Fault Diagnosis of Gas Regulator Based on PCA-LDA-SVM

In view of the non-linear and non-stationary characteristics of fault signals of gas regulators, the fault diagnosis method based on principal component analysis PCA, LDA and SVM is applied to fault diagnosis of gas regulators. PCA and LDA are used to reduce the dimension of data. Firstly, PCA is used to reduce the noise contained in the fault data of gas voltage regulator, and LDA is used to reduce the dimension, so that the same type of data can be clustered and different types of data can be easily distinguished. Finally, the PCA- LDA - SVM algorithm of fault diagnosis accuracy and running time respectively compared with PCA - SVM algorithm and SVM algorithm, the results show that the fault diagnosis model based on PCA- LDA - SVM not only can effectively extract the gas regulator fault feature data sample, also can to accurate classification and identification of fault category, improve the efficiency of fault diagnosis.