Fouling fault predict of steam turbine flow passage based on KPCA and LS-SVMR

This paper first provides a method for predicting fouling faults about flow passage of steam turbine based on kernel principal component analysis(KPCA) and least square support vector machine regression (LS-SVMR). First, KPCA is used to extract main features independent for each other from a lot of relaticve fault feature data. Afterwards, a model is established for predicting the trend of each main feature based on LS-SVMR in order to restruct feature vectors of fault classification. And then some typical fouling faults of steam turbine flow passage are identified by using SVM. Experimental results showed that the proposed method could effectively and efficiently forecast delitescent faults and typical fouling fault genres for the flow passage.