Immune model-based fault diagnosis

Authors:
Guan-Chun Luh;Wei-Chong Cheng
Affiliations:
Department of Mechanical Engineering, Tatung University, Taipei, Taiwan, ROC;Department of Mechanical Engineering, Tatung University, Taipei, Taiwan, ROC
Venue:
Mathematics and Computers in Simulation
Year:
2005

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Abstract

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