基于半监督谱核聚类的转子系统故障诊断
Rotor System Fault Diagnosis Based on Semi-supervised Spectrum Kernel Clustering

针对机械系统故障诊断中对先验知识利用不足和在高维特征空间中诊断难的问题，提出了一种基于成对约束和通过约束准则构造核函数的半监督谱核聚类方法。首先，在训练集中利用先验知识建立约束点对，即属于同一聚类的must link点对不属于同一聚类的cannot link点对；其次，通过样本连接图的结构信息和约束点对信息设计核函数，计算出投影矩阵；最后，在投影空间中使用k means算法聚类。测试集的每个样本点找到在对应训练集中〖WTBX〗k〖WTBZ〗个近邻样本的投影值，计算局部投影矩阵，从而可以在线计算出每个新来样本的投影值。实验表明，该算法较相关比对算法聚类准确率更高，可以满足转子系统故障诊断的实际需要。
In light of the problem of inadequate use of prior knowledge and the difficulty of diagnosis in a high dimensional feature space, a semi-supervised spectrum kernel clustering method is proposed that combines pairwise constraint information and construction of kernel function through the constraint rules. First, using prior knowledge to construct the pairwise constraints, the kernel function can be designed by graph structure information and constraints information. Then, the projection matrix can be calculated. Finally, clustering can be operated in the projected space with a k-means algorithm. In the test set, every new sample can find k neighbor samples in the train set and the projected values of the k neighbor samples, and the local projection matrix can then be calculated. Thus, the projected values of each new sample can be calculated online. The experiments show that the proposed algorithm is more accurate in clustering than the comparative algorithms, and can meet the actual needs of mechanical fault diagnosis.