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    • Study on the Prediction Method of Diesel Engine Piston Ring Pack Blow-by Based on Neural Network
    DOI：10.13949/j.cnki.nrjgc.2024.06.007
    Key Words:diesel engine  piston ring pack  blow-by  prediction model  particle swarm optimization（PSO）
    Author NameAffiliationE-mail
    WU Yue State Key Laboratory of Engines， Tianjin University， Tianjin 300072，China wuyue220@tju.edu.cn 
    LIANG Xingyu* State Key Laboratory of Engines， Tianjin University， Tianjin 300072，China lxy@tju.edu.cn 
    TU Danhong China Shipbuilding Power Engineering Institute Co.， Ltd.， Shanghai 200129， China 18d00084@cspi.net.cn 
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    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.
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