Review on some multivariate statistical process control methods for process monitoring

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

4 Citations (Scopus)

Abstract

Quality control and improvement have become one of important aspects in assuring production sustainability. Quality has been considered as one of competitive advantages for a company. One of main techniques in conducting quality control is control chart. Early development of this control chart was univariate. However, product quality can not be justified only by one product characteristic. Hence, multivariate statistical process control (MSPC) is necessary to be applied in order to monitor many product quality variables simultaneously. It is important to explore more accurate methods for MSPC modelling. Moreover, rapid development in process automation and computer technology has shifted quality control process, from product quality monitoring into process monitoring. Real time process monitoring for many process characteristics has been conducted to ensure optimal process. Automatic data acquisition has provided massive data stream: large and complex data. These phenomena have supported the application of data mining techniques for process monitoring. This paper focuses on reviewing previous literature related to MSPC, from traditional to data mining based methods. This study aims to synthesize previous literature in multivariate control charts for process monitoring, hence the area for future research is identified.

Original languageEnglish
Title of host publication6th International Conference on Industrial Engineering and Operations Management in Kuala Lumpur, IEOM 2016
PublisherIEOM Society
Pages754-759
Number of pages6
ISBN (Print)9780985549749
Publication statusPublished - 1 Jan 2016
Event6th International Conference on Industrial Engineering and Operations Management in Kuala Lumpur, IEOM 2016 - Kuala Lumpur, Malaysia
Duration: 8 Mar 201610 Mar 2016

Publication series

NameProceedings of the International Conference on Industrial Engineering and Operations Management
Volume8-10 March 2016
ISSN (Electronic)2169-8767

Conference

Conference6th International Conference on Industrial Engineering and Operations Management in Kuala Lumpur, IEOM 2016
CountryMalaysia
CityKuala Lumpur
Period8/03/1610/03/16

Keywords

  • Assignable cause
  • Data mining
  • Fault detection
  • Multivariate statistical process control
  • Process monitoring

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