A new online fault detection method based on PCA technique

被引：26

作者：

Jaffel, Ines ^{[1
]}

Taouali, Okba ^{[1
]}

Elaissi, Elyes ^{[1
]}

Messaoud, Hassani ^{[1
]}

机构：

[1] Ecole Natl Ingenieur Monastir, Unite Rech Automat Traitement Signal & Image ATSI, Monastir 5019, Tunisia

来源：

IMA JOURNAL OF MATHEMATICAL CONTROL AND INFORMATION | 2014年 / 31卷 / 04期

关键词：

fault detection; PCA; RPCA-FOP; SWPCA; FOP; eigenvalue decomposition; PRINCIPAL COMPONENT ANALYSIS; IDENTIFICATION; DIAGNOSIS; KPCA;

D O I：

10.1093/imamci/dnt025

中图分类号：

TP [自动化技术、计算机技术];

学科分类号：

0812 ;

摘要：

In this paper, we suggest an extension of a previous study in Recursive Singular Spectrum Analysis (RSSA) (Hongli & Hui-Jun (2012) Fault detection for Markovian jump systems with sensor saturations and randomly varying non-linearities. IEEE Trans. Circuits Syst. I: Regul. Pap., 59, 2354-2362) to an online method for fault detection. This extended method is based on first-order perturbation (FOP) theory where the eigenvalues and eigenvectors of the foregoing covariance matrix are updated taking into account the effect of new acquired data which are considered as perturbation in the actual covariance matrix. This proposed diagnosis method is entitled 'recursive principal component analysis based on FOP' (RPCA-FOP) and is compared with other PCA techniques existing in literature such as the conventional PCA and the sliding window PCA where the average computation time, the missed detection rate and the false alarm rate are evaluated for each method.

引用

下载

页码：487 / 499

页数：13

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