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研究了航空发动机控制系统传感器鲁棒故障检测与隔离问题,提出了一种克服不同干扰对控制系统诊断性能影响的方法.应用未知输入观测器(unknown input observer,简称UIO)理论来解耦航空发动机动态系统干扰,并针对控制系统传感器设计一族UIO,提取出一系列的传感器残差特征数据,通过分析残差队列的幅值特性,实现航空发动机控制系统传感器故障诊断.在高斯白噪声、模型工作点变化和非高斯噪声三类干扰下的数字仿真结果表明,不管何种干扰,UIO诊断方法均能检测和隔离出传感器故障,在诊断鲁棒性方面,要优于Kalman滤波器诊断算法.
A robust robust fault detection and isolation problem for aeroengine control system is studied, and a method of overcoming the influence of different disturbance on the performance of the control system is proposed.Unknown unknown input observer (UIO) theory is used to decouple the aero-engine A series of UIOs are designed for the sensors of the control system and a series of sensor residual characteristic data are extracted, and the amplitude characteristics of the residual queue are analyzed to realize the sensor fault diagnosis of the aeroengine control system. In Gaussian white noise, model work The simulation results under the three types of point variation and non-Gaussian noise show that the UIO method can detect and isolate the sensor fault no matter what kind of interference, and is superior to the Kalman filter in terms of diagnostic robustness.