大规模系统的数据驱动分布式故障检测和容错控制:一个子空间预测器辅助的集成设计方案
IEEE transactions on cybernetics
|August 4, 2025
概括
本研究引入了一种新的分布式框架,用于大规模系统中的数据驱动故障检测 (FD) 和故障耐受性控制 (FTC). 该方法有效地集成FD和FTC使用本地子系统数据,避免集中限制.
科学领域:
- 控制工程 控制工程 控制工程
- 系统科学 系统科学
- 数据驱动建模数据驱动建模
背景情况:
- 由于子系统的相互连接,集中故障检测和控制 (FD/FTC) 方法对于大型系统是不够的.
- 现有的方法与分布式架构和全球系统信息需求作斗争.
研究的目的:
- 提出一个数据驱动的框架,用于大型系统的综合故障检测和故障耐受性控制 (FD/FTC).
- 通过仅使用本地和邻近子系统数据来实现分布式设计.
主要方法:
- 一个子空间预测器辅助的框架有机地结合了FD和FTC.
- 每个子系统的预测器只使用本地和邻近的输入/输出 (I/O) 数据,促进分布式设计.
- 集成架构完全是数据驱动的,不需要大规模的系统机制信息.
主要成果:
- 拟议的框架使FD和FTC的分布式设计成为可能.
- 该方法成功地集成了故障检测和故障耐受性控制,而无需使用全局数据.
- 通过数值模拟和级联CSTR案例研究来验证可行性和有效性.
结论:
- 子空间预测器辅助框架为大型系统中的分布式FD和FTC提供了有效的数据驱动解决方案.
- 这种方法克服了集中式方法的局限性,并证明了其实际适用性.
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