This paper presents a fault detection and location (FDL) system for the situation of the coexistence of faults and health degradation in aeroengines. The FDL is able to locate the faulty sensors and actuators when the two kinds of faults coexist and avoid the interference of health degradation. The FDL is formed by a matrix of hybrid Kalman filters, and the different performances of hybrid Kalman filters can be used to distinguish the different faults. The proposed approach is applied to the nonlinear aeroengine model, and the ability of the proposed approach to detect and locate the faulty sensors and actuators reliably is demonstrated. According to the results, the FDL is able to locate the faulty sensors and actuators during the interference of health degradation.

The role of control system for aero-engine is to provide sufficient thrust and maintain physical and operational limits. Model reference adaptive control (MRAC) technique has been paid more attention for advantages in eliminating the effects of uncertainties arising from nonlinearity or component degradation and satisfying both safety and performance requirements in the entire flight envelope. However, due to the accuracy of aero-engine models, the implementation of the MRAC algorithm is challenging. In this paper, an MRAC architecture based on equilibrium manifold expansion (EME) model for an aero-engine is presented. An EME model is taken as the reference model in this architecture, which has high accuracy in the aero-engine modelling and contains both dynamic and steady-state characteristics of aero-engine. By using Lyapunov stability method, an MRAC based on EME model is designed and used to control the aero-engine obtaining desired performance. In the designed controller, uncertainty arising from component degradation of the aero-engine is estimated and the values of the uncertainty are replaced by the estimator. In order to indicate the control performance of the method provided in this article, the proposed MRAC architecture has been tested on a hardware-in-loop simulation platform, the simulation results have shown that compared the control performance with other traditional controllers, the aero-engine could quickly provide the desired control requirements without steady-state errors under the control of designed MRAC.

Turbofan engines are known as the heart of the aircraft. The turbofan’s health state determines the aircraft’s operational status. Therefore, the equipment monitoring and maintenance of the engine is an important part of ensuring the healthy and stable operation of the aircraft, and it is vital to monitor the remaining useful life (RUL) of the engine. The monitored data of turbofan engines have high dimensions and a long time span, which cause difficulties in predicting the remaining useful life of the engine. This paper proposes a residual life prediction model based on Autoencoder and a Temporal Convolutional Network (TCN). Among them, Autoencoder is used to reduce the dimension of the data and extract features from the engine monitoring data. The TCN network is trained on the obtained low-dimensional data to predict the remaining useful life. The model mentioned in this article is verified on the NASA public data set (C-MAPSS) and compared with common machine learning methods and other deep neural networks. The SAE-TCN model achieved better scores on the FD001 independent testing data set with an RMSE of 18.01 and a score of 161. The average relative error of the model relative to other common learning models is 0.9499 in RMSE and 0.2656 in Scoring Function. The experimental results show that the model proposed in this paper performs the best in the evaluation, and this conclusion has important implications for engine health.

A new adaptive modeling method for aircraft engine by using equilibrium manifold (EM) and its expansion (EME) model is presented, following research undertaken by the authors at School of Transportation Science and Engineering, Beijing University of Aeronautics and Astronautics, Beijing, China. The property of the expansion model and the effect of mapping design to the form are systematically studied. The model adaptivity analysis is discussed, and this paper also gives the identification procedure of modeling the aircraft engine approximate nonlinear model; the deterioration modification of compressor and the comparison with linear parameter-varying model and Kalman estimator are discussed. Simulations illustrate that modeling accuracy is high and the structure is simpler.