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Journal ArticleDOI

Statistical Process Control of Multivariate Processes

John F. MacGregor, +1 more
- 01 May 1994 - 
- Vol. 3, Iss: 3, pp 427-437
TLDR
An overview of multivariate statistical methods use for the statistical process control of both continuous and batch multivariate processes and examples are provided of their use for analysing the operations of a mineral processing plant, for on-line monitoring and fault diagnosis of a continuous polymerization process and for the on- line monitoring of an industrial batch polymerization reactor.
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This article is published in Control Engineering Practice.The article was published on 1994-05-01. It has received 1174 citations till now. The article focuses on the topics: Statistical process control & Univariate.

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Journal ArticleDOI

A Review of Process Fault Detection and Diagnosis Part I : Quantitative Model-Based Methods

TL;DR: This three part series of papers is to provide a systematic and comparative study of various diagnostic methods from different perspectives and broadly classify fault diagnosis methods into three general categories and review them in three parts.
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A review of process fault detection and diagnosis: Part III: Process history based methods

TL;DR: This final part discusses fault diagnosis methods that are based on historic process knowledge that need to be addressed for the successful design and implementation of practical intelligent supervisory control systems for the process industries.
Journal ArticleDOI

The Mahalanobis distance

TL;DR: The Mahalanobis distance, in the original and principal component (PC) space, will be examined and interpreted in relation with the Euclidean distance (ED).
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.
References
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Book

Principal Component Analysis

TL;DR: In this article, the authors present a graphical representation of data using Principal Component Analysis (PCA) for time series and other non-independent data, as well as a generalization and adaptation of principal component analysis.
Journal ArticleDOI

Partial least-squares regression: a tutorial

TL;DR: In this paper, a tutorial on the Partial Least Squares (PLS) regression method is provided, and an algorithm for a predictive PLS and some practical hints for its use are given.
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.