动态广义主成分分析及其在故障子空间建模中的应用

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

通信学报 ›› 2022, Vol. 43 ›› Issue (5): 92-101.doi: 10.11959/j.issn.1000-436x.2022091

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

火箭军工程大学，陕西西安 710025

修回日期:2022-03-27 出版日期:2022-05-25 发布日期:2022-05-01
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
基金资助:
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)

Dynamic generalized principal component analysis with applications to fault subspace modeling

Xiaofeng FENG, Jianfeng XU, Chuan HE

The Rocket Force University of Engineering, Xi’an 710025, China

Revised:2022-03-27 Online:2022-05-25 Published:2022-05-01
Supported by:
The National Natural Science Foundation of China(61903375);The National Natural Science Foundation of China(61773389);China Postdoctoral Science Foundation Project(2019M663635);The Natural Science Foundation of Shaanxi Province(2020JQ-298);The Young Science and Technology Nova of Shaanxi Province(2021KJXX-22)

摘要/Abstract

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。

关键词: 动态广义主成分分析, 故障子空间, 故障重构, 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.

Key words: dynamic generalized principal component analysis, fault subspace, fault reconstruction, fault diagnosis

中图分类号:

TP277

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.

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故障编号	故障子空间
故障1	Ξ=[10000]^T
故障2	Ξ=[01000]^T
故障3	Ξ=[00100]^T
故障4	Ξ=[00010]^T
故障5	Ξ=[00001]^T

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

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