Fast Fault Classification Method Research of Aircraft Generator Rotating Rectifier Based on Extreme Learning Machine

被引：0

作者：

Cui J. ^{[1
]}

Tang J. ^{[1
]}

Zhang Z. ^{[1
]}

Gong C. ^{[1
]}

Wang L. ^{[1
]}

机构：

[1] College of Automation Engineering, Nanjing University of Aeronautics and Astronautics, Nanjing, 211106, Jiangsu Province

来源：

| 2018年 / Chinese Society for Electrical Engineering卷 / 38期

基金：

中国国家自然科学基金;

关键词：

Aerospace generator; Extreme learning machine; Fault diagnosis; Mind evolutionary algorithm; Rotating rectifier;

D O I：

10.13334/j.0258-8013.pcsee.162334

中图分类号：

学科分类号：

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.

引用

页码：2458 / 2466

页数：8

共 29 条

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