湖北汽车工业学院学报2026，Vol.40Issue(2)：14-19,6.DOI:10.3969/j.issn.1008-5483.2026.02.003
基于SA-APSO-BP的多工况汽车刹车片磨损预测
Wear Prediction of Automotive Brake Pads Under Multiple Conditions Based on SA APSO BP
摘要
Abstract
To address the issue of insufficient prediction accuracy for brake pad wear under complex working conditions,a hybrid model integrating a BP neural network with simulated annealing and adap-tive particle swarm optimization was proposed.Braking pressure,initial speed,and friction surface tem-perature were taken as inputs.The population was initialized via Piecewise chaotic mapping,and a dis-tance control factor,along with the Metropolis criterion,was incorporated to adaptively adjust parame-ters,thereby enhancing global optimization capability.Experimental results show that the proposed model achieves a coefficient of determination of 0.993,a mean absolute percentage error of 4.1%,and a mean absolute error of 0.002 6,significantly outperforming traditional models.This approach provides reliable support for the intelligent monitoring and maintenance of braking systems.关键词
刹车片磨损/自适应粒子群算法/模拟退火算法/反向传播神经网络Key words
brake pad wear/adaptive particle swarm optimization/simulated annealing algorithm/back-propagation neural network分类
机械制造引用本文复制引用
郭家瑞,侯俊..基于SA-APSO-BP的多工况汽车刹车片磨损预测[J].湖北汽车工业学院学报,2026,40(2):14-19,6.基金项目
汽车动力传动与电子控制湖北省重点实验室开放基金(ZDK12023B10) （ZDK12023B10）
湖北省教育厅科学技术研究计划项目(B2024069) （B2024069）
湖北汽车工业学院博士科研启动基金(BK202223) （BK202223）
