基于可视图谱信号特征提取的滚动轴承故障诊断

Fault diagnosis of rolling bearing based on visual spectrum signal feature extraction

针对滚动轴承传统的故障特征提取方法易受外界的噪声干扰且包含大量的冗余信息的问题,提出一种可视图图谱信号特征提取的故障诊断方法。首先将振动信号转换成可视图图谱信号,计算每个可视图图谱信号的邻接矩阵与拉普拉斯矩阵,得到各种图谱指标,并采用双样本Z值方法筛选出合适故障特征作为故障特征向量,最后通过支持向量机分类算法得到轴承故障诊断分类结果。通过试验分析表明,与传统的故障特征提取方法比较,针对滚动轴承不同类型的故障诊断,采用基于可视图图谱信号的故障特征提取方法准确率提高了8.34%;为进一步证明该方法,针对滚动轴承外圈不同程度的故障诊断,该方法准确率提高了16.67%,更加表明基于可视图谱信号特征提取方法的优越性。

Aiming at the problem that the traditional fault feature extraction method of rolling bearing is easy to be disturbed by external noises and contains a lot of redundant information,a fault diagnosis method of feature extraction of visible spectrum signal was proposed.Firstly,the vibration signal is converted into viewable map signal,the adjacency matrix and Laplace matrix of each visual map signal are calculated,and various map indexes are obtained.The appropriate fault features are selected as the fault feature vector by using the double sample Z-value method.Finally,support vector machine(SVM)classification algorithm for bearing fault diagnosis classification results.The experimental analysis shows that,compared with the traditional fault feature extraction method,for different types of fault diagnosis of rolling bearing,the accuracy of fault feature extraction method based on visual map signal is improved by 8.34%;In order to further prove this method,for different degrees of fault diagnosis of the outer ring of rolling bearing,the accuracy of this method is improved by 16.67%,which shows the superiority of the signal feature extraction method based on visual spectrum.