Statistical Methods for Image Data Monitoring: A Systematic Overview

Articles in Press

Document Type : Review

Authors

1 Faculty of Industrial Engineering and Management, Malek Ashtar University of Technology, Tehran, Iran

2 Faculty of Aerospace, Malek Ashtar University of Technology,Tehran,Iran

10.24200/j65.2023.61823.2339

Abstract

Nowadays, one of the biggest challenges in the field of statistical process control (SPC) is how to effectively handle the abundance of big data in order to evaluate the quality of processes and products. Image data, which is increasingly utilized in the manufacturing and service industries, poses a significant component of this big data. Images offer a cost-effective means of swiftly generating a large volume of data within just a few seconds. Machine vision systems (MVSS) are extensively employed across various industries for obtaining information pertaining to dimensions, geometric features, surface defects, surface finish, as well as the differentiation between conforming and nonconforming products. Consequently, researchers are placing greater emphasis on utilizing statistical process control methods for analyzing image data to detect process variations and defective products, among other goals. This research contribution is highly attractive to practitioners seeking to leverage digital tools for quality management due to its diverse range of potential applications in addressing real-world issues (e.g., Quality 4).


Notably, the research effectively integrates machine learning, traditional statistical methods, and image processing within the framework of image-based statistical process monitoring. Choosing appropriate types of images should take into account their respective strengths and weaknesses. Binary images are well-suited for monitoring geometric features, grayscale images are suitable for assessing product surfaces, and multi-spectral images prove useful when color represents a critical quality characteristic.


This paper presents a systematic overview accompanied by a conceptual classification scheme based on content analysis methodology. The objective is to analyze and categorize prior research that has explored various aspects of statistical process monitoring applied to image data across different industries. The focus is specifically on reliable scientific sources, without any constraints on time limitations. Moreover, drawing from 64 relevant papers in this field, the paper highlights research gaps and provides directions to inspire future studies.

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Sharif Journal of Industrial Engineering & Management

Articles in Press, Accepted Manuscript
Available Online from 30 October 2023

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