Multilayer Architecture Based on HMM and SVM for Fault Classification

Abstract

In order to solve the problems of current machine learning in fault diagnosing system of the chemical plants, a better and effective multilayer architecture model is used in this paper. Hidden Markov Model (HMM) is good at dealing with dynamic continuous data and Support Vector Machine (SVM) shows superior performance for classification, especially for limited samples. Combining their respective virtues, we propose a new multilayer architecture model to improve classification accuracy for a fault diagnosis example. The simulation result shows that this two level architecture framework combining HMM and SVM is better than the single HMM method in high classification accuracy with small training samples.