面向列车轮对轴承的摩擦电自供能智能传感及数字孪生映射设计

doi:10.3901/JME.260478

机械工程学报 ›› 2026, Vol. 62 ›› Issue (12): 98-106.doi: 10.3901/JME.260478

• 特邀专栏：数字孪生赋能的高端装备智能运维 • 上一篇

扫码分享

面向列车轮对轴承的摩擦电自供能智能传感及数字孪生映射设计

王松¹, 郑晨², 姚德臣³, 高帅⁴, 韩勤锴¹, 罗均⁴, 褚福磊¹

1. 清华大学机械工程系北京 100084;
2. 北京工商大学计算机与人工智能学院北京 100048;
3. 北京建筑大学机电与车辆工程学院北京 100044;
4. 重庆大学机械与运载工程学院重庆 400044

收稿日期:2025-09-21 修回日期:2026-03-08 发布日期:2026-08-03
作者简介:王松，男，1998年出生，博士研究生。主要研究方向为能量采集与智能轴承。E-mail:song-wan24@mails.tsinghua.edu.cn
韩勤锴(通信作者)，男，1982年出生，博士，副研究员。主要研究方向为转子系统动力学与智能零部件设计。E-mail:hanqinkai@mail.tsinghua.edu.cn
基金资助:
国家自然科学基金(12272199)、摩擦学国家重点实验室(SKLT2021D11)和国家重点研发计划(2024YFB3409102)资助项目。

Design of Triboelectric Self-powered Smart Sensing and Digital Twin Mapping for Train Wheelset Bearings

WANG Song¹, ZHENG Chen², YAO Dechen³, GAO Shuai⁴, HAN Qinkai¹, LUO Jun⁴, CHU Fulei¹

1. Department of Mechanical Engineering, Tsinghua University, Beijing 100084;
2. School of Artificial Intelligence, Beijing Technology and Business University, Beijing 100048;
3. School of Electromechanical and Vehicle Engineering, Beijing University of Civil Engineering and Architecture, Beijing 100044;
4. College of Mechanical Engineering, Chongqing University, Chongqing 400044

Received:2025-09-21 Revised:2026-03-08 Published:2026-08-03

摘要/Abstract

摘要： 随着列车提速与高密度运营，轮对安全裕度被持续压缩，双列圆锥滚子轴承作为列车轮对的关键旋转承载件，其健康状况直接决定整辆列车的运行安全、可靠性与全寿命运维成本。针对现有在线监测依赖外接供电、传感器布置受限等瓶颈，提出一种与保持架同轴对称、超紧凑集成于轴承内部的摩擦发电机原位传感模块(Triboelectric-nanogenerator-based in-situ sensing module，TISM)，实现对双列圆锥滚子轴承两侧保持架-滚动体相对运动信息的自供能同步采集。构建TISM输出信号与转速、载荷等关键运行参数间的定性映射关系，为轴承的实时状态识别提供可靠依据。结果表明，TISM具备高灵敏度与自供能特性，且在材料选择和设计间隙方面表现出卓越的鲁棒性，能够为构建列车轮对轴承数字孪生体提供关键的多物理场参数支撑。在轨道车辆横向动力学实验台上的应用展示表明，TISM在复杂变速工况下依然表现出优异的监测有效性与自传感能力。通过对TISM输出信号进行时频特征分析，可实现对轴承运行状态的实时感知，并能精确捕捉保持架打滑等早期异常行为特征。研究展示了基于TISM的智能零部件孪生单体的虚实互动新模式，为滚动轴承智能化设计与自供能监测提供技术参考，并有望由此推广至列车整体系统的数字孪生形态。

关键词: 摩擦纳米发电机, 列车轮对, 智能轴承, 自供电传感, 数字孪生

Abstract: With the increasing speed and operational density of trains, the safety margin of wheelsets is progressively diminishing. As a critical rotating and load-bearing component, the health of the double-row tapered roller bearing directly governs the operational safety, reliability, and life-cycle cost of the entire vehicle. To overcome the limitations of existing online monitoring systems, such as their reliance on external power and restricted sensor placement, this study proposes a Triboelectric Nanogenerator (TENG)-based in-situ sensing module (TISM). This module is designed to be coaxially mirror-symmetric with the cage and ultra-compactly integrated within the bearing. This configuration enables the self-powered, synchronous acquisition of relative motion data between the cage and rollers on both sides of the double-row bearing. Furthermore, the study establishes a qualitative mapping between the TENG output signal and key operational parameters—including rotational speed, load, and lubrication status—providing a solid foundation for real-time bearing condition identification. Experimental results demonstrate that the monitoring scheme possesses high sensitivity and self-powering capabilities. It also exhibits excellent robustness in terms of material selection and design clearance，providing crucial multi-physics parameter support for the construction of a digital twin for the train wheelset bearing. Validation on a railway vehicle lateral dynamics test rig confirms the TISM’s excellent monitoring effectiveness and self-sensing capabilities under complex variable-speed conditions. Through time-frequency analysis of the acquired signals, real-time perception of the bearing's operating state and precise detection of early fault signatures，such as cage slip, can be achieved. This research provides a new technical paradigm for the intelligent design and self-powered monitoring of rolling bearings, showcases a cyber-physical interaction model for a component-level digital twin，and has the potential to be extended to the digital twin framework of the entire train system.

Key words: triboelectric nanogenerator, train wheelset, smart bearing, self-powered sensing, digital twin

中图分类号:

王松, 郑晨, 姚德臣, 高帅, 韩勤锴, 罗均, 褚福磊. 面向列车轮对轴承的摩擦电自供能智能传感及数字孪生映射设计[J]. 机械工程学报, 2026, 62(12): 98-106.

WANG Song, ZHENG Chen, YAO Dechen, GAO Shuai, HAN Qinkai, LUO Jun, CHU Fulei. Design of Triboelectric Self-powered Smart Sensing and Digital Twin Mapping for Train Wheelset Bearings[J]. Journal of Mechanical Engineering, 2026, 62(12): 98-106.

导出引用管理器 EndNote|Reference Manager|ProCite|BibTeX|RefWorks

链接本文: http://www.cjmenet.com.cn/CN/10.3901/JME.260478

http://www.cjmenet.com.cn/CN/Y2026/V62/I12/98

参考文献

[1] 周济. 智能制造–“中国制造2025”的主攻方向[J]. 中国机械工程，2015(17):2273-2284. ZHOU Ji. Intelligent manufacturing-main direction of “Made in China 2025”[J]. China Mechanical Engineering，2015(17):2273-2284.
[2] 伍朝辉，武晓博，王亮. 交通强国背景下智慧交通发展趋势展望[J]. 交通运输研究，2019，5(4):26-36. WU Zhaohui，WU Xiaobo，WANG Liang. Prospect of smart transportation development trend under the background of building a transportation powerhouse[J]. Transport Research，2019，5(4):26-36.
[3] 何强，李安玲，张继祝，等. 高速列车轴承的发展趋势[J]. 徐州工程学院学报，2012，27(1):58-64. HE Qiang，LI Anling，ZHANG Jizhu，et al. Development trend of high-speed train bearings[J]. Journal of Xuzhou Institute of Technology，2012，27(1):58-64.
[4] 刘文朋，杨绍普，刘永强，等. 轮轨激励下高速列车轴箱轴承故障诊断方法研究[J]. 机械工程学报，2025，61(6):249-259. LIU Wenpeng，YANG Shaopu，LIU Yongqiang，et al. Research on fault diagnosis method of high-speed train axle box bearing under wheel-rail excitation[J]. Journal of Mechanical Engineering，2025，61(6):249-259.
[5] 楚明，杨林川，王志伟，等. 曲线几何参数及制动工况对列车轴箱轴承动态特性影响研究[J]. 机械工程学报，2025，61(1):187-198. CHU Ming，YANG Linchuan，WANG Zhiwei，et al. Study on the influence of curve geometric parameters and braking conditions on dynamic characteristics of train axle box bearings[J]. Journal of Mechanical Engineering，2025，61(1):187-198.
[6] CUI Lingli，LI Wenjie，WANG Xin，et al. A novel quantitative diagnosis method for rolling bearing faults based on digital twin model[J]. ISA Transactions，2025，157:381-391.
[7] 刘大同，郭凯，王本宽，等. 数字孪生技术综述与展望[J]. 仪器仪表学报，2018，39(11):1-10. LIU Datong，GUO Kai，WANG Benkuan，et al. Review and perspective of digital twin technology[J]. Chinese Journal of Scientific Instrument，2018，39(11):1-10.
[8] 李恒奎，卫昱乾，杨岗. 动车组轴承轨旁声学故障诊断研究综述[J]. 轴承，2022(11):1-7. LI Hengkui，WEI Yuqian，YANG Gang. Review on wayside acoustic fault diagnosis of EMU bearings[J]. Bearing，2022(11):1-7.
[9] SUN Bojun，SHENG Zixin，SONG Peng，et al. State-of-the-art detection and diagnosis methods for rolling bearing defects:A comprehensive review[J]. Applied Sciences，2025，15(2):1001.
[10] 陈金海，李伟，张文远，等. 智能滚动轴承监测方法与技术研究现状综述[J]. 机械强度，2021，43(3):509-516. CHEN Jinhai，LI Wei，ZHANG Wenyuan，et al. Review on current research status of monitoring methods and technologies for smart rolling bearings[J]. Journal of Mechanical Strength，2021，43(3):509-516.
[11] NIU Simiao，WANG Zhonglin. Theoretical systems of triboelectric nanogenerators[J]. Nano Energy，2015，14:161-192.
[12] KIM W，KIM D，TCHO I，et al. Triboelectric nanogenerator:Structure，mechanism，and applications[J]. ACS Nano，2021，15(1):258-287.
[13] 高帅，韩勤锴，褚福磊. 具备实时动态稳定性监测特性的高速混合陶瓷摩擦电滚动轴承[J]. 机械工程学报，2024，60(15):80-88. GAO Shuai，HAN Qinkai，CHU Fulei. High-speed hybrid ceramic triboelectric rolling bearing with real-time dynamic stability monitoring capability[J]. Journal of Mechanical Engineering，2024，60(15):80-88.
[14] HAN Qinkai，JIANG Ziyuan，XU Xueping，et al. Self-powered fault diagnosis of rolling bearings based on triboelectric effect[J]. Mechanical Systems and Signal Processing，2022，166:108382.
[15] 高帅，韩勤锴，褚福磊. 航空发动机数字孪生驱动的高精度摩擦电轴承传感器[J]. 航空学报，2024，45(21):93-105. GAO Shuai，HAN Qinkai，CHU Fulei. High-precision triboelectric bearing sensor driven by digital twin of aero-engine[J]. Acta Aeronautica et Astronautica Sinica，2024，45(21):93-105.
[16] LIU Ruoshui，LI Hengyu，WANG Pengfei，et al. Synchronous in-situ self-powered monitoring method for speed and skidding of high-speed bearing based on triboelectric nanogenerator[J]. Advanced Functional Materials，2025:2505831.
[17] 陈书恒，李猛，刘恒，等. 采用频谱细化的超声法在线测量滚动轴承保持架转速[J]. 西安交通大学学报，2020，54(7):139-145. CHEN Shuheng，LI Meng，LIU Heng，et al. Online measurement of rolling bearing cage speed by ultrasonic method with spectrum refinement[J]. Journal of Xi’an Jiaotong University，2020，54(7):139-145.
[18] 薛梓涵，赵自强，葛翔宇，等. 滚动轴承润滑膜厚及保持架转速的超声测量[J]. 轴承，2024(2):46-54. XUE Zihan，ZHAO Ziqiang，GE Xiangyu，et al. Ultrasonic measurement of lubricating film thickness and cage speed of rolling bearings[J]. Bearing，2024(2):46-54.

面向列车轮对轴承的摩擦电自供能智能传感及数字孪生映射设计

Design of Triboelectric Self-powered Smart Sensing and Digital Twin Mapping for Train Wheelset Bearings

PDF

可视化

摘要/Abstract

引用本文

使用本文

参考文献

相关文章 15

编辑推荐

Metrics

本文评价

[1]	侯宏瑞, 宋道森, 张广明, 李印, 郭辰旭, 付志国, 周伟, 王萌杰, 段培开, 兰红波. 高分辨水溶性牺牲模具增材制造方法及应用研究[J]. 机械工程学报, 2026, 62(9): 372-382.
[2]	杨毅晟, 李明, 杨泽源, 闫喜强, 严思杰, 丁汉. 数字技术赋能风洞全生命周期体系[J]. 机械工程学报, 2026, 62(7): 97-113.
[3]	王金龙, 宣泳辉, 冀秀坤, 鲍永杰, 石泽宇. 基于单向降阶高保真代理模型的CFRP机翼力学状态数字孪生研究[J]. 机械工程学报, 2026, 62(7): 126-138.
[4]	罗忠, 李洪雨, 李雷, 董胤哲. 数字孪生驱动的转子系统变工况难测点响应实时预测方法[J]. 机械工程学报, 2026, 62(7): 221-233.
[5]	杨泽浩, 董威, 黄思翰, 阴艳超, 董李扬, 郑祖杰. 基于数字孪生和强化学习的智能生产线实时调度仿真优化方法[J]. 机械工程学报, 2026, 62(5): 12-25.
[6]	许鸿伟, 刘丽兰, 张洁, 秦威, 邢宏文, 汪玮, 刘思仁, 吕佑龙. 工业大模型驱动的航空智能制造AI孪生控制方法体系[J]. 机械工程学报, 2026, 62(5): 61-73.
[7]	吴锦辉, 陈永昶, 吴亚男, 郝会乾, 袁翔, 陶友瑞. 数字孪生驱动的反射面天线运行状态监测与虚拟调试方法[J]. 机械工程学报, 2026, 62(5): 263-273.
[8]	杨亮亮, 龚壮壮, 何西旺, 王沐晨, 闵强, 阚子云, 宋学官. 面向结构损伤识别的数字孪生建模[J]. 机械工程学报, 2026, 62(4): 328-341.
[9]	刘素艳, 董一林, 乔一鸣, 马增强. 基于改进蜣螂优化算法的轴承数字孪生模型的动态故障注入研究[J]. 机械工程学报, 2026, 62(3): 446-457.
[10]	王世博, 葛世荣, 管增伦, 周士林, 王丽杰, 李雪峰, 王赟, 袁晓明, 马广军. 面向数字孪生的采煤机滚筒实时截割载荷模型研究[J]. 机械工程学报, 2026, 62(3): 479-491.
[11]	刘海波, 邓平, 迟庆宇, 刘天然, 刘阔, 李特, 黄祖广, 刘行健, 薄其乐, STEVEN Y LIANG, 王永青. 智能加工中数据使能技术与应用[J]. 机械工程学报, 2026, 62(2): 407-444.
[12]	冯珂, 张紫琳, 倪清, 杨彬, 胡亚安, 孟理华, 雷亚国. 认知数字孪生赋能航空装备：新范式与研究前沿[J]. 机械工程学报, 2026, 62(12): 1-21.
[13]	胡文扬, 李奇, 林起鉴, 王天杨, 阎绍泽, 褚福磊. 面向旋转机械健康监测和早期故障检测的Diffusion-Ramanujan数字孪生架构[J]. 机械工程学报, 2026, 62(12): 77-86.
[14]	朱本然, 潮群, 王忠睿, 刘成良. 边云协同下数字孪生驱动的齿轮传动系统在线监测技术[J]. 机械工程学报, 2026, 62(12): 87-97.
[15]	李孝斌, 黄文明, 江沛, 尹超, 曹华军. 数字孪生驱动的承压设备设计制造运维跨阶段数据集成方法[J]. 机械工程学报, 2026, 62(11): 48-60.