[1]朱家远,张惠娟,杨忠,等.基于信号时频特性与GA-SVM的液压泵故障诊断方法[J].应用科技,2018,45(03):50-54.[doi:10.11991/yykj.201707004]
 ZHU Jiayuan,ZHANG Huijuan,YANG Zhong,et al.Fault diagnosis method of hydraulic pump based on time-frequency characteristic of signal and GA-SVM[J].yykj,2018,45(03):50-54.[doi:10.11991/yykj.201707004]
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基于信号时频特性与GA-SVM的液压泵故障诊断方法(/HTML)
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《应用科技》[ISSN:1009-671X/CN:23-1191/U]

卷:
第45卷
期数:
2018年03期
页码:
50-54
栏目:
自动化技术
出版日期:
2018-05-05

文章信息/Info

Title:
Fault diagnosis method of hydraulic pump based on time-frequency characteristic of signal and GA-SVM
作者:
朱家远1 张惠娟2 杨忠1 陈爽2 田瑶瑶1 张辉斌1
1. 南京航空航天大学 自动化学院, 江苏 南京 211106;
2. 航空机电综合航空科技重点实验室 电子工程部, 江苏 南京 211106
Author(s):
ZHU Jiayuan1 ZHANG Huijuan2 YANG Zhong1 CHEN Shuang2 TIAN Yaoyao1 ZHANG Huibin1
1. College of Automation Engineering, Nanjing University of Aeronautics and Astronautics, Nanjing 211106, China;
2. Electronic Engineering Department, Aviation Key Laboratory of Science and Technology on Aero Electromechanical System Integration, Nanjing 211106, China
关键词:
小波包分解小波包频带能量信号统计量支持向量机参数寻优遗传算法网格搜索法
Keywords:
wavelet packet decompositionwavelet packet band energysignal statisticssupport vector machineparameter optimizationgenetic algorithmgrid search method
分类号:
TP206.3
DOI:
10.11991/yykj.201707004
文献标志码:
A
摘要:
为提高液压泵故障诊断的准确率和速度,提出一种小波包频带能量结合信号时域统计量方差和均方根值的信号特性表示方法,以及一种用于寻找支持向量机最优惩罚因子和径向基核函数模型参数的实值编码遗传算法。实验结果表明这种信号特性表示方法能够很好地展示液压泵不同工作状态下的特征,使不同状态下的信号具有明显的区分度。通过与幂级数分格的网格搜索法对比,验证了实值编码的遗传算法能够有效且快速地找到支持向量机的最优参数。
Abstract:
To improve the accuracy and speed of hydraulic pump fault diagnosis, a signal characteristic representation method based on wavelet packet band energy, signal variance and mean square value, and a real-coded genetic algorithm used for seeking for the optimum penalty factor of support vector machine and the model parameter of the radical-base kernel function were proposed. The experimental results show that such signal characteristic representation method can reveal the characteristics of the hydraulic pump well under different working states, apparently distinguish the signals under different states. By comparison with the grid search method with power series of grid division, the optimal parameters of support vector machine can be found effectively and quickly by adopting the real-coded genetic algorithm.

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备注/Memo

备注/Memo:
收稿日期:2017-07-07。
基金项目:航空科学基金项目(20162852031);科技部重大科学仪器设备开发专项资助(2016YFF0103702);航空科学基金项目(2015ZF52067)
作者简介:朱家远(1993-),男,硕士研究生;杨忠(1969-),男,教授,博士生导师
通讯作者:杨忠,E-mail:YangZhong@nuaa.edu.cn
更新日期/Last Update: 2018-06-14