Table of Contents
Journal of Quality and Reliability Engineering
Volume 2014, Article ID 239861, 9 pages
http://dx.doi.org/10.1155/2014/239861
Research Article

A One-Class Classification-Based Control Chart Using the -Means Data Description Algorithm

1LARODEC, ISG, University of Tunis, 41 Avenue de la Liberté, 2000 Bardo, Tunisia
2Dhofar University, P.O. Box 2509, 211 Salalah, Oman

Received 27 December 2013; Revised 23 April 2014; Accepted 7 May 2014; Published 9 June 2014

Academic Editor: Yi-Hung Chen

Copyright © 2014 Walid Gani and Mohamed Limam. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Abstract

This paper aims to enlarge the family of one-class classification-based control charts, referred to as OC-charts, and extend their applications. We propose a new OC-chart using the -means data description (KMDD) algorithm, referred to as KM-chart. The proposed KM-chart gives the minimum closed spherical boundary around the in-control process data. It measures the distance between the center of KMDD-based sphere and the new incoming sample to be monitored. Any sample having a distance greater than the radius of KMDD-based sphere is considered as an out-of-control sample. Phase I and II analysis of KM-chart was evaluated through a real industrial application. In a comparative study based on the average run length (ARL) criterion, KM-chart was compared with the kernel-distance based control chart, referred to as K-chart, and the -nearest neighbor data description-based control chart, referred to as KNN-chart. Results revealed that, in terms of ARL, KM-chart performed better than KNN-chart in detecting small shifts in mean vector. Furthermore, the paper provides the MATLAB code for KM-chart, developed by the authors.