Neural Network Aided Extended Kalman Filter for Fault Detection and Isolation in Nonlinear Control System

Abstract

This article describes the presentation of neural networks for fault detection and isolation in nonlinear systems. Model-based fault detection schemes must include residual generation. The process of residual generation for nonlinear systems is often difficult because of the size of the issue or the absence of a suitable model from which the residual can be generated. To generate residuals for fault detection, this paper develops and applies neural network-based methods for nonlinear systems. Fault Detection and Isolation (FDI) is critical in many industries to ensure the safe operation of a process. Faults detection and Identification (FDI) methods are proposed to identify the type, size, position, and time of the fault. FDI are distinguished by their robustness, rapid detection, and isolation of faults. This paper compares the effectiveness of extended Kalman filters and neural network-based fault diagnosis systems. According to simulation results, the method has a faster convergence rate and a more accurate identification result than the traditional EKF algorithm.