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一个软传感器模型的烧结过程质量指数基于多源数据融合的软传感器模型.

Yuxuan Li^1,2, Weihao Jiang¹, Zhihui Shi¹

¹Hikvision Research Institute, Hangzhou 310051, China.

Sensors (Basel, Switzerland)

|July 11, 2023

概括

本研究引入了一种使用多源数据融合,结合工业视频数据的新型烧结质量预测模型. 该模型提高了复杂的工业烧结过程中关键质量变量的预测准确性.

关键词:

图像特征提取图像特征提取关键框架提取关键框架提取多源数据的数据融合.烧结质量的预测和预测.

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科学领域:

材料科学材料科学材料科学
工业工程工业工程工业工程
计算机视觉计算机视觉

背景情况:

工业烧结中的关键质量变量难以在线测量,导致延迟和数据稀缺.
现有的方法在实时监控和烧结过程中的数据稀缺性方面扎.

研究的目的:

使用多源数据融合开发一个准确的烧结质量预测模型.
将工业视频数据整合到烧结质量评估中.

主要方法:

从工业摄像头视频中根据特征高度提取关键.
使用浅层 (烧结分层) 和深层 (ResNet) 方法进行多尺度特征提取.
将提取的图像特征与工业时间序列数据融合为软传感器模型.

主要成果:

拟议的多源数据融合模型显著提高了烧结质量预测的准确性.
有效利用各种数据源,包括视觉和时间序列数据.
使用视频数据进行实时质量监测的可行性.

结论:

多源数据融合,结合视频分析,为准确的烧结质量预测提供了强大的解决方案.
开发的软传感器模型解决了传统离线测试和数据稀缺性的局限性.
这种方法增强了工业烧结中的工艺控制和质量管理.