Research on Cyclic Time Domain Extrapolation of Diesel Engine Crankshaft Load Spectrum Based on SVR Model

3rd International Symposium on Resource Exploration and Environmental Science

生态环境科学

Xu, Jinhao^1 ; Luo, Qingguo^1 ; Jing, Qi^1 ; Liu, Xin^1 ; J., Lu

Institute of Vehicle Engineering, Army Academy of Armored Forces, Beijing

100072, China^1

关键词: Engine crankshafts;    Extrapolation methods;    Generalization ability;    Learning abilities;    Load sequences;    Machine learning models;    Research results;    Simulation calculation;   

Others  :  https://iopscience.iop.org/article/10.1088/1755-1315/300/4/042097/pdf
DOI  :  10.1088/1755-1315/300/4/042097

学科分类：环境科学（综合）

来源: IOP

【 摘 要 】

The load spectrum is primarily used to provide a dynamic raw input of a basic load change to the component for simulation calculations or fatigue tests of fatigue life. In the load spectrum compilation of engine crankshaft, in order to preserve the influence of load sequence effect on fatigue damage during extrapolation and improve the accuracy of time domain extrapolation, this paper proposes a cyclic time domain extrapolation method based on SVR model. The research results show that the machine learning model has good learning ability and generalization ability, and the time domain extrapolation method can better realize the expansion of the measured samples of the crankshaft.