Research On Fault Diagnosis And Fault-tolerant Control Of Turbofan Aero-engine Control System

As the core system of the turbofan aero-engine,the control system keeps the engine working safely and reliably which provides thrust for flight.If the fault of the control system occurs,it is difficult to obtain the anticipant control purpose,and even leads to flight accidents.Therefore,in order to reduce maintenance costs and improve flight safety,it is significant to study fault diagnosis and fault-tolerant control of turbofan aero-engine control system.In this paper,a turbofan aero-engine is taken as the research object.The small perturbation model is established by the linear fitting method,and the affine parameter-dependent linear-parameter-varying(LPV)model is derived by the matrix pseudo-inverse method.When the fault of the turbofan aero-engine control system occurs,the fault should be diagnosed quickly and accurately.Based on the fault information,the fault-tolerant control should be used to ensure the safety operation of control system and satisfy some specified performance.Therefore,the design algorithm of a LPV estimator which has linear fractional transformation(LFT)parameter dependency is proposed in this paper.Firstly,for the turbofan aero-engine LPV model with both actuator and sensor fault under disturbances,the LPV fault estimation problem is transformed into a robustH_?control problem with LFT parameter dependency.By using S-procedure,the sufficient condition for the existence of the fault estimator is proposed,which can lead to less conservative results.Moreover,the LPV fault estimator design algorithm with adaptive scheduling characteristics is presented,and the parameters of the designed estimator will change adaptively with the LPV model.The LPV fault estimator can detect fault rapidly and reconstruct fault accurately.For the turbofan aero-engine control system with actuator multiplicative fault and sensor additive fault under disturbances,an active fault-tolerant control strategy based on the virtual actuator is designed to reconstruct the fault system.By adopting this strategy,it is no need to redesign the original controller,then the stability of the control system is guaranteed,and the control effect is similar with the fault-free system.Therefore,the fault-tolerant control is achieved.Considering the turbofan aero-engine has fault due to the performance degradation of gas path components,a prediction model for gas path performance parameters based on machine learning algorithms is proposed in this paper to predict and analyze the variation trend of gas path parameters and realizes the performance monitoring of the gas path components.First,starting test data of the turbofan aero-engine,the correlation analysis is carried out to choose the input parameters of the prediction model.Then,the input-output samples are established with the phase space reconstruction.Finally,AdaBoost.RT_ELM algorithm is applied to establish the gas path performance parameters prediction model.The prediction model can predict and analyze the variation trend of gas path parameters,and has high accuracy which can meet the engineering requirements.