基于TVD和MSB的滚动轴承故障特征提取

Fault feature extraction of rolling element bearings based on TVD and MSB

针对滚动轴承故障冲击信号周期性强且易被背景噪声淹没的特点,提出一种基于二阶全变分去噪(TVD)与调制信号双谱(MSB)的滚动轴承故障诊断方法。该方法利用二阶TVD处理故障信号,包络谱相关峭度被用于确定滤波过程中的参数λ;为进一步抑制噪声干扰,对滤波后信号进行MSB分析,选取故障特征频率占比最大的5个切片进行平均得到复合切片谱,提取出轴承故障特征;通过分析复合切片谱,判断轴承故障类型。将该方法运用到轴承故障仿真和实验信号。结果表明,所提出的方法能够有效抑制强背景噪声的干扰,实现了滚动轴承的故障诊断,证明了方法的有效性。

To solve the problem that the fault features of rolling element bearings are periodic but weak which usually are submerged in strong background noise, a fault diagnosis method based on the second order total variation denoising and modulation signal bispectrum was proposed. Firstly, TVD was applied to original vibration signal while the correlation kurtosis based on envelope spectrum was used as an index to select the optimal parameter λ. Then, the MSB was used to analyze the filtered signal to further suppress the interference of noise, the compound slice spectrum was composed by five selected slices based on index p . Finally, by analyzing the compound slice, the type of bearing fault was determined. The proposed method was applied in simulated and experimental fault signals of rolling element bearings. The results show that the method can reduce the effect of noise to realize accurate diagnosis of bearings’ faults.