基于增强最大二阶循环平稳盲解卷积的滚动轴承弱故障特征提取

Weak Fault Feature Extraction of Rolling Bearings Based on ECYCBD

提出一种基于增强最大二阶循环平稳盲解卷积(ECYCBD)的滚动轴承弱故障特征提取方法。该方法以故障信号自身特点为基础,设定故障特征频率和滤波器长度的选取范围;以重加权峭度值为适应度值,利用鲸鱼优化算法(WOA)优化选取最大二阶循环平稳盲解卷积(CYCBD)的参数,获取最优解卷积结果;通过Teager能量算子对故障特征进行增强,并借助快速傅里叶变换提取滚动轴承的故障特征。利用仿真和试验信号对方法的有效性进行验证,结果表明该方法能够在强背景噪声下准确、有效地提取滚动轴承故障冲击成分。

A weak fault feature extraction method for rolling bearings is proposed based on enhanced maximum second-order cyclostationary blind convolution(ECYCBD). Based on characteristics of fault signals, the selection range of fault feature frequency and filter length is set. Taking the reweighted kurtosis value as fitness value, the whale optimization algorithm(WOA) is used to optimally select the parameters of maximum second-order cyclostationary blind convolution(CYCBD), and the optimal deconvolution result is obtained. The fault feature is enhanced by Teager energy operator, and the fault feature of rolling bearings is extracted by means of fast Fourier transform. The simulation and test signals are used to verify the effectiveness of the method. The results show that the method extracts the impact components of rolling bearing faults under strong background noise accurately and effectively.