轴承故障的全视角特征提取与模式诊断方法

Whole view feature extraction and mode diagnosis method for bearing faults

为了提高轴承故障诊断的准确率,提出了一种轴承故障的全视角特征提取方法和专家森林算法的模式诊断方法。在故障特征提取方面,首先从时域、频域、时频域选择参数,以此来作为初始故障特征库,而后使用KPCA提取了基础故障库的全局结构特征,使用t-SNE算法提取了基础故障库的局部结构特征,从而保留了对故障模式相对敏感的全视角特征参数;在故障模式识别方面,为决策树赋予了专家属性和专家权值,得到了专家树的概念,基于专家树思想提出了专家森林算法,解决了随机森林算法无差别对待决策树的问题;最后采用实验的方式,对轴承故障全视角特征提取方法和基于专家森林算法的模式诊断方法进行了验证。研究结果表明:由KPCA+t-SNE结合提取的全视角故障特征优于单独提取的全局结构特征与局部结构特征;随机森林算法的诊断准确率均值为96.14%,专家森林算法的故障诊断准确率均值为99.48%,比随机森林算法提高了3.47%,验证了所提故障诊断方法的优越性。

In order to improve the accuracy of bearing fault diagnosis,the full view feature extraction method of bearing fault and the pattern diagnosis method of expert forest algorithm were proposed.Firstly,in terms of fault feature extraction,parameters from time domain,frequency domain and time-frequency domain were selected as the initial fault feature library.Then the global structural features of the basic fault library were extracted using KPCA,and the local structural features of the basic fault library were extracted using t-SNE algorithm,so as to retain the full view feature parameters that were relatively sensitive to fault modes.In the aspect of fault pattern recognition,expert attributes and expert weights were given to the decision tree,and the concept of expert tree was obtained.Based on the idea of expert tree,an expert forest algorithm was proposed,which solves the problem that the random forest algorithm treats the decision tree indiscriminately.Finally,experiments were used to verify the full-view feature extraction method of bearing faults and the mode diagnosis method based on the expert forest algorithm.The experimental results show that the full view fault features extracted by KPCA+t-SNE are better than the global and local structure features extracted separately;the average diagnosis accuracy of random forest algorithm is 96.14%,and the average fault diagnosis accuracy of expert forest algorithm is 99.48%,which is 3.47%higher than that of random forest algorithm,and verifies the superiority of the proposed fault diagnosis method.