An EWMA and region growing based control chart for monitoring image data. Issue 4 (3rd July 2020)

• Record Type: Journal Article
• Authors: Zuo, Ling; He, Zhen; Zhang, Min
• Is Part Of: Quality technology & quantitative management. Volume 17:Issue 4(2020)
• Page Start: 470
• Page End: 485
• Publication Date: 2020-07-03
• Subjects: Machine vision system -- image data -- high dimension -- statistical process control -- region growing algorithm -- EWMA based statistic; Quality control -- Periodicals; Quality control -- Statistical methods -- Periodicals; Industrial management -- Periodicals; Industrial management; Management -- Research -- Methodology -- Periodicals; Qualitative research -- Periodicals; Management; Quality control; Quality control -- Statistical methods; Periodicals; 658.00721
• DOI: 10.1080/16843703.2019.1682751
• Languages: English
• ISSNs: 1684-3703

Abstract

ABSTRACT: The widespread application of machine vision system promotes the development of image-based statistical process control methodologies to improve the utilization of image data. Previous research has focused on either identification of fault size and/or location or detection of fault occurrence. There is limited research on both fault detection and identification. In this paper, an EWMA and region growing based control charting method is proposed for monitoring images of industrial products of which quality is either characterized by uniformity (e.g. LCD monitors) or a specific pattern (e.g. manufactured tiles). Simulation results show that the proposed method is not only effective in quick detection of the fault but also accurate in estimating the fault size and location. An experimental study is also provided to highlight how practitioners can implement the proposed method in applications.