Journal ArticleDOI
The process chemometrics approach to process monitoring and fault detection
Barry M. Wise,Neal B. Gallagher +1 more
TLDR
The state-of-the-art of process chemometrics and current trends in research and applications are reviewed.About:
This article is published in Journal of Process Control.The article was published on 1996-12-01. It has received 844 citations till now. The article focuses on the topics: Chemometrics & Fault detection and isolation.read more
Citations
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Robust Model-Based Fault Diagnosis for Dynamic Systems
Jie Chen,Ron J. Patton +1 more
TL;DR: Robust Model-Based Fault Diagnosis for Dynamic Systems targets both newcomers who want to get into this subject, and experts who are concerned with fundamental issues and are also looking for inspiration for future research.
Journal ArticleDOI
Statistical process monitoring: basics and beyond
TL;DR: It is demonstrated that the reconstruction-based framework provides a convenient way for fault analysis, including fault detectability, reconstructability and identifiability conditions, resolving many theoretical issues in process monitoring.
Journal ArticleDOI
Survey on data-driven industrial process monitoring and diagnosis
TL;DR: A state-of-the-art review of the methods and applications of data-driven fault detection and diagnosis that have been developed over the last two decades are provided to draw attention from the systems and control community and the process control community.
Journal ArticleDOI
A comparison study of basic data-driven fault diagnosis and process monitoring methods on the benchmark Tennessee Eastman process
TL;DR: A comparison study on the basic data-driven methods for process monitoring and fault diagnosis (PM–FD) based on the original ideas, implementation conditions, off-line design and on-line computation algorithms as well as computation complexity are discussed in detail.
Journal ArticleDOI
Nonlinear process monitoring using kernel principal component analysis
TL;DR: In this article, a new nonlinear process monitoring technique based on kernel principal component analysis (KPCA) is developed, which can efficiently compute principal components in high-dimensional feature spaces by means of integral operators and nonlinear kernel functions.
References
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System Identification: Theory for the User
TL;DR: Das Buch behandelt die Systemidentifizierung in dem theoretischen Bereich, der direkte Auswirkungen auf Verstaendnis and praktische Anwendung der verschiedenen Verfahren zur IdentifIZierung hat.
Book
Applied Regression Analysis
Norman R. Draper,Harry Smith +1 more
TL;DR: In this article, the Straight Line Case is used to fit a straight line by least squares, and the Durbin-Watson Test is used for checking the straight line fit.
Journal ArticleDOI
Principal component analysis
TL;DR: Principal Component Analysis is a multivariate exploratory analysis method useful to separate systematic variation from noise and to define a space of reduced dimensions that preserve noise.
Book
Linear Optimal Control Systems
Huibert Kwakernaak,Raphael Sivan +1 more
TL;DR: In this article, the authors provide an excellent introduction to feedback control system design, including a theoretical approach that captures the essential issues and can be applied to a wide range of practical problems.
Book
A User's Guide to Principal Components
TL;DR: In this paper, the authors present a directory of Symbols and Definitions for PCA, as well as some classic examples of PCA applications, such as: linear models, regression PCA of predictor variables, and analysis of variance PCA for Response Variables.