Artificial Immune Regulation (AIR) for Model-Based Fault Diagnosis

Abstract

In this paper, a novel approach to immune model-based fault diagnosis methodology for nonlinear systems is presented. An immune-model based fault diagnosis architecture including forward/inverse immune model identification, the residual generation, fault alarm concentration (FAC), and artificial immune regulation (AIR). In this work, the artificial immune regulation was developed to diagnose the failures. A two-link manipulator simulation was employed to validate the effectiveness and robustness of the diagnosis approach. The results show that it can detect and isolate actuator faults, sensor faults and system component faults simultaneously.