A Review and Perspective on Control Charting with Image Data

Megahed, Fadel M.; Woodall, William H.; Camelio, Jaime A.

doi:10.1080/00224065.2011.11917848

Cited by 117 publications

(61 citation statements)

References 53 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…As discussed by Megahed et al (2011), images are becoming cheaper to obtain and can contain a considerable amount of information on process and product quality. A primary driving force in the development of statistical science has been the need to adapt and invent theory and methods to handle practical problems faced by scientists and other practitioners.…”

Section: Innovations In Statistical Sciencementioning

confidence: 99%

Innovation, Quality Engineering, and Statistics

Box

Woodall

2012

Quality Engineering

Self Cite

View full text Add to dashboard Cite

We discuss the key roles of statistics and quality engineering in the innovation process in business and industry. We review approaches that can be used in order to increase the chances of innovative discoveries. Most important, we stress the necessity for the quality engineering community to strengthen and promote its role in innovation. As Bisgaard (2006) has said, we should reframe much of what we do as systematic innovation. Adapting to the changing business and economic climate can revitalize our profession; failure to adapt threatens it.

show abstract

Section: Innovations In Statistical Sciencementioning

confidence: 99%

Innovation, Quality Engineering, and Statistics

Box

Woodall

2012

Quality Engineering

Self Cite

View full text Add to dashboard Cite

show abstract

“…The use of image data for part quality inspection is becoming more widespread in industry (Qiu, 2005;Yan et al, 2015;Megahed et al, 2011). Random porous and cellular materials represent a category of complicated structures where novel SPC methods are needed to cope with the challenging nature of quality inspection data (Kim et al, 2014;Zhuravleva et al, 2013;Campoli et al, 2013;Banhart, 2001).…”

Section: A Motivating Real Case Studymentioning

confidence: 99%

Profile Monitoring of Probability Density Functions via Simplicial Functional PCA With Application to Image Data

et al. 2018

View full text Add to dashboard Cite

The advance of sensor and information technologies is leading to data-rich industrial environments, where big amounts of data are potentially available. In this scenario, image data play a relevant role, as they can easily describe many phenomena of interest. This study focuses on images where several and similar features of interest are randomly distributed and characterized by no spatially correlated structure. Examples are pores in parts obtained via casting or additive manufacturing, voids in metal foams and light-weight components, grains in metallographic analysis, etc. The proposed approach consists of summarizing the random occurrences of the observed features via its (empirical) probability density function (PDF). In particular, a novel approach for PDF monitoring is proposed. It is based on simplicial functional principal component analysis (SFPCA), which is performed by applying an isometric isomorphism between the space of density functions, i.e., the Bayes space B 2 , and the space of square integrable functions L 2 . A simulation study shows the enhanced monitoring performances provided by the SFPCA-based profile monitoring against other competitors proposed in the literature. Eventually, a real case study dealing with the quality control of foamed materials production is discussed, to highlight a practical use of the proposed methodology.

show abstract

“…In a recent work, Megahed et al (2011) have perfomed a review on the use of control charts with image data. Many different methods are presented and commented.…”

Section: Introductionmentioning

confidence: 99%

Statistical process control based on Multivariate Image Analysis: A new proposal for monitoring and defect detection

Prats-Montalbán

Ferrer

2014

Computers & Chemical Engineering

View full text Add to dashboard Cite

The monitoring, fault detection and visualization of defects are a strategic issue for product quality. This paper presents a novel methodology based on the integration of textural Multivariate image analysis (MIA) and multivariate statistical process control (MSPC) for process monitoring. The proposed approach combines MIA and p-control charts, as well as T 2 and RSS images for defect location and visualization. Simulated images of steel plates are used to illustrate the monitoring performance of it. Both approaches are also applied on real clover images. Original Research Article2

show abstract

A Review and Perspective on Control Charting with Image Data

Cited by 117 publications

References 53 publications

Innovation, Quality Engineering, and Statistics

Innovation, Quality Engineering, and Statistics

Profile Monitoring of Probability Density Functions via Simplicial Functional PCA With Application to Image Data

Statistical process control based on Multivariate Image Analysis: A new proposal for monitoring and defect detection

Contact Info

Product

Resources

About