotation,PR),再以峭度为准则筛选出含故障信息丰富的有效PR,对每个有效分量进行ACDIF滤波提取冲击成分然后信号重构,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,该方法能够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。
In order to extract the fault impact components from the vibration signals disturbed by harmonic and random noise, the four morphological operators are fused to propose an improved morphological filtering method - Average Combination Difference Morphological Filter (ACDIF) , Combined with Intrinsic time scale decomposition (ITD), and applied the ITD-ACDIF method to the fault diagnosis of the rolling bearing. Firstly, the bearing vibration signal is subjected to ITD decomposition to obtain a series of rotation components (Proper rotation, PR), and then the effective PR with fault information is selected according to the kurtosis criterion. ACDIF filtering is performed on each effective component to extract the impact component and then the signal weight Finally, the fault features in the reconstructed signal are extracted by spectral analysis. Numerical simulation and bearing fault vibration test results show that this method can effectively filter out harmonic interference, extract the characteristics of impact failure under strong background noise, and realize the equipment fault diagnosis.