This paper presents a deep learning-based framework for predicting the dynamic performance of suspension systems in multi-axle vehicles, emphasizing the integration of machine learning with traditional vehicle dynamics modeling. A Multi-Task Deep Belief Network Deep Neural Network (MTL-DBN-DNN) was developed to capture the relationships between key vehicle parameters and suspension performance metrics. The model was trained on data generated from numerical simulations and demonstrated superior prediction accuracy compared to conventional DNN models. A comprehensive sensitivity analysis was conducted to assess the impact of various vehicle and suspension parameters on dynamic suspension performance. Additionally, the Suspension Dynamic Performance Index (SDPI) was introduced as a holistic measure to quantify overall suspension performance, accounting for the combined effects of multiple parameters. The findings highlight the effectiveness of multitask learning in improving predictive models for complex vehicle systems.

翻译：本文提出了一种基于深度学习的框架，用于预测多轴车辆悬架系统的动态性能，重点强调了机器学习与传统车辆动力学建模的融合。研究开发了一种多任务深度信念网络深度神经网络（MTL-DBN-DNN），以捕捉关键车辆参数与悬架性能指标之间的关系。该模型基于数值仿真生成的数据进行训练，相较于传统的DNN模型，展现出更优的预测精度。研究进行了全面的敏感性分析，以评估各种车辆及悬架参数对动态悬架性能的影响。此外，本文引入了悬架动态性能指数（SDPI）作为一个综合度量，用于量化整体悬架性能，该指数综合考虑了多个参数的共同作用。研究结果突显了多任务学习在改进复杂车辆系统预测模型方面的有效性。