Title: Process Control Using Predicted Quality Data


Raport Badawczy = Research Report ; RB/46/2013


Instytut Badań Systemowych. Polska Akademia Nauk ; Systems Research Institute. Polish Academy of Sciences

Place of publishing:



24 pages ; 21 cm ; Bibliography p. 24


SPC procedures are usually designed to control stability of directly ob­served parameters of a process. However, when quality parameters of interest are related to reliability characteristics it is practically hardly possible to monitor such characteristics directly. Instead, we use some training data in order to build a model that is used for the prediction of the value of an unobservable variable of inter­est basing on the values of observed explanatory variables. Such prediction models have been developed for normally distributed characteristics, both observable and unobservable. However, when reliability is concerned the random variables of in­terest are usually described by non-normal distributions, and their mutual depen­dence may be quite complicated. In the paper we consider the model of a process when traditionally applied assumptions are violated. We show that in such a case some non-statistical prediction models proposed in the area of data-mining, such as Quinlan’s C4.5 decision tree, perform better than popular linear prediction models. However, new problems have to be considered when shifts in the levels of process parameters may influence the performance of applied classification algorithms.


Raport Badawczy = Research Report

Detailed Resource Type:


Resource Identifier:






Language of abstract:



Creative Commons Attribution BY 4.0 license

Terms of use:

Copyright-protected material. [CC BY 4.0] May be used within the scope specified in Creative Commons Attribution BY 4.0 license, full text available at: ; -

Digitizing institution:

Systems Research Institute of the Polish Academy of Sciences

Original in:

Library of Systems Research Institute PAS

Projects co-financed by:

Operational Program Digital Poland, 2014-2020, Measure 2.3: Digital accessibility and usefulness of public sector information; funds from the European Regional Development Fund and national co-financing from the state budget.



Citation style:

This page uses 'cookies'. More information