Open AccessMULTIVARIATE CONTROL CHARTS BASED ON DATA DEPTH FOR SUBGROUP LOCATION AND SCALE - CASE STUDY
Author(s) -
Izabela D. Czabak-Górska
Publication year - 2018
Publication title -
cbu international conference proceedings ...
Language(s) - English
Resource type - Journals
eISSN - 1805-997X
pISSN - 1805-9961
DOI - 10.12955/cbup.v6.1292
Subject(s) - control chart , multivariate statistics , univariate , statistical process control , shewhart individuals control chart , quality (philosophy) , control (management) , computer science , multivariate analysis , \bar x and r chart , chart , process (computing) , process control , scale (ratio) , statistics , data mining , control limits , ewma chart , mathematics , artificial intelligence , machine learning , geography , philosophy , cartography , epistemology , operating system
The purpose of the article is to present a method for determining control charts, which allow to control few interrelated quality characteristics. Often, in production practice, there is a need to simultaneously control several interrelated quality characteristics. The use of univariate control charts, separately for each quality characteristics, may lead to inadequate corrective actions of a production process. In such situations, it is recommended to use multivariate control charts, for example, the T2 control chart. However, the use of this classic approach involves making complicated calculations. Therefore, the author of this paper suggests using multivariate control charts based on data depth proposed by Liu. In this paper, the author presented the idea and principles of the multivariate control chart based on data depth and then, using it to assess the statistical stability of the process in a manufacturing company, engaged in the production of window fittings.