A Survey: Industrial Anomaly Detection based on Data Mining

Science and Technology of Engineering, Chemistry and Environmental Protection Pub Date : 2024-02-19 DOI:10.61173/p6g5je55

Jinrui Li

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Abstract

Industrial defect detection plays a crucial role in modern manufacturing. Identifying and addressing inferior products contributes to enhancing product quality, strengthening product competitiveness, and increasing customer satisfaction. Existing surveys of industrial defect detection are relatively scarce and struggle to reflect the latest development trends. Therefore, this article provides a more detailed and in-depth survey of industrial defect detection technologies. The article first reviews the development history of industrial defect detection methods. It then covers three aspects: the concept of general anomalies, concepts related to image anomaly detection, and industrial defects, providing an overview of industrial defect detection in these areas. It also summarizes the current state of development, as well as the advantages and disadvantages of each aspect. Additionally, the article identifies the limitations of industrial detection methods in practical industrial applications. Finally, it looks forward to the future development trends and potential research directions in this field, aiming to inspire future research.

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调查：基于数据挖掘的工业异常检测

工业缺陷检测在现代制造业中发挥着至关重要的作用。识别和处理劣质产品有助于提高产品质量、增强产品竞争力和提高客户满意度。现有的工业缺陷检测调查相对较少，难以反映最新的发展趋势。因此，本文对工业缺陷检测技术进行了更详细、更深入的研究。文章首先回顾了工业缺陷检测方法的发展历程。然后从一般异常的概念、图像异常检测的相关概念和工业缺陷三个方面，对这些领域的工业缺陷检测进行了概述。文章还总结了各方面的发展现状和优缺点。此外，文章还指出了工业检测方法在实际工业应用中的局限性。最后，文章展望了该领域的未来发展趋势和潜在研究方向，旨在为未来研究提供启发。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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Science and Technology of Engineering, Chemistry and Environmental Protection

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