Authors: Seoung Bum Kim; Weerawat Jitpitaklert; Victoria C.P. Chen; Jinpyo Lee; Sun-Kyoung Park
Addresses: School of Industrial Management Engineering, Korea University, Anam-Dong, Seongbuk-Gu, Seoul 136-713, Korea ' Department of Industrial and Manufacturing Systems Engineering, University of Texas at Arlington, Arlington, 76019, Texas, USA ' Department of Industrial and Manufacturing Systems Engineering, University of Texas at Arlington, Arlington, 76019, Texas, USA ' School of Business, Hongik University, 94 Wausan-ro, Mapo-gu, Seoul 121-791, Korea ' School of Business Administration, Hanyang Cyber University, Haengdang-Dong, Seongdong-Gu, Seoul 133-791, Korea
Abstract: Control charts have been widely recognised as important tools in system monitoring of abnormal behaviour and quality improvement. Traditional control charts have a major assumption that successive observations are uncorrelated and normally distributed. When this assumption is violated, the traditional control charts do not perform well, but instead show increased false alarm rates. In this study, we propose a data mining model adjustment control chart to address autocorrelation problems for cascade processes. The basic idea of the proposed control chart is to monitor the residuals obtained by data mining models. The data mining models used in this study include support vector regression and artificial neural networks. A simulation study was conducted to evaluate the performance of the proposed control chart and compare it with the standard regression adjustment control chart and the observations-based control chart in terms of average run length performance. The results showed that the proposed data mining model adjustment control charts yielded better performance than the two other methods considered in this study. [Received 8 December 2010; Revised 19 June 2011; Revised 9 September 2011; Accepted 29 November 2011]
Keywords: autocorrelated processes; cascade processes; data mining; model-based control charts; statistical process control; SPC; model adjustment; support vector regression; artificial neural networks; ANNs; simulation.
European Journal of Industrial Engineering, 2013 Vol.7 No.4, pp.442 - 455
Received: 08 May 2021
Accepted: 12 May 2021
Published online: 25 Jun 2013 *