A multivariate sign chart for monitoring dependence among mixed-type data

Research output: Journal Publications and Reviews (RGC: 21, 22, 62)21_Publication in refereed journal

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Author(s)

  • Junjie Wang
  • Qin Su
  • Yue Fang
  • Pengwei Zhang

Related Research Unit(s)

Detail(s)

Original languageEnglish
Pages (from-to)625-636
Journal / PublicationComputers and Industrial Engineering
Volume126
Online published4 Oct 2018
Publication statusPublished - Dec 2018

Abstract

Statistical process control (SPC) has been widely utilised for quality improvement and surveillance in industrial engineering. Modern industrial applications have witnessed more and more mixed-type quality characteristics such as those consisting of ordinal categorical and continuous ones. However, traditional charting techniques consider the dependence in either categorical or continuous data and hardly combine the two in quality control. Under the assumption that the ordinal attribute levels of a factor are determined by a latent continuous variable, there exists an order among categorical observations of this factor, which is similar to that among continuous observations. Then mixed-type observations can be transformed into a unified framework of standardized ranks, based on directions of which with respect to their centre parameter, the spatial-sign covariance matrix can be calculated for statistical surveillance of cross-dependence among mixed-type factors. The affine invariant property of consequent charting statistic helps improve the efficiency of detecting dependence shifts in mixed-type data. Simulation results demonstrate the superiority of proposed control chart and an additive manufacturing (3D printing) example shows that it can perform excellently well in practice.

Research Area(s)

  • Latent variable, Spatial sign, Standardized rank, Covariance matrix, Statistical process control

Citation Format(s)

A multivariate sign chart for monitoring dependence among mixed-type data. / Wang, Junjie; Su, Qin; Fang, Yue; Zhang, Pengwei.

In: Computers and Industrial Engineering, Vol. 126, 12.2018, p. 625-636.

Research output: Journal Publications and Reviews (RGC: 21, 22, 62)21_Publication in refereed journal