[1]李 惠,陈蔚芳,商苏成.基于EEMD_BP网络的滚珠丝杠副故障模式识别[J].机械与电子,2018,(04):28-32.
　LI Hui,CHEN Weifang,SHANG Sucheng.Fault Pattern Recognition of Ball Screws Based on EEMD_BP Network[J].Machinery & Electronics,2018,(04):28-32.
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基于EEMD_BP网络的滚珠丝杠副故障模式识别
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《机械与电子》[ISSN:1001-2257/CN:52-1052/TH]

卷:
期数:
2018年04期
页码:
28-32
栏目:
机电一体化技术
出版日期:
2018-04-24

文章信息/Info

Title:
Fault Pattern Recognition of Ball Screws Based on EEMD_BP Network
文章编号:
1001-2257(2018)04-0028-05
作者:
李 惠; 陈蔚芳; 商苏成
(南京航空航天大学机电学院,江苏 南京 210016)
Author(s):
LI Hui; CHEN Weifang; SHANG Sucheng
(College of Mechanical and Electrical Engineering, Nanjing University of Aeronautics and Astronautics,Nanjing 210016, China)
关键词:
EEMD;  故障诊断;  BP神经网络;  滚珠丝杠副
Keywords:
EEMD;  fault diagnosis;  BP neural network;  ball screw
分类号:
TH17
文献标志码:
A
摘要:
针对在机械故障诊断领域,对信号的时频域处理分析提取特征值往往不能准确判断机械故障状态的问题。在对数控机床滚珠丝杠副振动信号研究中,提出了利用集合经验模态(EEMD)方法分析受到噪声干扰的3种不同状态的滚珠丝杠副振动信号。利用BP神经网络理论,以振动信号的时频域特征值及EEMD分解得到内禀模态函数(IFM)特征值作为输入,建立BP神经网络模型,并通过实验验证诊断网络模型的可靠性。
Abstract:
In the field of mechanical fault diagnosis, extracting eigenvalues from time-frequency analysis of signals cannot accurately determine the mechanical fault state. Therefore, in the study of vibration signal from ball screw pair of NC machine tools, three different states of ball screw vibration signals that are affected by noise are analyzed by using EEMD method. Based on the BP neural network theory, the time-frequency eigenvalues of the vibration signal and the IMF from EEMD analysis are used to establish the BP neural network of fault diagnostic recognition in ball screw. And the reliability of network model diagnosis is verified through experiments.

参考文献/References:

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备注/Memo

备注/Memo:
收稿日期:2017-12-15
基金项目:国家04科技重大专项(2015ZX04001002)
作者简介:李 惠(1993-),女,安徽合肥人,硕士研究生,主要从事信号采集处理方面研究; 陈蔚芳(1966-),女,江苏无锡人,教授,博士研究生导师,主要从事数控技术研究。
更新日期/Last Update: 2018-04-24