In the parallel hybrid electric propulsion system (PHEPS), the integrated electric power system serves as an augmenter to the conventional turbomachinery. In order to maximize the performance improvement for the original aeroengine components, this study presents a novel multienergy management control schedule design method for each different flight condition. Through digital simulation and hardware in the loop simulation based on a parallel hybrid geared turbofan engine (PH-GTF) model, results show that compared with the baseline GTF engine, the PH-GTF propulsion system exhibits significant performance improvements: 31% reduction in compressor airflow losses, 5% and 2% surge margin improvements in the low-pressure compressor during accelerated/decelerated transients, and 18.8% fuel savings under the cruise condition.

This paper proposes a fusion-based dual-task architecture that jointly performs degradation pattern recognition and remaining useful life prediction for aeroengines, achieving improved prediction accuracy through multi-source information fusion.

Because of the complexity of the structure and the instability of the external air flow, a lot of vibration problems will inevitably occur during the operation of aero-engine. Aiming at the vibration problem of the whole aero-engine, a general dynamic model of rotor-support-casing vibration transmission is established according to the actual structure of the aero-engine and the summary of experience. Moreover, starting from the vibration control problem of the internal and external casing of aero-engine, a new control algorithm (geometric design method) is used in this paper to design the vibration reduction controller in the limited frequency domain. In the case of limited sensors and actuator, the controller will be used to try to control the vibration of multiple outputs (ie, the inner and outer casings of the aero-engine), and compare the vibration reduction effect with the vibration reduction controller designed by the classical control theory method (PID). Finally, the simulation model is built and verified by Matlab/Simulink. The results show that the geometric design method can intuitively obtain the existence, uniqueness and optimality of the optimal controller in the limited frequency domain, and the optimal vibration reduction control for the main control object can be as high as 25dB. Compared with traditional control methods, geometric design method has obvious advantages.

The aero-engine system is complex, and the working environment is harsh. As the fundamental component of the aero-engine control system, the sensor must monitor its health status. Traditional sensor fault detection algorithms often have many parameters, complex architecture, and low detection accuracy. Aiming at this problem, a convolutional neural network (CNN) whose basic unit is an inception block composed of convolution kernels of different sizes in parallel is proposed. The network fully extracts redundant analytical information between sensors through different size convolution kernels and uses it for aero-engine sensor fault detection. On the sensor failure dataset generated by the Monte Carlo simulation method, the detection accuracy of Inception-CNN is 95.41%, which improves the prediction accuracy by 17.27% and 12.69% compared with the best-performing non-neural network algorithm and simple BP neural networks tested in the paper, respectively. In addition, the method simplifies the traditional fault detection unit composed of multiple fusion algorithms into one detection algorithm, which reduces the complexity of the algorithm. Finally, the effectiveness and feasibility of the method are verified in two aspects of the typical sensor fault detection effect and fault detection and isolation process.

Complex and strong nonlinearity are important characteristics of aero-engines, and they often work in harsh environments. How to design a simple, stable, and small-calculation nonlinear controller applying appropriate nonlinear theory is a great challenge. Aiming at this challenge, a controller design method based on Nonlinear Generalized Minimum Variance (NGMV) be proposed by this paper. The NGMV controller of an aero-engine is designed and implemented, and its excellent control performance is proved by comparison with the traditional PI controller.