基于LQPSO-BP神经网络的柴油机轴系扭振预测与参数协同优化  

Prediction of Torsional Vibration and Parameter Collaborative Optimization of Diesel Engine Shaft System Based on LQPSO-BP Neural Network

作　　者：吴晓 王光辉 WU Xiao;WANG Guanghui(School of Automotive Engineering,Hubei University of Automotive Technology,Shiyan Hubei 442002)

机构地区：[1]湖北汽车工业学院汽车工程学院,湖北十堰442002

出　　处：《湖北理工学院学报》2025年第2期15-24,共10页Journal of Hubei Polytechnic University

摘　　要：针对柴油发动机曲轴扭振引发的疲劳断裂问题,提出一种融合改进量子粒子群算法与BP神经网络的协同优化方法。首先,构建13自由度曲轴扭振模型,识别4、4.5、6阶次为关键共振谐次,在4300 r/min下4阶振幅峰值达0.7°。其次,采用LQPSO算法,通过动态收缩-膨胀因子和Levy飞行策略,提升算法收敛速度23.6%。基于最优拉丁超立方抽样生成1000组样本,构建LQPSO-BP神经网络模型。优化后在4300 r/min与2800 r/min时振幅峰值分别降低64.3%与44.4%,频域响应最大值由282.67 Hz衰减至257.47 Hz,模型均方误差较传统BP降低70.9%,决定系数提升至0.967。A collaborative optimization method that integrates an improved quantum particle swarm algorithm with a BP neural network is proposed to address the issue of fatigue fracture caused by torsional vibration of diesel engine crankshafts.Firstly,a 13-degree-of-freedom crankshaft torsional vibration model was constructed,identifying the 4th,4.5th,and 6th orders as key resonance harmonics,with a peak amplitude of 0.7°at 4300 r/min for the 4th order.Secondly,the LQPSO algorithm was employed using dynamic contraction-expansion factors and Levy flight strategies,achieving a 23.6%improvement in convergence speed.Based on optimal Latin hypercube sampling,1000 sample sets were generated to construct the LQPSO-BP neural network model.Post-optimization results show 64.3%and 44.4%reductions in peak amplitudes at 4300 r/min and 2800 r/min respectively,while the maximum frequency response decreased from 282.67 Hz to 257.47 Hz.The model's mean square error decreased by 70.9%compared to the traditional BP,with the coefficient of determination reaching 0.967.

关 键 词：轴系扭振 量子粒子群算法 最优拉丁超立方抽样 LQPSO-BP神经网络 参数协同优化 

分 类 号：TK422[动力工程及工程热物理—动力机械及工程] TP183[自动化与计算机技术—控制理论与控制工程]