Immune Model-Based Fault Diagnosis

This paper presents an intelligent methodology for diagnosing incipient faults. In this fault diagnosis system, in order to enhance the immune algorithms performance, we propose the improved immune-based symbiotic a new evolutionary learning algorithm. This new evolutionary learning algorithm is based on a particle swarm optimization (PSO) technique to improve the mutation mechanism. The application of real-valued negative selection algorithms to simulated and real-world systems is considered. These algorithms deal with the self-nonself discrimination problem in immunity computing, where normal process behaviour is coded as the self and any deviations from normal behaviour is encoded as nonself. The performance of the proposed method is demonstrated using simulation data and compared with other methods. The classification results showed that the proposed method outperforms traditional PSO-based method.