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为快速高效地完成涡扇发动机传感器故障诊断算法的硬件在环仿真试验,构建了以NI CompactRIO为核心的传感器故障诊断系统的快速原型实时仿真平台.基于一簇卡尔曼滤波器,在LabVIEW编程环境中建立了传感器故障诊断系统.分别在涡扇发动机模型稳态和动态工作时完成了对单个传感器故障的检测、隔离和重构的硬件在环仿真试验并验证了算法精度.经过大量试验,结果表明:基于卡尔曼滤波器理论的诊断算法能在传感器故障情况下确保控制系统安全运行,诊断精度最高可达1.4%;同时表明,该快速原型实时仿真平台的设计是成功的.研究工作为发动机传感器故障诊断系统的半物理仿真试验奠定了基础.
In order to accomplish the hardware-in-the-loop simulation of turbofan engine sensor fault diagnosis algorithm quickly and efficiently, a rapid prototyping real-time simulation platform of sensor fault diagnosis system based on NI CompactRIO was built.According to a cluster of Kalman filters, A sensor fault diagnosis system was set up.The hardware-in-the-loop simulation of single sensor fault detection, isolation and reconstruction was completed and the accuracy of the algorithm was verified when the turbofan engine model was in steady state and dynamic state respectively.After a large number of experiments, The results show that the Kalman filter theory based diagnosis algorithm can ensure the safe operation of the control system under the condition of sensor failure, and the diagnosis accuracy can reach as high as 1.4%. At the same time, it shows that the design of the rapid prototype real-time simulation platform is successful. The semi-physical simulation of sensor fault diagnosis system lays the foundation.