Statistical Characteristics Analysis of Damage Parameters of Aero-Engine Rotor System Based on Artificial Neural Network

Aero-engine is one of the core components of modern aerospace aircraft, and the performance and reliability of its rotor system are crucial to ensure the safe operation of aerospace aircraft. In this paper, a finite element model of the aero-engine rotor system is established to analyze the load state of the aero-engine rotor system under the combined effects of centrifugal load, aerodynamic load, temperature load, and rotational acceleration. In addition, considering the time required for the finite element analysis of the rotor system, a surrogate model based on an artificial neural network is developed to analyze the damage parameters of the key components of the aero-engine and the statistical characteristics of the damage parameters of the key components are quickly obtained using the developed surrogate model. The results show that the proposed artificial neural network-based surrogate model for damage parameters of the rotor system of aero-engines has good prediction accuracy and efficiency and can provide practical technical support for online condition monitoring and reliability assessment of large and complex machinery.