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Journal ArticleDOI

Process analysis, monitoring and diagnosis, using multivariate projection methods

TLDR
Applications are provided on the analysis of historical data from the catalytic cracking section of a large petroleum refinery, on the monitoring and diagnosis of a continuous polymerization process and on the Monitoring of an industrial batch process.
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This article is published in Chemometrics and Intelligent Laboratory Systems.The article was published on 1995-04-01. It has received 702 citations till now. The article focuses on the topics: Statistical process control & Univariate.

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Citations
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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

The process chemometrics approach to process monitoring and fault detection

TL;DR: The state-of-the-art of process chemometrics and current trends in research and applications are reviewed.
Journal ArticleDOI

Generalized contribution plots in multivariate statistical process monitoring

TL;DR: Control limits for both types of contributions are introduced to show the relative importance of a contribution compared to the contributions of the corresponding process variables in the batches obtained under normal operating conditions.
BookDOI

Handbook of Water Analysis

TL;DR: In this paper, the authors present a sampling and data treatment method for water analysis, which is based on the analysis of water samples collected by the National Institute of Water and Environmental Sciences.
Journal ArticleDOI

Fault Detection Using the k-Nearest Neighbor Rule for Semiconductor Manufacturing Processes

TL;DR: In this paper, a fault detection method using the k-nearest neighbor rule (FD-kNN) is developed for the semiconductor industry, which makes decisions based on small local neighborhoods of similar batches, and is well suited for multimodal cases.
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

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.
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.