[1]李凌均,陈超,韩捷,等.全矢支持向量回归频谱预测方法[J].郑州大学学报(工学版),2016,37(03):78.[doi:10.13705/ j.issn.1671 -6833.2016.03.018]
　LI Lingjun,CHEN Chao,HAN Jie,et al.The Prediction Method of Frequency Spectrum Based on Full Vector Support Vector Regression[J].Journal of Zhengzhou University (Engineering Science),2016,37(03):78.[doi:10.13705/ j.issn.1671 -6833.2016.03.018]
点击复制

全矢支持向量回归频谱预测方法()
分享到：

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

卷:
37卷
期数:
2016年03期
页码:
78
栏目:
出版日期:
2016-05-10

文章信息/Info

Title:
The Prediction Method of Frequency Spectrum Based on Full Vector Support Vector Regression
作者:
李凌均; 陈超; 韩捷; 陈宏
1.郑州大学机械工程学院,河南郑州450001;2.河南机电职业学院,河南.郑州451191
Author(s):
LI Lingjun1; CHEN Chao1; 2; HAN Jie1; CHEN Hong1
1.Research Institute of Vibration Engineering, Zhengzhou University , Zhengzhou 450001 , China;2.Henan Mechanical andElectrical Vocational College , Zhengzhou 451191 , China
关键词:
全矢谱; 支持向量回归; 时间序列; 频谱预测
Keywords:
full vector spectrum; support vector regression;  time series;  frequency spectrum prediction
分类号:
TH17
DOI:
10.13705/ j.issn.1671 -6833.2016.03.018
文献标志码:
A
摘要:
为了对机械设备进行故障类型和故障部位的准确预测,提出了全矢支持向量回归的频谱预测新方法.该方法使用全矢谱信息融合技术对同源双通道信号进行信息融合,采用支持向量回归对全矢谱特征参数进行预测,保证了训练数据信息的全面性以及预测结果的准确性.该方法对振动信号的频谱结构分布情况进行准确预测,从而为对机组的故障类型和故障部位预测奠定技术基础.通过对某1000MW汽轮机轴振进行频谱预测验证结果表明,该方法在对振动信号频谱结构特征进行预测方面具有较高的预测准确性.
Abstract:
In order to predict the fault type and the fault position of rotating machinery more accurately, a newprediction method of frequency spectrum based on full vector support vector regression is proposed. The newmethod uses the full vector spectrum technology to merge the homologous double channel signal information,and uses support vector regression to predict the full vector spectrum characteristic parameters,which can en-sure the comprehensiveness of the training data and the accuracy of predict result. This method can forecastthe frequency spectrum of the vibration signal accuracy and can then give the technical based for fault type andfault position predict. The experimental results of the frequency spectrum forecast of a 1000 MW steam turbineshaft show that this method can predict fully and accurately the spectrum’s structure of vibration signal.

相似文献/References:

[1]郝伟,林辉翼,郝旺身,等.基于全矢稀疏编码的滚动轴承故障识别方法[J].郑州大学学报(工学版),2019,40(03):6.[doi:10.13705/j.issn.1671-6833.2018.03.007]
　Hao Wei,Lin Huiyi,Hao Wangshen,et al.Fault Recognition Method of Rolling Bearing Based on Full Vector Sparse Coding[J].Journal of Zhengzhou University (Engineering Science),2019,40(03):6.[doi:10.13705/j.issn.1671-6833.2018.03.007]

更新日期/Last Update: