基于智能神经网络的航空发动机运行安全分析

To improve the accuracy and calculation efficiency of aero-engine operation safety analysis, this paper proposes a time-varying safety analysis method, combining the characteristics of flight missions and aero-engine operation, and relying on data envelopment analysis, with Quick Access Recorder (QAR) as the analysis data. The operation safety of the aero-engine is analyzed by considering four factors: engine operation state, fuel/oil operation state, aircraft flight state, and external operation conditions. To solve the high nonlinearity and strong coupling of the factors affecting aero-engine operation safety, an intelligent neural network model (PSO/BR-ANN) is proposed. The proposed model is based on the Artificial Neural Network (ANN) algorithm and optimized by the improved Particle Swarm Optimization (PSO) algorithm and Bayesian Regularization (BR) algorithm. The time-varying aero-engine safety margin is obtained by analyzing the aero-engine operation safety of a B737-800 flight mission from Beijing to Urumqi, verifying the effectiveness of the method. The comparison of PSO/BR-ANN, random forest and ANN shows that PSO/BR-ANN improves the analysis accuracy and calculation efficiency. The proposed method and model can provide useful reference for aero-engine operation safety analysis, special case treatment, maintenance and design.