Fault detection and isolation in hybrid process systems using a combined data-driven and observer-design methodology-Reference-Cited by-同舟云学术

Fault detection and isolation in hybrid process systems using a combined data-driven and observer-design methodology

Published:2014-04-30 Issue:8 Volume:60 Page:2805-2814
ISSN:0001-1541
Container-title:AIChE Journal
language:en
Short-container-title:AIChE J.

Author:

Tong Chudong¹²,El-Farra Nael H.²,Palazoglu Ahmet²,Yan Xuefeng¹

Affiliation:

1. Key Laboratory of Advanced Control and Optimization for Chemical Processes of Ministry of Education; East China University of Science and Technology; Shanghai 200237 P.R. China

2. Dept. of Chemical Engineering and Materials Science; University of California; Davis CA 95616

Publisher

Wiley

Subject

General Chemical Engineering,Environmental Engineering,Biotechnology

Link

http://onlinelibrary.wiley.com/wol1/doi/10.1002/aic.14475/fullpdf

Reference45 articles.

1. Statistical process monitoring: basics and beyond;Qin;J Chemom.,2003

2. A review of process fault detection and diagnosis: part I: quantitative model-based methods;Veenkatasubramanian;Comput Chem Eng.,2003

3. Model-based Fault Diagnosis in Dynamic Systems Using Identification Techniques

4. Statistical monitoring of dynamic multivariate process part 1: modeling autocorrelation and cross-correlation;Xie;Ind Eng Chem Res.,2006

5. Statistical and causal model-based approaches to fault detection and isolation;Yoon;AIChE J.,2000

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