Gower distance-based multivariate control charts for a mixture of continuous and categorical variables

Authors:
Gulanbaier Tuerhong;Seoung Bum Kim
Affiliations:
-;-
Venue:
Expert Systems with Applications: An International Journal
Year:
2014

Citing 6
Cited 0

Improved kernel principal component analysis for fault detection

Expert Systems with Applications: An International Journal
A neural network ensemble-based model for on-line monitoring and diagnosis of out-of-control signals in multivariate manufacturing processes

Expert Systems with Applications: An International Journal
Fault detection using robust multivariate control chart

Expert Systems with Applications: An International Journal
A proposed framework for control chart pattern recognition in multivariate process using artificial neural networks

Expert Systems with Applications: An International Journal
A new nonparametric EWMA Sign Control Chart

Expert Systems with Applications: An International Journal
Data mining model-based control charts for multivariate and autocorrelated processes

Expert Systems with Applications: An International Journal

Quantified Score

Hi-index	12.05

Visualization

Abstract

Processes characterized by high dimensional and mixture data challenge traditional statistical process control charts. In this study, we propose a multivariate control chart based on the Gower distance that can handle a mixture of continuous and categorical data. An extensive simulation study was conducted to examine the properties of the proposed control chart under various scenarios and compared it with some existing multivariate control charts. The simulation results revealed that the proposed control chart outperformed the existing charts when the number of categorical variables increases. Furthermore, we demonstrated the applicability and effectiveness of the proposed control charts through a real case study.