Results 241 to 250 of about 21,463,749 (293)

Combined Multivariate Statistical Process Control

IFAC Proceedings Volumes, 2004
Abstract Multivariate statistical process control (MSPC) based on principal component analysis (PCA) has been widely used in chemical processes. Recently, the use of independent component analysis (ICA) was proposed to improve monitoring performance. In the present work, a new method, referred to as combined MSPC (CMSPC).
Manabu Kano, Shouhei Tanaka, Shinji Hasebe, Iori Hashimoto, Hiromu Ohno   +4 more
openaire   +1 more source

Multistate multivariate statistical process control

Applied Stochastic Models in Business and Industry, 2018
AbstractFor high‐dimensional, autocorrelated, nonlinear, and nonstationary data, adaptive‐dynamic principal component analysis (AD‐PCA) has been shown to do as well or better than nonlinear dimension reduction methods in flagging outliers. In some engineered systems, designed features can create a known multistate scheme among multiple autocorrelated ...
Odom, Gabriel J., Newhart, Kathryn B., Cath, Tzahi Y., Hering, Amanda S.   +3 more
openaire   +3 more sources

Multivariate statistical process control with dynamic external analysis

Proceedings of the 41st SICE Annual Conference. SICE 2002., 2003
The practicability of statistical process control (SPC) is limited due to the assumption that a process is operated in a steady state. In this paper, external analysis is proposed to cope with changes in operating conditions such as load changes and grade transitions.
Kano, Manabu, Maruta, Hiroshi, Tanaka, Shouhei, Hasebe, Shinji, Hashimoto, Iori, Ohno, Hiromu   +5 more
openaire   +1 more source

A Multivariate Change-Point Model for Statistical Process Control

Technometrics, 2006
Multivariate statistical process control (SPC) carries out ongoing checks to ensure that a process is in control. These checks on the process are traditionally done by T2, multivariate cusum, and multivariate exponentially weighted moving average control charts.
K. D. Zamba, Douglas M. Hawkins
openaire   +2 more sources

Multivariate Statistical Process Control and Process Performance Monitoring

IFAC Proceedings Volumes, 1998
Abstract Multivariate Statistical Process Performance Monitoring (MSPPM) provides a diagnostic tool for the monitoring and detection of process malfunctions for continuous and batch manufacturing processes. This paper initially reviews the concept of process performance monitoring through an industrial application to a fluidised bed-reactor and a ...
E.B. Martin, A.J. Morris, C. Kiparissides   +2 more
openaire   +1 more source

Multivariate statistical process control for autocorrelated processes

International Journal of Production Research, 1996
Multivariate statistical process control is often used in chemical and process industries where autocorrelation is most prevalent. We present a realistic model that generates autocorrelation and crosscorrelation and provides a useful approach to characterizing process data.
openaire   +1 more source

Multivariate Statistical Process Control in Batch Process Monitoring

IFAC Proceedings Volumes, 1996
Abstract Batch processing was widely practised long before the advent of the modern chemical industry. A number of limitations have inhibited the success of batch monitoring:- the finite duration of a batch, the presence of significant non-linearities, the lack of on-line sensors for measuring quality variables, the absence of steady-state operation,
S. Albert, G.A. Montague, A.J. Morris, E.B. Martin   +3 more
openaire   +1 more source

Multivariate Statistical Process Control in Etching Process

ECS Transactions, 2010
The purposes of multivariate statistical process control (MSPC) are to enhance process ability by quickly detecting process abnormalities and pinpointing the specific cause of the fault. This paper presents the implementing of MSPC system in etching process including principal component analysis (PCA), and fault contributions from square prediction ...
openaire   +1 more source

Statistical quality control of multivariable continuous processes

Proceedings of 1994 American Control Conference - ACC '94, 2005
Multivariate statistical methods are utilized for developing statistical quality control techniques for multivariable continuous processes. The performance of univariate Shewhart charts, Hotelling's T/sup 2/ combined with univariate Shewhart charts, residuals based SPC techniques, and parameter based SPC techniques are compared. Control of mean droplet
A. Negiz, E.S. Lagergren, A. Cinar
openaire   +1 more source

Variable Selection for Multivariate Statistical Process Control

Journal of Quality Technology, 2010
A methodology is described for the selection of a subset of variables for measuring and monitoring in statistical process control. The two-stage approach requires fewer variables that must be measured, thereby reducing the time and cost associated with ..
Isabel González, Ismael Sánchez
openaire   +1 more source