对于工业传感器网络中根源原因诊断和延迟估计的滞后特异转移
Rui Chen1, Shu Liang1, Jian-Guo Wang2
1College of Electronic and Information Engineering, Tongji University, Shanghai 200092, China.
Sensors (Basel, Switzerland)
|July 12, 2025
概括
这项研究引入了延迟特异转移 (LSTE),以精确确定工业设施中的干扰来源. LSTE准确地识别了第一个偏离的传感器及其信号传播时间,改进了干扰分析.
科学领域:
- 工艺工程和控制的过程工程和控制.
- 数据科学和机器学习
- 工业监测和诊断 工业监测和诊断
背景情况:
- 现代工业厂在高频率下产生大量的多传感器数据.
- 传感器数据的可变运输和停留时间延迟使得准确的干扰分析变得复杂.
- 现有的方法难以准确识别过程干扰的起源和传播时间.
研究的目的:
- 开发和验证一种新的方法,使用历史传感器数据识别过程干扰的来源.
- 准确量化干扰通过工业系统传播的时间延迟.
- 与传统方法相比,通过减少虚假链接来提高干扰检测的可靠性.
主要方法:
- 应用延迟特异转移 (LSTE) 来分析多传感器时间序列数据.
- 包括一个自我预测优化步骤来从传感器数据中删除自我信息.
- 在各种工业案例研究中对LSTE进行基准测试:非线性模拟,田纳西州的东曼工厂,三相分离器和高炉.
主要成果:
- 在所有经过测试的场景中,LSTE成功地发现了最初的传感器偏离正常运行.
- 该方法准确地报告了干扰的传播时间,与已知的过程物理学保持一致.
- 与经典转移相比,LSTE显示了虚假因果关系的显著减少.
结论:
- 滞后特异转移是一种有效的工具,用于精确确定干扰来源和量化复杂工业过程中的传播延迟.
- 自预测优化提高了用于工业传感器数据分析的转移的准确性和可靠性.
- 在工业环境中,LSTE为实时监控,故障检测和流程优化提供了强大的进步.
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