Deep learning-based modeling method for probabilistic LCF life prediction of turbine blisk  

作　　者：Cheng-Wei Fei Yao-Jia Han Jiong-Ran Wen Chen Li Lei Han Yat-Sze Choy 

机构地区：[1]Department of Aeronautics and Astronautics,Fudan University,Shanghai 200433,China [2]Department of Mechanical Engineering,Hong Kong Polytechnic University,Hong Kong 999077,China

出　　处：《Propulsion and Power Research》2024年第1期12-25,共14页推进与动力（英文）

基　　金：National Natural Science Foundation of China (Grant No.52375237);National Sci-ence and Technology Major Project (Grant J2022-IV-0012);Shanghai Belt and Road International Cooperation Project of China (Grant No.20110741700);China Postdoctoral Science Foundation (Grant No.2021M700783);Research Grants Council of the Hong Kong SAR of China (PolyU 15209520).

摘　　要：Turbine blisk is one of the typical components of gas turbine engines.The fatigue life of turbine blisk directly affects the reliability and safety of both turbine blisk and aeroengine whole-body.To monitor the performance degradation of an aeroengine,an efficient deep learning-based modeling method called convolutional-deep neural network(C-DNN)method is proposed by absorbing the advantages of both convolutional neural network(CNN)and deep neural network(DNN),to perform the probabilistic low cycle fatigue(LCF)life prediction of turbine blisk regarding uncertain influencing parameters.In the C-DNN method,the CNN method is used to extract the useful features of LCF life data by adopting two convolutional layers,to ensure the precision of C-DNN modeling.The two close-connected layers in DNN are employed for the regression modeling of aeroengine turbine blisk LCF life,to keep the ac-curacy of LCF life prediction.Through the probabilistic analysis of turbine blisk and the com-parison of methods(ANN,CNN,DNN and C-DNN),it is revealed that the proposed C-DNN method is an effective mean for turbine blisk LCF life prediction and major factors affecting the LCF life were gained,and the method holds high efficiency and accuracy in regression modeling and simulations.This study provides a promising LCF life prediction method for complex structures,which contribute to monitor health status for aeroengines operation.

关 键 词：Convolutional-deep neural network Low cycle fatigue Life prediction Turbine blisk Probabilistic prediction 

分 类 号：V23[航空宇航科学与技术—航空宇航推进理论与工程]