基于结构分析的故障检测和隔离:应用于多发动机推进集群
Renato Murata1,2, Julien Marzat1, Hélène Piet-Lahanier1
1Département Traitement de l'Information et des Systèmes (DTIS), Office National d'Études et de Recherches Aérospatiales (ONERA), Université Paris-Saclay, 6 Chem. de la Vauve aux Granges, 91120 Palaiseau, France.
Sensors (Basel, Switzerland)
|February 26, 2025
概括
使用结构分析 (SA) 的新型基于模型的故障检测和隔离 (FDI) 系统有效地识别和隔离多发动机火箭集群中的故障. 这种方法在极端运行条件下提高了系统可靠性.
科学领域:
- 航空航天工程 航空航天工程
- 控制系统工程 控制系统工程
- 系统可靠性 系统可靠性
背景情况:
- 多发动机火箭集群在极端条件下运行,使故障检测和隔离 (FDI) 变得复杂.
- 准确检测小规模断层对于任务的成功和安全至关重要.
研究的目的:
- 使用结构分析 (SA) 为多引擎集群开发基于模型的外国直接投资系统.
- 创建用于最小剩余子集选择和故障隔离的算法.
- 通过新的灵敏度指标来提高故障诊断的准确性.
主要方法:
- 结构分析 (SA) 应用于三引擎集群模型.
- 产生和选择超过16000个剩余发电机候选人.
- 开发算法,以识别最小核心值的剩余子集.
- 引入子集灵敏度指数 (SSI) 和剩余灵敏度指数 (RSI) 以进行最佳的子集选择.
- 在十种故障场景下进行性能评估的蒙特卡洛模拟.
主要成果:
- 使用最小剩余子集,生成每个故障的独特故障签名的方法.
- 基于拟议的SSI指标,确定最佳的剩余子集.
- 一个利用RSI预测活跃故障的新故障隔离算法.
- 在检测和隔离传感器和执行器故障方面,FDI系统的有效性得到了证明.
结论:
- 拟议的基于模型的FDI系统有效地解决了多发动机火箭系统故障诊断的复杂性.
- 新的灵敏度指数 (SSI,RSI) 改善了残留物选择,以实现强大的故障隔离.
- 开发的算法提供了一种可靠的方法来识别和隔离故障,提高运营安全.
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