[1]韩捷,吴彦召,陈磊,等.全矢-ARMA模型在机械振动强度预测研究的应用[J].郑州大学学报(工学版),2016,37(06):43-47.[doi:10.13705/j.issn.1671-6833.2016.06.024]
　Han Jie,Wu Yanzhao,Chen Lei,et al.Research on mechanical vibration intensity prediction based on FVS-ARMA model[J].Journal of Zhengzhou University (Engineering Science),2016,37(06):43-47.[doi:10.13705/j.issn.1671-6833.2016.06.024]
点击复制

全矢-ARMA模型在机械振动强度预测研究的应用()
分享到：

《郑州大学学报(工学版)》[ISSN:1671-6833/CN:41-1339/T]

卷:
37
期数:
2016年06期
页码:
43-47
栏目:
出版日期:
2016-11-30

文章信息/Info

Title:
Research on mechanical vibration intensity prediction based on FVS-ARMA model
作者:
韩捷; 吴彦召; 陈磊; 郝旺身; 张钱龙
郑州大学 振动工程研究所,河南 郑州,450001
Author(s):
Han Jie;  Wu Yanzhao;  Chen Lei;  Hao Wang;  Zhang Qianlong
Institute of Vibration Engineering, Zhengzhou University, Zhengzhou, Henan 450001
关键词:
Keywords:
DOI:
10.13705/j.issn.1671-6833.2016.06.024
文献标志码:
A
摘要:
单通道预测方法由于获取振动信息不完善，导致预测结果一致性差，从而不能很好地实现故障的预测。通过全矢谱获得的频谱结构具有唯一性的特点，能够很好地弥补单通道的不足，在此基础上，将时序预测方法ARMA模型与全矢谱技术相结合，提出了全矢-ARMA模型，并把该方法应用到机械振动强度预测研究中。实验表明，该方法预测结果与实际较吻合。
Abstract:
Considering incomplete vibration information that leading to poor consistency of predictive results, single-channel prediction method cannot realize accurate prediction of machine fault. While by obtaining spec-tral structure with unique characteristics, full vector spectrum ( FVS) can well make up for the deficiency of single-channel. Further the prediction method of FVS-ARMA model was proposed in this paper, which com-bined ARMA model with full vector spectrum technology. It was applied to predict the mechanical vibration strength. Experiments showed that prediction results of this method were identical to the practical effects.

相似文献/References:

[1]韩捷,张钱龙.全矢谱技术在WTDS实验台故障诊断中的应用[J].郑州大学学报(工学版),2017,38(01):74.[doi:10.13705/j.issn.1671-6833.2016.04.032]
　Han Jie,Zhang Qianlong.The Application of Full Vector Spectrum Technology in Wind Turbine Drivetrain Diagnostics Simulator[J].Journal of Zhengzhou University (Engineering Science),2017,38(06):74.[doi:10.13705/j.issn.1671-6833.2016.04.032]
[2]雷文平,宋圣霖,郝旺身,等.基于FV-FBE的滚动轴承故障诊断研究[J].郑州大学学报(工学版),2020,41(05):82.[doi:10.13705/j.issn.1671-6833.2020.03.020]
　LEI Wenping,SONG Shenglin,HAO Wangshen,et al.Fault Diagnosis of Rolling Bearing Based on FV-FBE[J].Journal of Zhengzhou University (Engineering Science),2020,41(06):82.[doi:10.13705/j.issn.1671-6833.2020.03.020]

更新日期/Last Update: