Results 261 to 270 of about 15,402,643 (297)

Monitoring of a thermoelectric power plant based on multivariate statistical process control

2016 IEEE Conference on Evolving and Adaptive Intelligent Systems (EAIS), 2016
Thermoelectric power plants have critical units, such as the boiler and the turbine-generator, which are complex multivariate systems. These units exhibit non-stationary behavior and multiple operational modes that imply constant changes of set points of key performance variables.
Joyce M. F. Fonseca, Bruno M. Sousa, Webber E. Aguiar, Anisio R. Braga, André P. Lemos, Hugo C. C. Michel, Carmela M. P. Braga   +6 more
openaire   +1 more source

Taking Multivariate Statistics out into Industry — Multivariate Statistical Progress Control and Process Performance Monitoring

1997
The paper describes how the multivariate statistical techniques of principal components analysis (PCA) and projection to latent structures (PLS) can contribute to comprehensive improvements in an industrial process. Univariate SPC has been widely applied in industry but it effectively only detects disturbances related to individual measurement sources (
Elaine B. Martin, A. Julian Morris
openaire   +1 more source

On-line HPLC combined with multivariate statistical process control for the monitoring of reactions

Analytica Chimica Acta, 2007
On-line high performance liquid chromatography is used to monitor a steady state reaction over 35.2 h, with 197 chromatograms recorded as the reaction progresses. For each chromatogram, peaks are detected, baseline corrected, aligned and integrated to provide a peak table consisting of the intensities of 19 peaks, two corresponding to the reactants ...
Zhu, L, Brereton, RG, Thompson, DR, Hopkins, PL, Escott, REA   +4 more
openaire   +3 more sources

Multiscale PCA with application to multivariate statistical process monitoring

AIChE Journal, 1998
AbstractMultiscale principal‐component analysis (MSPCA) combines the ability of PCA to decorrelate the variables by extracting a linear relationship with that of wavelet analysis to extract deterministic features and approximately decorrelate autocorrelated measurements. MSPCA computes the PCA of wavelet coefficients at each scale and then combines the
openaire   +1 more source

Adaptive Multivariate Statistical Process Control for Monitoring Time-Varying Processes

Industrial & Engineering Chemistry Research, 2006
An adaptive multivariate statistical process monitoring (MSPC) approach is described for the monitoring of a process with incurs operating condition changes. Samplewise and blockwise recursive formulas for updating a weighted mean and covariance matrix are derived.
Choi, SW, Martin, EB, Morris, AJ, Lee, IB   +3 more
openaire   +2 more sources

An ensemble framework based on multivariate statistical analysis for process monitoring

Expert Systems with Applications, 2022
Zhichao Li, Li Tian, Xuefeng Yan
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Nonparametric Threshold Estimation of Autocorrelated Statistics in Multivariate Statistical Process Monitoring

Journal of Chemometrics
ABSTRACT Multivariate statistical process monitoring is commonly used to detect abnormal process behavior in real time. Multiple process variables are monitored simultaneously, and alarms are issued when monitoring statistics exceed a predetermined threshold.
Taylor R. Grimm, Kathryn B. Newhart, Amanda S. Hering   +2 more
openaire   +1 more source

Improvement on Multivariate Statistical Process Monitoring Using Multi-scale ICA

2006
A multi-scale independent component analysis (ICA) approach is investigated for industrial process monitoring. By integrating the ability of wavelet on multi-scale analysis and that of ICA on extracting independent components for non-Gaussian process variables, the multivariate statistical monitoring techniques can obtain improved performance ...
Fei Liu 0001, Chang-Ying Wu
openaire   +2 more sources

Bayesian fault isolation in multivariate statistical process monitoring dimitry gorinevsky

Proceedings of the 2011 American Control Conference, 2011
Consider a set of multivariable input/output process data. Given a new observation we ask the following questions: is the new observation normal or abnormal? is one of the inputs or outputs abnormal (faulty) and which? Assuming a linear regression model of the process, the problem is solved through Bayesian hypothesis testing.
openaire   +2 more sources

Statistical Monitoring of Complex Multivariate Processes

2012
Uwe Kruger, Lei Xie
openaire   +1 more source