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

Abstract

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

CLC Number:

TP277

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.

Figures/Tables 9

References 26

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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

Dynamic generalized principal component analysis with applications to fault subspace modeling

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