一种改进的动态核主元分析故障检测方法

翟坤; 杜文霞; 吕锋; 辛涛; 句希源

doi:10.11949/j.issn.0438-1157.20181411

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一种改进的动态核主元分析故障检测方法

过程系统工程 | 更新时间：2026-07-24

- 一种改进的动态核主元分析故障检测方法
- Fault detect method based on improved dynamic kernel principal component analysis
- 化工学报 2019年70卷第2期页码：716-722
- 作者机构：
  
  1.河北师范大学物理科学与信息工程学院，河北石家庄 050024
  2.河北师范大学职业技术学院，河北石家庄 050024
- 作者简介：
  
  翟坤（1991—），男，硕士研究生，826540060@qq.com
  杜文霞（1973—），女，博士，副教授，dwx20040513@163.com
- 基金信息：
  
  国家自然科学基金项目(61673160);河北省自然科学基金项目(F2018205102);河北省教育厅重点基金项目(ZD2016053);河北省教育厅青年基金项目(QN2018087)
- DOI：10.11949/j.issn.0438-1157.20181411
  中图分类号： TP 206
- 收稿：2018-11-26，
  
  修回：2018-12-05，
  
  网络首发：2020-06-16，
  
  纸质出版：2019-02-05
- 稿件说明：
移动端阅览
翟坤, 杜文霞, 吕锋, 辛涛, 句希源. 一种改进的动态核主元分析故障检测方法[J]. 化工学报, 2019, 70(2): 716-722

ZHAI Kun, DU Wenxia, LYU Feng, XIN Tao, JU Xiyuan. Fault detect method based on improved dynamic kernel principal component analysis[J]. CIESC Journal, 2019, 70(2): 716-722
翟坤, 杜文霞, 吕锋, 辛涛, 句希源. 一种改进的动态核主元分析故障检测方法[J]. 化工学报, 2019, 70(2): 716-722 DOI： 10.11949/j.issn.0438-1157.20181411.

ZHAI Kun, DU Wenxia, LYU Feng, XIN Tao, JU Xiyuan. Fault detect method based on improved dynamic kernel principal component analysis[J]. CIESC Journal, 2019, 70(2): 716-722 DOI： 10.11949/j.issn.0438-1157.20181411.

摘要

针对复杂工业系统动态非线性故障检测过程精度低和计算量大的问题

提出了一种改进的动态核主元分析故障检测方法，该方法首先利用不可区分度剔除相关程度较小或者不相关变量

减少数据量，然后通过观测值扩展对筛选后的新数据构建增广矩阵，并对矩阵使用核主元分析提取变量数据的非线性空间相关特征，最后通过监测

和

SPE

两种统计量诊断出系统发生故障及识别故障变量。仿真实验证明，该方法能对风力发电机故障进行有效监测和诊断，与KPCA方法相比，改进的动态核主元分析方法对微小故障更为敏感。

Abstract

To solve the problems of low accuracy and large computation in dynamic non-linear detection process of complex industrial system

a fault detection method of improved dynamic kernel principal component analysis (IDKPCA) is proposed. First

the undistinguishable degree is used to eliminate the variables with low correlation degree or no correlation degree

so as to the amount of data is reduced

then the augmented matrix is constructed for the new data after screening by extending the observed value

the nonlinear spatial correlation characteristics of variable data is extracted by KPCA

finally monitoring statistics

and

SPE

are used to diagnose system failure and identify fault variables. Simulation experiment shows that this method can effectively monitor and diagnose the fault of wind turbine

and compared with KPCA method

the improved dynamic kernel principal component analysis method is more sensitive to minor faults.

关键词

Keywords

references

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