在热带生产中,用于最终滚动温度的数字双联故障诊断平台
Chen Desheng1, Shao Jian1, Li Mingxin1
1National Engineering Research Center of Flat Rolling Equipment, University of Science and Technology Beijing, Beijing 100083, China.
Materials (Basel, Switzerland)
|November 14, 2023
概括
现在更容易检测热中最终滚动温度异常. 一种新的数据驱动方法准确地识别和分析原因,以92%的准确性改进了手动分析.
科学领域:
- 材料科学与工程 材料科学与工程
- 工业过程控制 工业过程控制
- 数据科学数据科学数据科学
背景情况:
- 最终的轮温度对于热带的性能和稳定性至关重要.
- 在工业环境中,诊断最终滚动温度的异常是具有挑战性的.
研究的目的:
- 开发数据驱动的风险评估方法,用于检测和分析最终滚动温度异常.
- 在热工艺中提高异常检测和根本原因分析的准确性和效率.
主要方法:
- 利用随机森林和隔离森林算法来检测条纹头异常.
- 采用豪斯多夫距离和最长常见次序 (LCSS) 算法进行全长条曲线分析.
- 综合知识图和贝叶斯网络,以确定复杂的因果关系和异常的根本原因.
主要成果:
- 拟议的方法有效地选重要参数,并评估带头和全长异常的风险.
- 综合知识图和贝叶斯网络方法成功识别了异常原因.
- 现场应用在推断异常原因方面显示了92%的准确性,明显超过手动分析.
结论:
- 结合机械和数据建模的综合方法可以快速,自动化和准确地检测和分析最终滚动温度异常.
- 这种数据驱动系统为热工艺的工业过程控制提供了显著的进步.
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