陈果

个人信息Personal Information

教授 博士生导师

招生学科专业:
交通运输工程 -- 【招收博士、硕士研究生】 -- 民航学院
电子信息 -- 【招收博士、硕士研究生】 -- 民航学院
交通运输 -- 【招收博士、硕士研究生】 -- 民航学院
交通运输工程 -- 【招收硕士研究生】 -- 通用航空与飞行学院
交通运输 -- 【招收硕士研究生】 -- 通用航空与飞行学院

性别:男

学历:研究生(博士后)

学位:工学博士学位

所在单位:通用航空与飞行学院

联系方式:报考研究生咨询的同学请发送短信至:13851875041 或发送邮件至:cgnuaacca@163.com

电子邮箱:

扫描关注

论文成果

当前位置: 中文主页 >> 科学研究 >> 论文成果

Sharing pattern feature selection using multiple improved genetic algorithms and its application in bearing fault diagnosis

点击次数:

所属单位:民航学院

发表刊物:J. Mech. Sci. Technol.

摘要:In order to select the effective features or feature subsets and realize an intelligent diagnosis of aero engine rolling bearing faults, this paper presents a sharing pattern feature selection method using multiple improved genetic algorithms. Based on the simple genetic algorithm, a multiple-population improved genetic algorithm was proposed, which improves the speed and effect of algorithm and overcomes the shortcomings of local optima that simple genetic algorithm is easy to fall into. Because all populations regularly share and exchange their selecting features, the proposed algorithms can quickly dig up the current effective feature patterns, and then analyze and deal with the strong correlation between the feature patterns. This will not only give clear directions for the descendant evolution, but also help to achieve high accuracy feature selection, for, the features are highly distinctive. This multiple-population improved genetic algorithm was applied to rolling bearing fault feature selection and comparisons with other methods are carried out, which demonstrates the validity of sharing pattern feature selection method proposed. © 2019, The Korean Society of Mechanical Engineers and Springer-Verlag GmbH Germany, part of Springer Nature.

ISSN号:1738-494X

是否译文:

发表时间:2019-01-01

合写作者:Guan, Xiaoying

通讯作者:陈果