Artificial immune particle swarm optimization for fault diagnosis of mine hoist

This paper presents an intelligent methodology for diagnosing incipient faults in mine hoist. 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 Discrete Particle Swarm Optimization (DPSO) technique to improve the mutation mechanism. Also to solve the problem that exists in fault diagnosis based on the traditional method using distance discriminant function, an improved method based on immunity strategy with similarity measurement of principle component kernel is presented. The effectiveness of the DPSO based immune algorithms is demonstrated through the classification of the fault signals in mine hoist. Simulation results show that the new scheduling algorithm can deal with the uncertainty situation and be suitable for multi-faults diagnosis, compared to the traditional scheduling algorithms.