用数据驱动方法对化学反应器进行故障诊断
Pu Du1, Nabil M Abdel Jabbar2, Benjamin A Wilhite1
1Artie McFerrin Department of Chemical Engineering, Texas A&M University, College Station, Texas 77843, United States of America.
概括
这项关于连续水箱反应堆 (CSTR) 故障诊断的研究表明,随机森林 (RF) 和k-最近邻居 (KNN) 等数据驱动方法是有效的. 一个隔离森林 (IF) 算法帮助重新校准模型以提高性能.
科学领域:
- 化学工程是化学工程的重要组成部分.
- 过程控制 过程控制
- 故障诊断系统故障诊断系统
背景情况:
- 连续炉反应堆 (CSTR) 在化学过程中至关重要.
- 有效的故障诊断对于CSTR的安全性和效率至关重要.
- 传统方法可以通过改变过程参数来破坏.
研究的目的:
- 在CSTR.中调查故障诊断 (检测,隔离,估计).
- 评估数据驱动方法 (RF,KNN) 和基于模型的方法.
- 开发一个强大的故障诊断框架,适应参数转移.
主要方法:
- 来自CSTR对3-皮科林氧化的实验数据.
- 随机森林 (RF) 和k-最近邻居 (KNN) 的应用用于故障诊断.
- 利用隔离森林 (IF) 进行异常检测和模型重新校准.
主要成果:
- 在名义条件下RF和KNN成功检测,隔离和估计了故障.
- 热传递系数的变化扰乱了数据驱动和基于模型的方法.
- IF算法使模型重新校准,恢复性能;更新的数据集改善了RF/KNN的适应性.
结论:
- 数据驱动方法 (RF,KNN) 在CSTR故障诊断中显示出可扩展性和适应性.
- 基于模型的方法需要重新校准策略,以确保对参数变化的稳定性.
- 结合了数据驱动和基于模型的方法的综合框架,确保了可靠的故障诊断.
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