Overview of PCA-Based Statistical Process-Monitoring Methods for Time-Dependent, High-Dimensional Data

Some Current Directions in the Theory and Application of Statistical Process Monitoring

(2017) William H. Woodall et al.   JOURNAL OF QUALITY TECHNOLOGY

An Object-Oriented Framework for Robust Multivariate Analysis

(2015) Valentin Todorov et al.   Journal of Statistical Software

Fault detection in the Tennessee Eastman benchmark process using dynamic principal components analysis based on decorrelated residuals (DPCA-DR)

(2013) Tiago J. Rato et al.   CHEMOMETRICS AND INTELLIGENT LABORATORY SYSTEMS

Defining the structure of DPCA models and its impact on process monitoring and prediction activities

(2013) Tiago J. Rato et al.   CHEMOMETRICS AND INTELLIGENT LABORATORY SYSTEMS

Advantage of Using Decorrelated Residuals in Dynamic Principal Component Analysis for Monitoring Large-Scale Systems

(2013) Tiago J. Rato et al.   INDUSTRIAL & ENGINEERING CHEMISTRY RESEARCH

Recursive Fault Detection and Isolation Approaches of Time-Varying Processes

(2012) Lamiaa M. Elshenawy et al.   INDUSTRIAL & ENGINEERING CHEMISTRY RESEARCH

The Future of Quality Technology: From a Manufacturing to a Knowledge Economy & From Defects to Innovations

(2011) Søren Bisgaard   Quality Engineering

Adaptive process monitoring using efficient recursive PCA and moving window PCA algorithms

(2010) Jyh-Cheng Jeng   Journal of the Taiwan Institute of Chemical Engineers

Comparison of multivariate statistical methods for dynamic systems modeling

(2010) Susana Barceló et al.   QUALITY AND RELIABILITY ENGINEERING INTERNATIONAL

Principal component analysis for data containing outliers and missing elements

(2007) Sven Serneels et al.   COMPUTATIONAL STATISTICS & DATA ANALYSIS

Variable MWPCA for Adaptive Process Monitoring

(2007) Xiao Bin He et al.   INDUSTRIAL & ENGINEERING CHEMISTRY RESEARCH