点击次数：

所属单位：自动化学院

发表刊物：Zhongguo Dianji Gongcheng Xuebao

摘要：The aerospace generator is playing a more and more important role in the development of modern more-electric and all-electric aircraft. The reliability of important components of aircraft generator will be the focus in the future research. Focusing on the faults classification problem of aerospace generator rotating rectifier (AGRR), this investigation presented a fast classification technique based on extreme learning machine (ELM), improved with mind evolutionary algorithm (MEA). This technique utilized the MEA to optimize the parameters of ELM, and hence, an optimized model of ELM could be achieved and then, applied to rotating rectifier faults classification of aerospace generator. Simulation and experimental results showed that, the optimized ELM could achieve good diagnosis performance and high classification speed. Hence, the presented method can be considered to the application of aerospace generator rotating rectifier faults diagnosis and localization. © 2018 Chin. Soc. for Elec. Eng.

ISSN号：0258-8013

是否译文：否

发表时间：2018-04-20

合写作者：Tang, Junxiang,张卓然,龚春英,汪丽影

通讯作者：崔江