Affiliations: [a] Laboratory of Automatic Signal and Image Processing, National School of Engineers of Monastir, University of Monastir, Tunisia
| [b] Electrical and Computer Engineering Program, Texas A&M University at Qatar, Qatar
| [c] Department of Mathematical Sciences, Prince Sultan University, Riyadh, Saudi Arabia
| [d] Chemical Engineering Program, Texas A&M University at Qatar, Qatar
Correspondence:
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Corresponding author. Majdi Mansouri, Electrical and Computer Engineering Program, Texas A&M University at Qatar, Qatar. E-mail: majdi.mansouri@qatar.tamu.edu.
Abstract: In this paper, a novel fault detection and isolation (FDI) framework based on kernel PCA (KPCA) and generalized likelihood ratio test (GLRT) that is capable of detecting and identifying faults is developed. Specifically, three main objectives are addressed. First, system model identification and residuals generation are addressed using KPCA model. Second, KPCA-based GLRT method is proposed to detect different types of faults in the systems. Third, partial KPCA (PKPCA)-based GLRT is developed for fault isolation. The proposed approach aims to apply a structured PKPCA -based GLRT to a set of sub-models. The fault detection and isolation performances using PKPCA-based GLRT are illustrated through two examples: a simulated continuous stirred tank reactor (CSTR) data and an air quality monitoring network data. The obtained results demonstrate the effectiveness of the partial KPCA-based GLRT method over the partial PCA-based GLRT method.
Keywords: Partial kernel principal component analysis (PKPCA), fault detection and
isolation (FDI), generalized likelihood ratio test (GLRT), continuous stirred tank reactor (CSTR), air
quality monitoring networks (AQMN)
