摘要
为提升履带装甲车辆综合传动装置疲劳损伤的快速计算能力及其剩余寿命的准确评估性能,提出一种满足多工况条件的传动主轴运行载荷实时评估模型.提取特种车辆典型运行工况的特征参数,并采用K均值聚类及支持向量机方法实现特种车辆典型运行工况的实时判别;构建基于卷积-长短期记忆神经网络的多工况传动主轴运行载荷实时评估模型,采用贝叶斯算法对模型中学习率、神经网络隐含层单元数等超参数进行优化,以提高传动主轴运行载荷评估的准确性;依据特种车辆典型工况下的运行数据开展模型实例验证.结果表明,在换挡工况、转向工况及爬坡工况下,模型对传动主轴运行载荷评估结果的平均绝对百分比误差分别为0.150、0.014、0.006(5°坡工况)及0.004(10°坡工况),表明了本文模型在变工况条件下综合传动装置传动主轴运行载荷评估的有效性.
Abstract
To facilitate the rapid calculation of fatigue damage and accurate assessment of the remaining life of the integrated transmission system,a real-time evaluation model for the running load of the transmission main shaft under multiple operating conditions is proposed.The model first extracted characteristic parameters of typical operating conditions for special vehicles and realizeed real-time discrimination of these conditions using K-means clustering and support vector machine methods.Then,a transmission main shaft load evaluation model based on convolutional neural networks-long short-term memory was constructed.Bayesian algorithm was employed to optimize hyperparameters,such as learning rate and network depth,aiming to improve the accuracy of the transmission main shaft load evaluation.Finally,the model was validated through experimental data collected under typical operating conditions of special vehicles.The results show that under shifting,steering,and climbing conditions,the average relative errors of the model's load evaluation for the transmission main shaft are 0.150,0.014,0.006(5-degree slope),and 0.004(10-degree slope),respectively,indicating the effectiveness of the model in assessing the load of the integrated transmission system under variable operating conditions.
基金项目
机电系统测控北京市重点实验室开放课题资助项目(KF20222223203)
维普
万方数据