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一个图像数据集用于检测不需要的螺栓旋转
Tom Bolton1, Julian Bass1, Tarek Gaber1
1University of Salford, Department of Science, Engineering, and Environment, The Crescent, Salford, M5 4WT, United Kingdom.
Data in brief
|July 14, 2025
概括
本研究引入了一套新的数据集,用于分析工业系统中螺栓降解. 该数据集捕获不同条件下的螺栓组件的图像,以帮助预测性维护和安全评估.
科学领域:
- 工业工程 工业工程 工业工程
- 材料科学 材料科学 材料科学
- 计算机视觉 计算机视觉
背景情况:
- 部件降解对于工业安全和效率至关重要.
- 现有的数据集缺乏关于机械固定器随时间推移的数据.
- 预测性维护需要有关元件磨损的数据.
研究的目的:
- 为研究螺栓降解创建一个新的数据集.
- 为开发用于组件监控的机器学习模型提供资源.
- 为弥补现有数据集对变时机械元件的差距.
主要方法:
- 收集了超过1100张螺栓装置的图像.
- 多种不同的图像采集角度和螺栓旋转度.
- 在受控的实验室条件下进行数据收集.
主要成果:
- 创建了一个独特的数据集,包含螺栓装置图像.
- 数据集捕捉了螺栓状况和方向的变化.
- 这种资源是第一个用于时间变化的螺栓分析的资源.
结论:
- 这一新型数据集支持对工业部件监控的研究.
- 能够开发人工智能来检测机械固定件的降解.
- 促进预测性维护策略的进步.
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