滚动轴承故障的谱相关特征分析

摘要

Vibration signals of rolling bearing contained deterministic components and random components, both of them reflected the failure information of the bearing. For qualitative diagnosis of bearing faults using random components less vibration signal data was needed,that increased the computational efficiency for cyclostational analysis. It was proposed herein logarithmic contour maps of spectral correlation density was used firstly to reveal the change of weak random components caused by bearing faults, and then to take the slice at a resonance frequency to extract the fault information. An analysis example with real bearing vibration signals shows that even in condition of lower frequency resolution, spectral correlation density can realize the demodulation of fault information, achieve the purpose of fault feature extraction, and improve the calculation efficiency.%滚动轴承的振动响应信号包含确定性成分和随机成分,两者都能反映轴承发生故障的信息.利用随机成分进行故障定性诊断,可以只使用较少的振动信号数据,计算效率高,有利于工程实际应用.针对轴承振动信号中随机成分能量较低、分布频率范围较宽的特点,采用对数谱相关函数灰值图反映信号中随机成分对循环平稳特性的影响,定性判断故障引起的谱相关函数中随机成分的变化,然后通过共振区切片进行故障解调分析,提取特征信息.通过实测正常轴承和内圈点蚀故障轴承振动信号的对比分析,表明即使在较低频率分辨率条件下,谱相关密度也能实现故障信息的解调,并可以提高计算效率.