国外文献 > 1.轴承 > 2. 轴承类型与部件 > 滚动轴承
关键词搜索：
| 标 题： |
A Big Data Application in Manufacturing Industry-Computer Vision to Detect Defects on Bearings
制造业中的大数据应用——利用计算机视觉检测轴承缺陷 |
| 作 者： |
Perin Unal
Ozlem Albayrak
Meerim Kubatova
Bilgin U. Deveci
Ege Cirakman
C. Ipek Kocal
A. Murat Ozbayoglu |
| 关键词： |
Computer vision [计算机视觉]
Big data [ 大数据]
Artificial intelligence [ 人工智能]
Defect detection [ 缺陷检测]
Image classification with localization [ 带定位的图像分类]
Rolling bearings [ 滚动轴承] |
| 出版年： |
2022 |
| 文集名： |
2022 IEEE International Conference on Big Data (Big Data 2022)
2022年IEEE国际大数据会议（Big Data 2022） |
| 会议名： |
2022 IEEE International Conference on Big Data (Big Data 2022)
2022年IEEE国际大数据会议（Big Data 2022） |
| 届 次： |
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| 摘 要： |
In the contemporary rotating machinery, bearings are critical and indispensable parts. Early detection of rolling bearing defects carries crucial importance, because undetected defects on the rolling bearings may end in loss of time, resources, money and even lives. In parallel to the accelerated utilization of deep learning applications in the manufacturing industry, different studies have been conducted to determine and evaluate defects on the surfaces of rolling bearings. In this study, a new system, that contains a hardware platform and software components in order to detect surface defects of the metal rolling bearings has been developed. To detect defects, optic image data of the bearings were used, and then computer vision and artificial intelligence techniques were applied to them. In the system, TC-VISION, the source of big data is the platform designed and developed using the optical camera. The results of the applied CNN algorithms performed better than the targeted values with respect to several metrics. The F1 score obtained is close to 100%. The developed system is aimed to be enhanced further in order to develop a fully automated inspection and quality control system for metal rolling bearing systems appropriate for serial production in real industrial environments.
在现代旋转机械中，轴承是关键且不可或缺的部件。早期发现滚动轴承缺陷具有重要意义，因为未被检测到的轴承缺陷可能导致时间、资源、资金甚至生命的损失。随着深度学习技术在制造业中的广泛应用，越来越多的研究致力于识别和评估滚动轴承表面的缺陷。本研究开发了一套新型系统，该系统包含硬件平台和软件组件，用于检测金属滚动轴承的表面缺陷。系统通过采集轴承的光学图像数据，并应用计算机视觉与人工智能技术进行缺陷检测。其中，TC-VISION作为大数据源，基于光学摄像头设计并开发了该平台。所采用的CNN算法在多个指标上表现优于目标值，获得的F1分数接近100%。该开发系统旨在进一步优化，以构建一套适用于实际工业环境批量生产的全自动金属轧制轴承系统的检测与质量控制系统。 |
| 中文内容为网络AI翻译，专业术语未必准确，仅供参考。 |
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