Fault diagnosis based on Particle Swarm Optimization by Artificial Immunisation Algorithm
Particle swarm optimization (PSO) is a new general machine-learning tool under the evolutionary algorithms (EAs) and gained lots of attention in various engineering applications, this paper presents an intelligent methodology for diagnosing incipient faults in mine hoist. Artificial immunisation algorithm (AIA) is used to optimise the parameters in PSO in this paper. The AIA is a new optimisation method based on the biologic immune principle of human being and other living beings. It can effectively avoid the premature convergence and guarantees the variety of solution. With the parameters optimised by AIA, the total capability of the PSO classifier is improved. The fault diagnosis of mine hosit shows that the PSO optimised by AIA can give higher recognition accuracy than the normal PSO.
fault diagnosis model artificial immune Particle swarm optimization
WANG Chu-Jiao XIA Shi-Xiong XUAN Hong-Peng
School of Computer Science and Technology China University of Mining and Technology Xu Zhou, China
国际会议
武汉
英文
1236-1239
2009-11-18（万方平台首次上网日期，不代表论文的发表时间）