研究空间时间协同传感器故障诊断方法,用于顶部吹风炉的研究
Dongnian Jiang1, Jinjiang Zhao1
1College of Electrical and Information Engineering, Lanzhou University of Technology, Lanzhou 730050, Gansu, China.
ISA transactions
|June 5, 2024
概括
这项研究引入了一种用于诊断顶吹炉系统中传感器故障的新方法. 通过分析时空数据,该方法提高了系统可靠性和操作安全性.
科学领域:
- 工业工程 工业工程 工业工程
- 数据科学数据科学数据科学
- 材料科学 材料科学 材料科学
背景情况:
- 顶部吹风炉系统具有许多在恶劣环境中运行的传感器,导致老化和干扰的频繁故障.
- 传感器故障损害了这些关键工业系统的安全可靠运行.
- 现有的故障诊断方法往往忽视空间传感器关系,只关注时间数据.
研究的目的:
- 为顶吹炉系统开发先进的传感器故障诊断方法.
- 有效地整合空间和时间数据,以提高故障检测准确度.
- 提高工业炉运行的运行安全性和可靠性.
主要方法:
- 使用最大的相互信息构建了一个传感器网络知识图.
- 使用基于最大信息的图形卷积网络 (MI-GCN) 来提取多尺度的空间特征.
- 使用时空图形变压器 (STGT) 来捕捉时间依赖.
- 通过分析预测和实际传感器值之间的正常化残留值来诊断故障.
主要成果:
- 拟议的时空模型准确地捕捉了复杂的传感器相互依赖.
- 通过使用现实数据,成功识别了顶吹炉系统中的传感器故障.
- 与忽视空间信息的传统方法相比,表现出更高的性能.
结论:
- 综合时空方法显著提高了传感器故障诊断的准确性.
- 这种方法提高了顶吹炉运行的安全性和可靠性.
- 拟议的MI-GCN和STGT框架为工业传感器监控提供了一个强大的解决方案.
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