发动机及控制系统状态监测方法研究

Research on Monitoring Methods for Engine and Control System Status

本文针对发动机及控制系统并发故障的情况,设计一种基于决策级融合的单传感器自调整预测模型,提出一种基于数据驱动算法Meta-OSELM,用于构造发动机传感器的解析余度。并考虑多传感器发生故障时,基于信号融合传感器解析余度算法失效问题,设计一种基于决策级融合的自调整预测模型,实现涡扇发动机的传感器自调整预测,并进行发动机及控制系统并发故障下的故障监测与诊断验证。

This article designs a single sensor self tuning prediction model based on decision level fusion for concurrent faults in engines and control systems,and proposes a data-driven algorithm Meta-OSELM for constructing analytical redundancy of engine sensors.And considering the problem of signal fusion sensor analysis redundancy algorithm failure when multiple sensors fail,a self adjusting prediction model based on decision level fusion is designed to achieve sensor self adjusting prediction for turbofan engines,and fault monitoring and diagnosis verification under concurrent faults in the engine and control system are carried out.