An EWMA and region growing based control chart for monitoring image data

Zuo, Lei; He, Zhen; Zhang, Min

doi:10.1080/16843703.2019.1682751

Cited by 14 publications

(9 citation statements)

References 51 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…A MVS is a computer-based system for analyzing and processing image data that is provided by imagecapturing devices (e.g., cameras, X-ray devices, or vision sensors). New studies show that implementing MVSs in industrial environments could be fully utilized to improve the quality of the product Zuo et al 117 . In this regard, researchers developed a new interdisciplinary field of research by integrating MVS approaches and SPC principles.…”

Section: Monitoring Image Datamentioning

confidence: 99%

“…By means of a comparative study, Stankus and Castillo-Villar 153 showed that the new methodology has a significantly shorter out-of-control ARL than the He et al 152 methodology for the scratch and no significant difference in out-of-control ARL for the incorrect surface curvature. Zuo et al 117 reported that the existing research in the image-based SPC area has focused on either identification of fault size and/or location or detection of fault occurrence and there is limited research on both fault detection and identification. To handle such situations, they proposed an EWMA and region growing based control chart for monitoring of 8-bit grayscale images of industrial products.…”

Section: Monitoring Image Datamentioning

confidence: 99%

See 1 more Smart Citation

Application of Machine Learning in Statistical Process Control Charts: A Survey and Perspective

Tran¹,

Nadi

Nguyễn

et al. 2021

Springer Series in Reliability Engineering

View full text Add to dashboard Cite

Over the past decades, control charts, one of the essential tools in Statistical Process Control (SPC), have been widely implemented in manufacturing industries as an effective approach for Anomaly Detection (AD). Thanks to the development of technologies like the Internet of Things and Artificial Intelligence (AI), Smart Manufacturing (SM) has become an important concept for expressing the end goal of digitization in manufacturing. However, SM requires a more automatic procedure with capabilities to deal with huge data from the continuous and simultaneous process. Hence, traditional control charts of SPC now find difficulties in reality activities including designing, pattern recognition, and interpreting stages. Machine Learning (ML) algorithms have emerged as powerful analytic tools and great assistance that can be integrating to control charts of SPC to solve these issues. Therefore, the purpose of this chapter is first to presents a survey on the applications of ML techniques in the stages of designing, pattern recognition, and interpreting of control charts respectively in SPC especially in the context of SM for AD. Second, difficulties and challenges in these areas are discussed. Third, perspectives of ML techniquesbased control charts for AD in SM are proposed. Finally, a case study of an ML-based control chart for bearing failure AD is also provided in this chapter.

show abstract

Section: Monitoring Image Datamentioning

confidence: 99%

Section: Monitoring Image Datamentioning

confidence: 99%

Application of Machine Learning in Statistical Process Control Charts: A Survey and Perspective

Tran¹,

Nadi

Nguyễn

et al. 2021

Springer Series in Reliability Engineering

View full text Add to dashboard Cite

show abstract

“…In this context a data image is taken from a process, and using 2D wavelet transformations features are extracted and a statistic can be derived to monitor and plot on the control chart. Wavelet analysis has been widely used in image processing and we expect more papers on the use of advanced image processing using wavelet to be applied to image statistical process control (Amirkhani and Amiri, 2020;Zuo et al, 2019).…”

Section: Some Research Directionsmentioning

confidence: 99%

On wavelet-based statistical process monitoring

Cohen

Atoui

2020

Transactions of the Institute of Measurement and Control

View full text Add to dashboard Cite

This paper presents an overview of wavelet-based techniques for statistical process monitoring. The use of wavelet has already had an effective contribution to many applications. The increase of data availability has led to the use of wavelet analysis as a tool to reduce, denoise, and process the data before using statistical models for monitoring. The most recent review paper on wavelet-based methods for process monitoring had the goal to review the findings up to 2004. In this paper, we provide a recent reference for researchers and engineers with a different focus. We focus on: (i) wavelet statistical properties, (ii) control charts based on wavelet coefficients, and (iii) wavelet-based process monitoring methods within a machine learning framework. It is clear from the literature that wavelets are widely used with multivariate methods compared to univariate methods. We also found some potential research areas regarding the use of wavelet in image process monitoring and designing control charts based on wavelet statistics, and listed them in the paper.

show abstract

“…The proposed method summarizes the random occurrences of the observed features by using their probability density functions (PDFs). Zuo, He and Zhang [8] presented a image monitoring method which combines the region growing algorithm with the EWMA-based statistic. In their method, quality characteristic is either characterized by uniformity or a specific pattern.…”

Section: Introductionmentioning

confidence: 99%

Statistical modeling and monitoring of image data in the presence of temporal and spatial correlations

et al. 2022

View full text Add to dashboard Cite

Owing to the increasing sensitivity of the processes and the inadequacy of the methods based on human inspection, use of product images in statistical process control has been considered by some researchers. In this paper, the regression-based approach was developed to monitor image data under two-scale analysis, large-scale and small-scale. Because geometric profiles created from images have complex and non-linear behaviour, wavelet transformation was used to extract the main features (regression coefficients) under large-scale analysis. Observations in mentioned profiles could be temporally or spatially correlated as well. To monitor the small-scale components which could be expressed by correlation in error terms, one parametric and one nonparametric methods were developed. After extracting features for both scales, some appropriate test statistics were computed. Then, monitoring the process was performed by plotting these test statistics on corresponding control charts. Performance of the proposed method was evaluated in terms of run-length and change-point measures. Simulation and industrial case studies were also performed to evaluate the proposed method's performance in detecting different shifts. The results indicated the proper performance of the proposed method in monitoring industrial processes to detect out-of-control conditions and identify the source of variability.

show abstract

An EWMA and region growing based control chart for monitoring image data

Cited by 14 publications

References 51 publications

Application of Machine Learning in Statistical Process Control Charts: A Survey and Perspective

Application of Machine Learning in Statistical Process Control Charts: A Survey and Perspective

On wavelet-based statistical process monitoring

Statistical modeling and monitoring of image data in the presence of temporal and spatial correlations

Contact Info

Product

Resources

About