The Design and Performance of the Multivariate Synthetic-T2Control Chart

DOI10.1080/03610920802178413zbMath1292.62148OpenAlexW2157909416MaRDI QIDQ5495186

Marco A. de Luna, Francisco Aparisi

Publication date: 31 July 2014

Published in: Communications in Statistics - Theory and Methods (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1080/03610920802178413

zbMATH Keywords

quality controlmultivariateARLSPCsynthetic

Mathematics Subject Classification ID

Applications of statistics in engineering and industry; control charts (62P30)

Related Items (11)

Optimisation of a set of or principal components control charts using genetic algorithms ⋮ The Design of the Multivariate Synthetic Exponentially Weighted Moving Average Control Chart ⋮ Some Comments Regarding the Synthetic Chart ⋮ The economic design of multivariate binomial EWMA VSSI control charts ⋮ Economically Optimal Design of a Multivariate SyntheticT²Chart ⋮ The Synthetic Mean Square Error Control Chart ⋮ Adaptive Hotelling'sT²Control Charts with Run Rules ⋮ Design of a multivariate exponentially weighted moving average control chart with variable sampling intervals ⋮ Development of a multiattribute synthetic-np chart ⋮ Sensitivity Analyses of the Economic and Economic Statistical Designs of the Multivariate SyntheticT²Chart ⋮ A synthetic control chart for the coefficient of variation

Cites Work

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• Fast initial response features for EWMA control charts
• Optimization of univariate and multivariate exponentially weighted moving-average control charts using genetic algorithms
• Joint statistical design of double sampling \(\bar X\) and \(s\) charts
• Steady-state run length analysis of a shewhart quality control chart with supplementary runs rules
• A Multivariate Synthetic Control Chart for Monitoring Process Mean Vector
• Multivariate CUSUM Quality-Control Procedures
• A Multivariate Exponentially Weighted Moving Average Control Chart
• Hotelling's T²control chart with adaptive sample sizes
• Economic design of X charts with variable parameters: The Markov chain approach

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