基于自动编码器的系统用于检测颗粒机融过程中的异常
Mingxiang Zhu1,2, Guangming Zhang1, Lihang Feng1
1College of Electrical Engineering and Control Science, Nanjing Tech University, Nanjing 211899, China.
Sensors (Basel, Switzerland)
|November 27, 2024
概括
这项研究引入了一种基于自编码器的系统,用于识别聚颗粒机中的化异常. 深度学习方法通过有效检测和区分异常程度来提高生产效率和产品质量.
科学领域:
- 工业制造业 工业制造业 工业制造业
- 人工智能的人工智能
- 材料科学 材料科学 材料科学
背景情况:
- 手动监测和传统方法不足以检测聚颗粒机中的化异常.
- 融挤出工艺需要有效的异常识别,以保持生产效率和产品质量.
研究的目的:
- 提出和评估一种基于自编码器的系统,用于识别聚颗粒机中的化异常.
- 解决目前在检测和区分化异常状态方面的方法的局限性.
主要方法:
- 利用自动编码技术进行基于深度学习的异常检测.
- 实施数据增强技术,包括随机改变图像亮度和旋转,以提高系统的稳定性.
- 在聚颗粒剂融挤出数据上训练并测试了系统.
主要成果:
- 拟议的自动编码系统在检测融异常方面表现出高效率.
- 该系统有效地区分了不同程度的融化异常.
- 通过数据增强,实现了对环境光强度变化的增强强度.
结论:
- 自动编码技术显示出在化异常识别中的工业应用的巨大潜力.
- 开发的系统为聚颗粒机提供了更好的检测效率和泛化性能.
- 进一步的研究可以探索先进的深度学习技术,以加强工业过程监控.
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