传感器故障下的航空发动机机载自适应模型重构

利用航空发动机测量参数偏离正常工作情况下的变化量,可以估计发动机的非额定工作状况,并以此对机载模型进行校正,使其与真实发动机工作状况保持一致。建立了包含发动机性能蜕化因素的状态变量模型并对其进行了增广,设计了卡尔曼滤波器,根据可测输出偏离量对发动机性能蜕化值进行了估计,并将性能蜕化值用于修正发动机不可测输出参数。考虑了当某一传感器发生故障后,利用一簇卡尔曼滤波器对发生故障的传感器进行诊断并隔离,并依据剩余非故障传感器的信息对自适应模型进行重构。仿真结果表明,重构的自适应模型能够满足精度及实时性要求。 

Aero-Engine Adaptive Model Re-Construction under Sensor Failure

The measured parameter deviations from nominal values can be used to predict the deterioration level of an aero-engine.A state variable model,which considered the deterioration factors,was developed.These deterioration factors were then treated as augmented states of the state variable model,thus they could be predicted by the Kalman filter based on sensed parameter deviations.If there was a sensor failure,the prediction results might not be appropriate.So a series of Kalman filters were designed as a diagnostic method to search the failed sensor.The adaptive model could be re-constructed once the failed sensor was found out.The immeasurable output parameters were trimmed by the predicted deteriorations.Adaptive performance of the re-constructed adaptive model were presented and compared with the model which had no sensor failures.The re-constructed adaptive model is shown to be able to well match the actual engine.