基于递归图和局部非负矩阵分解的轴承故障诊断

Bearing fault diagnosis based on recurrence plots and local non-negative matrix factorization

针对轴承振动信号的非平稳特征和现实中难以提取故障参数的情况,提出了一种基于递归图和局部非负矩阵分解的轴承故障诊断方法。该方法首先对采集到的轴承振动信号进行递归图分析,生成灰度图;然后用局部非负矩阵分解对生成的递归图进行特征参数提取,得到系数编码矩阵;最后采用分类器对上述编码矩阵直接进行模式识别,从而实现轴承故障的自动化诊断。将该方法应用在4种典型工况的轴承故障诊断实例中,应用结果表明,该方法可对不同工况的递归图自适应地计算特征参数,避免了人为因素对诊断准确率的影响,具有较好的自适应性和鲁棒性。

In view of non-stationary characteristics of bearing vibration signal and difficulty ofextracting fault parameters in reality,a bearing fault diagnosis based on recurrence plots and local nonnegative matrix factorization was proposed.Firstly,recurrence plots of the collected bearing vibration signal is analyzed and gray scale is generated.Then,characteristic parameters of the recurrence plots are extracted by the local non-negative matrix decomposition to obtain coefficient coding matrix.Finally,classifier is used for pattern recognition of coding matrix,so as to achieve automatic diagnosis of bearing failure.The method is applied to four kinds of typical bearing fault diagnosis cases,and the application results show that the method can calculate characteristic parameters adaptively for recurrence plots of different operating conditions and avoid influence of human factor on accuracy rate of diagnosis with better adaptivity and robustness.