• 主题
高级搜索  >
历史搜索 清空历史
首页> 外文期刊>IFAC PapersOnLine >A Systematic Approach to Dynamic Monitoring of Industrial Processes Based on Second-Order Slow Feature Analysis ?
【24h】

A Systematic Approach to Dynamic Monitoring of Industrial Processes Based on Second-Order Slow Feature Analysis ?

机译：基于二阶慢特征分析的工业过程动态监控系统方法 ？

获取原文
           
页面导航
  • 摘要
  • 著录项
  • 相似文献
  • 相关主题

摘要

Slow feature analysis has proven to be an effective process monitoring and fault diagnosis approach. By isolating temporal behaviors from steady-state variations in process data, slow feature analysis enables a concurrent monitoring of operating condition and process dynamics, based on which false alarms triggered by nominal operating condition deviations can be effectively removed. However, the present formulation of slow feature analysis only makes use of the first-order time difference of time series data, thereby falling short of addressing high-order dynamics in process operations. In this work, we propose a second-order formulation of slow feature analysis, and further develop a systematic framework for process monitoring and fault diagnosis, which can provide more meaningful information about process dynamics to assist decision-making of operators. Case studies on the Tennessee Eastman benchmark process are conducted to demonstrate the efficacy of the proposed method.
机译：慢速特征分析已被证明是一种有效的过程监控和故障诊断方法。通过将时间行为与过程数据的稳态变化隔离开来，慢速特征分析可以同时监视操作条件和过程动态，从而可以有效地消除名义操作条件偏差触发的错误警报。但是，慢速特征分析的当前公式仅利用了时间序列数据的一阶时间差，从而未能解决过程操作中的高阶动力学问题。在这项工作中，我们提出了慢速特征分析的二阶公式，并进一步开发了用于过程监控和故障诊断的系统框架，该框架可以提供有关过程动力学的更有意义的信息，以帮助操作员进行决策。对田纳西州伊士曼基准程序进行了案例研究，以证明该方法的有效性。

著录项

相似文献

  • 外文文献
  • 中文文献
  • 专利
获取原文

客服邮箱：kefu@zhangqiaokeyan.com

京公网安备：11010802029741号 ICP备案号：京ICP备15016152号-6 六维联合信息科技 (北京) 有限公司©版权所有

  • 客服微信

  • 服务号