张航,鲍久圣,阴妍,曹婷,胡德平,苏文胜,朱晨钟,曹靖雨,顾永华.露天矿重型卡车制动摩擦温升特性及其智能预测模型[J].表面技术,2025,54(11):83-99, 172.
ZHANG Hang,BAO Jiusheng,YIN Yan,CAO Ting,HU Deping,SU Wensheng,ZHU Chenzhong,CAO Jingyu,GU Yonghua.Frictional Braking Temperature Rise Characteristics and Intelligent Prediction Model of Heavy-duty Mining Trucks in Open-pit Mines[J].Surface Technology,2025,54(11):83-99, 172 |
| 露天矿重型卡车制动摩擦温升特性及其智能预测模型 |
| Frictional Braking Temperature Rise Characteristics and Intelligent Prediction Model of Heavy-duty Mining Trucks in Open-pit Mines |
| 投稿时间：2024-10-08 修订日期：2025-03-21 |
| DOI：10.16490/j.cnki.issn.1001-3660.2025.11.007 |
| 中文关键词: 重型矿用卡车 制动温升特性 热-力耦合 预测模型 |
| 英文关键词:heavy-duty mining truck temperature rise characteristics thermal-mechanical coupling prediction model |
| 基金项目:江苏省科技成果转化专项资金(BA2023035);徐州市重点研发计划(KC22419);江苏高校优势学科建设工程资助项目(PAPD) |
| 作者 | 单位 |
| 张航 | 中国矿业大学 机电工程学院，江苏 徐州 221116 |
| 鲍久圣 | 中国矿业大学 机电工程学院，江苏 徐州 221116 |
| 阴妍 | 中国矿业大学 机电工程学院，江苏 徐州 221116 |
| 曹婷 | 中国矿业大学 机电工程学院，江苏 徐州 221116 |
| 胡德平 | 徐州徐工重型车辆有限公司，江苏 徐州 221112 |
| 苏文胜 | 江苏省特种设备安全监督检验研究院，南京 210036 |
| 朱晨钟 | 徐州徐工重型车辆有限公司，江苏 徐州 221112 |
| 曹靖雨 | 中国矿业大学 机电工程学院，江苏 徐州 221116 |
| 顾永华 | 江苏省特种设备安全监督检验研究院，南京 210036 |
|
| Author | Institution |
| ZHANG Hang | School of Mechanical and Electrical Engineering, China University of Mining and Technology, Jiangsu Xuzhou 221116, China |
| BAO Jiusheng | School of Mechanical and Electrical Engineering, China University of Mining and Technology, Jiangsu Xuzhou 221116, China |
| YIN Yan | School of Mechanical and Electrical Engineering, China University of Mining and Technology, Jiangsu Xuzhou 221116, China |
| CAO Ting | School of Mechanical and Electrical Engineering, China University of Mining and Technology, Jiangsu Xuzhou 221116, China |
| HU Deping | Xuzhou XCMG Heavy Vehicle Co., Ltd., Jiangsu Xuzhou 221112, China |
| SU Wensheng | Jiangsu Institute of Special Equipment Safety Supervision and Inspection, Nanjing 210036, China |
| ZHU Chenzhong | Xuzhou XCMG Heavy Vehicle Co., Ltd., Jiangsu Xuzhou 221112, China |
| CAO Jingyu | School of Mechanical and Electrical Engineering, China University of Mining and Technology, Jiangsu Xuzhou 221116, China |
| GU Yonghua | Jiangsu Institute of Special Equipment Safety Supervision and Inspection, Nanjing 210036, China |
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| 中文摘要: |
| 目的 针对露天矿重型卡车摩擦制动温升特性不明、危及行车安全的问题，研究其鼓式制动器在制动过程中的温升特性，并建立预测模型。方法 通过理论计算、仿真分析和试验研究相结合的方法，分析重型矿用卡车的制动模式，采用直接耦合法建立其鼓式制动器的热-力耦合模型;分析紧急制动、持续制动和循环多次制动等3种模式下，不同纵坡坡度、总质量和制动初速度对制动温升的影响;在实验室搭建矿车制动温升模拟试验系统，对仿真结果进行验证，在持续制动和循环多次制动模式下开展27组正交试验，获得8 876个样本数据;基于CNN-LSTM-Attention算法构建制动温升的智能预测模型，利用智能预测模型对3组新工况下的36个制动温升数据进行预测分析。结果 在持续制动模式下，平均预测误差为5.2 ℃，3种工况的相对误差不超过7%;在循环多次制动模式下，平均预测误差为4.79 ℃，3种工况的相对误差不超过5%。结论 所设计的智能预测模型具有较高的预测精度，适用于解决露天矿重型卡车的行车安全问题。 |
| 英文摘要: |
| This research focuses on the frictional braking temperature rise characteristics and intelligent prediction model of heavy-duty mining trucks in open-pit mines, aiming to address the potential safety risks resulting from unpredictable braking- induced temperature increase. The study is centered around the XG105 three-bridge rigid mining truck of XCMG Group. Initially, the three main braking modes including emergency braking, continuous braking, and cyclic multiple braking are thoroughly analyzed. A thermal- mechanical coupling model of the drum brake is then developed with the direct coupling method. In this process, the vehicle mass is equivalently distributed onto the brake drum to simplify the complex vehicle dynamics model. During braking, the energy conversion occurs, and a significant portion of the mechanical energy of the vehicle is transformed into heat in the braking system. A correction coefficient of 0.9 is adopted, signifying that approximately 90% of the mechanical energy is converted into heat absorbed by the brakes. This coefficient is crucial as it more accurately reflects the actual heat-generating situation in the braking process. Subsequently, a simulation test system for the brake temperature rise of mining trucks is established in the laboratory. In accordance with relevant standards, parameters such as load, longitudinal slope gradient, and initial braking speed are precisely set. The load conditions include no-load (35 t), full-load (105 t), and 30% over-load (126 t), while the longitudinal slope gradient values are set at 6%, 7%, and 8%. These parameter settings cover a wide range of actual operating conditions in open-pit mines. Under continuous braking and cyclic multiple braking modes, 27 sets of orthogonal experiments are conducted, generating a total of 8 876 sample data. This large-scale data collection is essential for comprehensively understanding the temperature rise characteristics under different braking conditions. Finally, an intelligent prediction model for brake temperature rise is constructed based on the CNN-LSTM-Attention algorithm. By means of Pearson correlation analysis, input features with a correlation coefficient exceeding 0.8 with the maximum braking temperature are carefully selected. These features, such as travel time, longitudinal slope gradient, initial braking speed, total quality, and braking frequency, are highly relevant to the temperature rise phenomenon. After a series of data preprocessing steps, including data cleaning, division, normalization, and transformation, the model is trained. The trained model shows remarkable prediction accuracy. In the continuous braking mode, under three new working conditions, the average prediction error is 5.2 ℃, and the relative error of the percentage does not exceed 7%. In the cyclic multiple braking mode, under three new working conditions, the average prediction error is 4.79 ℃, and the relative error of the percentage is within 5%. In summary, the thermal-mechanical coupling model effectively simulates different braking scenarios, providing a reliable way for analyzing the temperature rise process. The intelligent prediction model based on the CNN-LSTM-Attention algorithm can accurately forecast the maximum braking temperature rise. However, there are areas for improvement. Future research should concentrate on optimizing the friction coefficient and refining simulation parameters to make the model more adaptable to the complex and variable real-world conditions in open-pit mines. This study offers a solid theoretical basis for enhancing the driving safety of mining trucks and paves the way for further research in this field. |
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