基于级联过完备字典稀疏表征的滚动轴承复合故障诊断方法

Compound faults diagnosis method of rolling bearing based on sparse representation of cascaded over complete dictionary

针对滚动轴承多故障特征相互耦合不易诊断的问题,提出了一种将级联过完备字典与以基追踪降噪问题为优化原则的特征符号搜索(FSS)算法相结合的轴承复合故障诊断方法。该方法根据轴承故障冲击响应信号的特点,以轴承阻尼二阶系统的单位冲击响应函数作为级联过完备字典基函数,并通过相关滤波法获取构建字典的参数。结合得到的字典,使用特征符号搜索算法对信号中不同类型的故障冲击成分进行稀疏分解并重构,进而实现各故障特征的分离和提取。轴承复合故障仿真信号和实测信号的分析结果表明,所提方法能有效地分离和提取出复合故障中的各类故障特征,同时对降噪具有极好的鲁棒性。

Aiming at the problem that multi fault features of rolling bearing are coupled and difficult to diagnose,a bearing compound faults diagnosis method is proposed,which combines cascaded over complete dictionary with feature sign search(FSS)algorithm based on the optimization principle of basis pursuit de-noising.According to the characteristics of the bearing fault impulse response signal,the unit impulse response function of the bearing damping second-order system is used as the basis function of the cascaded over complete dictionary,and the parameters of the dictionary are obtained by the correlation filtering method.Combining the obtained dictionary,using the feature sign search algorithm to sparsely decompose and reconstruct different types of fault impact components in the signal,and then realize the separation and extraction of each fault feature.The analysis results of the simulated and measured signals of bearing compound faults show that the proposed method can effectively separate and extract various fault features in compound faults,and at the same time has excellent robustness to noise reduction.