LDA_SVM方法在化工过程故障诊断中的应用

Application of LDA and SVM method in fault diagnosis of chemical process

针对复杂化工生产过程数据多样性、高维性以及风险重复性的特点,结合网格搜索(GS)与K折交叉验证(K-CV)理论,提出一种基于线性判别分析(LDA)与支持向量机(SVM)相融合的故障诊断方法。首先利用LDA对正常工况和5类故障模式的混合运行数据进行矢量映射,压缩特征空间维度,抽取并重构故障特征信息。将预处理后的数据作为输入样本,利用GS与K-CV得到最佳SVM分类器,实现故障诊断。仿真结果表明,相对于单一SVM和PCA(主元分析)_SVM故障诊断模型,LDA与SVM融合故障诊断方法收敛速度快、诊断准确率高、模型健壮,对化工过程6种运行模式的故障识别准确率达到93.9%。

Based on the variety and high dimensionality of the data and also the characteristic of repetitive risks in complicated chemical processes,a fault diagnosis method based on linear discriminant analysis(LDA)and support vector machine(SVM)was proposed combined with the grid search(GS)and K-fold cross validation(K-CV)theory.In this method,LDA is used to map the normal operation and five types of fault data by vectorization,compressing the dimensions of the feature space,extracting and reconstructing the feature information.Subsequently,the optimal parameters of SVM model are established for the processed data by using GS and K-CV to diagnose faults.In this work,the introduced LDA_SVM mixed model is compared with SVM and PCA(principal component analysis)_SVM fault diagnosis models,where the new method proved to be superior with fast convergence,high recognition rates and robustness.In this work,it is also showed that accuracy of diagnosis results for the six types of running modes in chemical process has reached 93.9%for the proposed method.