一种适应式的元启发式优化方法,用于田纳西州的东曼过程,用于工业容错控制系统的控制系统
Faizan E Mustafa1, Ijaz Ahmed1, Abdul Basit1
1Department of Electrical Engineering, Pakistan Institute of Engineering and Applied Sciences (PIEAS), Islamabad, Pakistan.
PloS one
|February 21, 2024
概括
本研究介绍了适应群众搜索算法 (ACSA) 对于田纳西东曼过程 (TEP). 该ACSA提高故障检测和减少工业运营中的错误报警.
科学领域:
- 工业过程控制 工业过程控制
- 在工程领域的人工智能.
- 优化算法 优化算法
背景情况:
- 田纳西伊斯曼过程 (TEP) 是评估故障检测和错误报警系统的基准.
- 现有的方法往往与工业操作的动态和复杂性质作斗争,导致效率低下.
研究的目的:
- 通过使用自适应群众搜索算法 (ACSA) 开发TEP的自适应监测策略.
- 为了提高故障识别的准确性,并尽量减少错误报警.
- 创建一个强大的系统,能够处理过程变化和干扰.
主要方法:
- 适应群众搜索算法 (ACSA) 的应用,灵感来自群众行为,用于过程监控.
- 使用ACSA优化监控变量,警报值和决策标准.
- 在TEP数据集上进行了全面的模拟,以验证方法.
主要成果:
- 与传统的静态方法相比,基于ACSA的监测策略显示出更高的故障检测率.
- 观察到虚假报警发生率显著减少.
- ACSA有效地管理了过程变化,干扰和不确定性,证明了它的稳定性.
结论:
- ACSA为TEP中故障检测和错误报警减轻提供了有效和可适应的解决方案.
- 拟议的适应性监测策略显示出在其他复杂工业过程中更广泛应用的前景.
- 这项研究有助于推进故障检测技术,提高工业安全和运营效率.
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