Detection and classification mean-shifts in multi-attribute processes by artificial neural networks

DOI10.1080/00207540601039809zbMath1141.90412OpenAlexW2166866738MaRDI QIDQ3498880

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Publication date: 19 May 2008

Published in: International Journal of Production Research (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1080/00207540601039809

zbMATH Keywords

artificial neural networkprocess monitoringmultivariate processesmulti-attribute control chart

Mathematics Subject Classification ID

Production models (90B30)

Related Items (3)

Control charts for fraction nonconforming in a bivariate binomial process ⋮ Control chart for monitoring multivariate COM-Poisson attributes ⋮ Development of a multiattribute synthetic-np chart

Cites Work

• Multivariate CUSUM Quality-Control Procedures
• A Multivariate Exponentially Weighted Moving Average Control Chart
• A neural fuzzy control chart for detecting and classifying process mean shifts
• A neural network approach for the analysis of control chart patterns
• Control chart for multivariate attribute processes
• A two-stage neural network approach for process variance change detection and classification
• An integrated neural network approach for simultaneous monitoring of process mean and variance shifts a comparative study
• Quality Control Methods for Multivariate Binomial and Poisson Distributions

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