Subspace aided data-driven fault detection for LTI systems

Author

Chen Jiao ; Fang Huajing ; Liu Xiaoyong

Author_Institution

Sch. of Autom., Huazhong Univ. of Sci. & Technol., Wuhan, China

fYear

2015

fDate

23-25 May 2015

Firstpage

2712

Lastpage

2715

Abstract

This paper mainly solves the issue of subspace aided data-driven fault detection for LTI systems, identifying the residual generator without the specific model. By the input and output data, Akaike information criterion and singular value decomposition are used to determined the system order firstly and a comparison between them is made. Then based on a certain algorithm, we can get some useful subspace to construct a residual generator for fault detection effectively. Finally, we apply the theoretical method into simulation studies to show its feasibility and effectiveness.

Keywords

fault diagnosis; linear systems; singular value decomposition; Akaike information criterion; LTI systems; residual generator; singular value decomposition; subspace aided data-driven fault detection; system order; Fault detection; Fault diagnosis; Generators; Linear systems; Process control; Singular value decomposition; System identification; Akaike Information Criterion; Determination of System Order; Fault Detection; Subspace Identification Methods;

fLanguage

English

Publisher

ieee

Conference_Titel

Control and Decision Conference (CCDC), 2015 27th Chinese

Conference_Location

Qingdao

Print_ISBN

978-1-4799-7016-2

Type

conf

DOI

10.1109/CCDC.2015.7162391

Filename

7162391