Fault Diagnosis Based on Piecewise Least Square Support Vector Machine

Abstract

In the process of beer fermentation, in order to establish the precise temperature sensor fault diagnosis model, on the basis of standard support vector machine (SVM), We proposed piecewise least square support vector machine method, first using fuzzy c-means clustering (FCM) of the sample of poly class analysis to divide fermentation stage and the establishment of local model.Then the least square support vector machine (LS SVM) method is used for modeling of various types of samples.The experimental results show that the model has a high accuracy in the process of temperature fault diagnosis of beer fermentation process.After comparison, the proposed method establishes the model’s generalization ability better than other SVM methods to build the model.