内燃机工程2024，Vol.45Issue(6)：60-70,11.DOI:10.13949/j.cnki.nrjgc.2024.06.007
基于神经网络的柴油机活塞环组窜气量预测方法研究
Study on the Prediction Method of Diesel Engine Piston Ring Pack Blow-by Based on Neural Network
摘要
Abstract
In response to the phenomenon of poor sealing in the engine,which leads to a decrease in engine power and economy,as well as damage to important components,a single cylinder test engine of a certain diesel engine was taken as the research object.The sealing performance of the piston ring pack was simulated and calculated.A back propagation neural network(BPNN)prediction model for gas leakage was established for five inputs including opening clearance,chamfer length,radial elasticity,working temperature,and one output of blow-by.Four algorithms were used to improve the prediction performance of the model,namely grey wolf optimization(GWO),whale optimization algorithm(WOA),genetic algorithm(GA),and particle swarm optimization(PSO).The results indicate that the PSO-BP prediction model has strong generalization ability and predictive performance for blow-by.The high accuracy and stability of the particle swarm optimization-back propagation(PSO-BP)prediction model provide a powerful decision support tool for engine design and maintenance,helping to achieve more accurate fault diagnosis and predictive maintenance,reduce operating costs,and improve the overall performance and economic benefits of the engine.关键词
柴油机/活塞环组/窜气量/预测模型/粒子群优化Key words
diesel engine/piston ring pack/blow-by/prediction model/particle swarm optimization(PSO)分类
能源科技引用本文复制引用
吴玥,梁兴雨,屠丹红..基于神经网络的柴油机活塞环组窜气量预测方法研究[J].内燃机工程,2024,45(6):60-70,11.基金项目
国家自然科学基金项目(52342606) （52342606）
工信部船机重大专项之船用发动机高可靠性设计和验证关键技术项目National Natural Science Foundation of China(52342606) （52342606）
Designing and Verifying Key Technologies of Marine Engines High Reliability in Major Special Project Ship Research Program of MIIT （）
