A Study on a Fault Detection and Isolation Method of Nonlinear Systems using SVM and Neural Network

Abstract

Abstract:In this paper, we propose a fault diagnosis method using artificial neural network and SVM (Support Vector Machine) to detect and isolate faults in the nonlinear systems. The proposed algorithm consists of two main parts: fault detection through threshold testing using a artificial neural network and fault isolation by SVM fault classifier. In the proposed method a fault is detected when the errors between the actual system output and the artificial neural network nominal system output cross a predetermined threshold. Once a fault in the nonlinear system is detected the SVM fault classifier isolates the fault. The computer simulation results demonstrate the effectiveness of the proposed SVM and artificial neural network based fault diagnosis method. Keywords

Physiological Responses-Based Emotion Recognition Using Multi-Class SVM with RBF Kernel

“…four types of classification methods were employed to classify emotions after comparing the accuracy of these methods such as kNN (k-Nearest Neighbor), fuzzy-kNN, discriminant function analysis with linear (LDF), quadratic (QDF) kernels, and support vector machine (SVM) and so on [6]. Especially, SVMs are widely used for many purpose in various field [12]. The introductions of techniques among the above ones which are applied in this work are described as bellow.…”