基于MDP-SVM的过程多类型故障诊断

针对工业过程多类型故障诊断率低的问题, 提出一种边界判别投影(MDP)与支持向量机(SVM)相融合(MDP-SVM）的方法。边界判别投影常用于人脸识别领域，其可以将多类数据降维，获得不同类别清晰的边界。与主成分分析(PCA)和局部线性嵌入(LLE)算法相比，考虑了样本的局部结构和全局结构，避免了小样本问题。降维的数据通过SVM分类器进行类别判断，利用粒子群(PSO)算法得到最佳SVM分类器，实现故障诊断。仿真结果表明：相对于传统方法，本文所提方法故障识别准确率达到95.379%，而且可同时识别出多类故障。

Process Multi-type fault diagnosis based on MDP-SVM

To solve the problem of low diagnosis rate of multi-type faults in industrial processes, a method of boundary discriminant projection (MDP) and support vector machine (SVM) fusion (MDP-SVM) was proposed. Boundary discriminant projection is often used in the field of face recognition, which can reduce the dimensionality of multiple types of data to obtain clear boundaries of different categories. Compared with principal component analysis (PCA) and local linear embedding (LLE), the local and global structures of samples are considered and the problem of small samples is avoided. The classification of dimensionality reduction data is judged by SVM classifier, and the optimal SVM classifier is obtained by particle swarm optimization (PSO) algorithm to achieve fault diagnosis. The simulation results show that compared with the traditional method, the fault identification accuracy of the proposed method can reach 95.379%, and multiple faults can be identified simultaneously.