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Control chart

About: Control chart is a research topic. Over the lifetime, 7640 publications have been published within this topic receiving 159688 citations. The topic is also known as: Shewhart chart & process-behavior chart.


Papers
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Book
01 Jan 1985
TL;DR: In this article, the authors present a survey of statistical process control and capability analysis techniques for improving the quality of a business process in the modern business environment, using a variety of techniques.
Abstract: Quality Improvement in the Modern Business Environment.STAISTICAL METHODS USEFUL IN QUALITY IMPROVEMENT.Modeling Process Quality.Inferences About Process Quality.BASIC METHODS OF STATISTICAL PROCESS CONTROL AND CAPABILITY ANALYSIS.Methods and Philosophy of Statistical Process Control.Control Charts for Variables.Control Charts for Attributes.Process and Measurement Systems System Capability Analysis.OTHER STATISTICAL PROCESS MONITORING AND CONTROL TECHNIQUES.Cumulative Sum and Exponentially Weighted Moving Average Control Charts.Other Univariate SPC Techniques.Multivariate Process Monitoring and Control.Engineering Process Control and SPC.PROCESS DESIGN AND IMPROVEMENT WITH DESIGNED EXPERIMENTS.Factorial and Fractional Factorial Designs for Process Design and Improvement.Process Optimization with Designed Experiments.ACCEPTANCE SAMPLING.Lot--by--Lot Acceptance Sampling for Attributes.Other Acceptance Sampling Techniques.Appendix.Bibliography.Answers to Selected Exercises.Index.

7,312 citations

Journal ArticleDOI
TL;DR: The problem of using time-varying trajectory data measured on many process variables over the finite duration of a batch process is considered and multiway principal-component analysis is used to compress the information contained in the data trajectories into low-dimensional spaces that describe the operation of past batches.
Abstract: The problem of using time-varying trajectory data measured on many process variables over the finite duration of a batch process is considered. Multiway principal-component analysis is used to compress the information contained in the data trajectories into low-dimensional spaces that describe the operation of past batches. This approach facilitates the analysis of operational and quality-control problems in past batches and allows for the development of multivariate statistical process control charts for on-line monitoring of the progress of new batches. Control limits for the proposed charts are developed using information from the historical reference distribution of past successful batches. The method is applied to data collected from an industrial batch polymerization reactor.

1,359 citations

Journal ArticleDOI
TL;DR: 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.

1,174 citations

Journal ArticleDOI
TL;DR: In this article, a multivariate extension of the exponentially weighted moving average (EWMA) control chart is presented, and guidelines given for designing this easy-to-implement multivariate procedure.
Abstract: A multivariate extension of the exponentially weighted moving average (EWMA) control chart is presented, and guidelines given for designing this easy-to-implement multivariate procedure. A comparison shows that the average run length (ARL) performance of this chart is similar to that of multivariate cumulative sum (CUSUM) control charts in detecting a shift in the mean vector of a multivariate normal distribution. As with the Hotelling's χ2 and multivariate CUSUM charts, the ARL performance of the multivariate EWMA chart depends on the underlying mean vector and covariance matrix only through the value of the noncentrality parameter. Worst-case scenarios show that Hotelling's χ2 charts should always be used in conjunction with multivariate CUSUM and EWMA charts to avoid potential inertia problems. Examples are given to illustrate the use of the proposed procedure.

1,174 citations

Book
01 Oct 1998
TL;DR: In this article, a statistical description of the quality of processes and measurements is given, and an introduction to Hypothesis Testing is given. But this is not a complete survey of the literature.
Abstract: Introduction. Statistical Description of the Quality of Processes and Measurements. The Normal Distribution. An Introduction to Hypothesis Testing. Some Important Hypothesis Tests. Analysis of Variance. Control Charts. Straight Line Regression and Calibration. Vectors and Matrices. Multiple and Polynomial Regression. Non-linear Regression. Robust Statistics. Internal Method Validation. Method Validation by Interlaboratory Studies. Other Distributions. The 2 2 Contingency Table. Principal Components. Information Theory. Fuzzy Methods. Process Modelling and Sampling. An Introduction to Experimental Design. Two-level Factorial Designs. Fractional Factorial Designs. Multi-level Designs. Mixture Designs. Other Optimization Methods. Genetic Algorithms and Other Global Search Strategies. Index.

928 citations


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Performance
Metrics
No. of papers in the topic in previous years
YearPapers
20241
2023256
2022473
2021396
2020391
2019359