动态广义主成分分析及其在故障子空间建模中的应用

冯晓伟; 许剑锋; 何川

doi:10.11959/j.issn.1000-436x.2022091

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动态广义主成分分析及其在故障子空间建模中的应用

学术论文 | 更新时间：2024-06-05

- 动态广义主成分分析及其在故障子空间建模中的应用
- Dynamic generalized principal component analysis with applications to fault subspace modeling
- 通信学报 2022年43卷第5期页码：92-101
- 作者机构：
  
  火箭军工程大学，陕西西安 710025
- 作者简介：
  
  [ "冯晓伟（1986- ），男，四川绵阳人，博士，火箭军工程大学讲师，主要研究方向为随机信号处理、工业过程监控技术等" ]
  [ "许剑锋（1977- ），男，河北石家庄人，博士，火箭军工程大学副教授、硕士生导师，主要研究方向为雷达信号处理技术" ]
  [ "何川（1985- ），男，山东莱芜人，博士，火箭军工程大学副教授、博士生导师，主要研究方向为信号处理技术" ]
- 基金信息：
  
  国家自然科学基金资助项目(61903375);国家自然科学基金资助项目(61773389);中国博士后科学基金资助项目(2019M663635);陕西省自然科学基金资助项目(2020JQ-298);陕西省青年科技新星基金资助项目(2021KJXX-22)
- DOI：10.11959/j.issn.1000-436x.2022091
  中图分类号： TP277
- 网络出版日期：2022-05，
  
  纸质出版日期：2022-05-25
- 稿件说明：
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冯晓伟, 许剑锋, 何川. 动态广义主成分分析及其在故障子空间建模中的应用[J]. 通信学报, 2022,43(5):92-101.

Xiaofeng FENG, Jianfeng XU, Chuan HE. Dynamic generalized principal component analysis with applications to fault subspace modeling[J]. Journal on communications, 2022, 43(5): 92-101.
冯晓伟, 许剑锋, 何川. 动态广义主成分分析及其在故障子空间建模中的应用[J]. 通信学报, 2022,43(5):92-101. DOI： 10.11959/j.issn.1000-436x.2022091.

Xiaofeng FENG, Jianfeng XU, Chuan HE. Dynamic generalized principal component analysis with applications to fault subspace modeling[J]. Journal on communications, 2022, 43(5): 92-101. DOI： 10.11959/j.issn.1000-436x.2022091.

摘要

针对传统故障子空间建模方法未考虑故障数据中同时包含正常工况信息和故障工况信息的实际情况，或未考虑故障数据中的动态因素而导致的对故障子空间提取不够准确的问题，提出了一种动态广义主成分分析方法。通过将带延迟的输入数据进行空间重组，采用广义主成分分析方法提取正常工况和各故障工况之间的动态特征信息，实现对故障子空间的准确建模，并进一步建立故障库实现故障诊断。仿真结果表明，所提方法能够准确提取动态过程的故障子空间，并可用于动态工业过程的故障诊断。

Abstract

In order to solve the problem of inaccurate modeling of fault subspace

traditional fault subspace modeling method did not consider the fact that fault data contain both normal and fault condition information

or did not consider the dynamic factors in the fault data

these flaws may lead to the case that the fault subspace cannot be extracted accurately

a dynamic generalized principal component analysis (DGPCA) method was proposed.By reorganizing the lagged input data

the dynamic characteristics between normal and fault data were extracted by the proposed DGPCA method

and then the fault subspaces could be modeled for further fault diagnosis.Finally

simulation results confirm the availability of the proposed method for fault subspace modeling and fault diagnosis.

关键词

Keywords

references

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