佟庆彬

基本信息

教育背景

佟庆彬，教授、博士生导师。毕业于哈尔滨工业大学，获工学博士学位。次年进入北京交通大学电气工程博士后流动站，从事电气工程方向关于系统关键部件故障诊断和健康管理方面的研究工作。2016年3月至2017年3月，美国University of Missouri-Columbia 访问学者。教育部国家级人才计划评审专家，留学基金委评审专家，教育部学位与研究生评估专家。主持并参与国家自然科学基金、北京市自然科学基金、科技部“科技支撑”、国际科技合作项目、中国铁路总公司（原铁道部）重点项目、国家重点实验室项目等40余项科研项目，在国内外学术期刊发表SCI、EI检索论文60余篇。主要从事轨道交通、电力与电子、新能源下的人工智能和智能故障诊断方向研究，包括轨道交通装备关键部件的故障诊断与健康评估；电气设备关键部件在线监测与故障诊断；电子器件及组件的故障诊断和健康管理；大数据下的故障诊断、损伤评估及寿命预测；动态非线性、非平稳信号分析与处理等。

招收推免生、硕士、博士研究生，欢迎有志于人工智能和故障诊断的同学加入课题组！

欢迎具有传感器硬件设计经历的同学加入课题组！

（毕业生就业于国网、科研院所、高校等）

研究方向 ：

工作经历

2018.12－至今       北京交通大学，电气工程学院，教授，博士生导师

2016.10－2018.11 北京交通大学，电气工程学院，副教授，博士生导师

2016.03－2017.03 美国密苏里大学哥伦比亚分校，机械与航空工程系，访问学者

2012.12－2016.09 北京交通大学，电气工程学院，副教授，硕士生导师

2009.11－2012.11 北京交通大学，电气工程学院，讲师

研究方向

• 电工理论与新技术
• 电气工程

招生专业

• 电气工程硕士
• 电气工程博士

科研项目

Ø  基于深度学习的域自适应滚动轴承故障诊断方法研究，教育部中央高校基本科研业务费重点资助项目，2023-2025

Ø  基于深度迁移学习的轨道交通列车走行部滚动轴承故障诊断方法研究，北京市自然科学基金-丰台轨道交通前沿研究联合基金，2021-2024

Ø  数据驱动的地铁车辆牵引电机轴承智能故障诊断与模式识别方法研究，北京市自然科学基金，2021-2023

Ø  动车组牵引电机轴承在线安全监控技术研究，国家铁路局，2021-2022

近年来主持和参与的科研项目：

Ø  长大坡道困难工况下牵引运行技术研究，国家重点研发计划

Ø  润滑脂组成对高速铁路牵引电机轴承润滑脂使用寿命的影响规律研究，中石油 

Ø  动车组机械传动系统轴承测试与评价技术研究，原铁道部重点课题 

Ø  动车组机械传动系统牵引电机轴承润滑分析与评价技术研究，南车电机 

Ø  时速500公里高速试验列车异步牵引电动机，南车电机 

Ø  检修动车组轴箱轴承温升原因分析研究，四方厂 

Ø  含水条件下油膜轴承油润滑机理的研究，中石油 

Ø  160公里城际动车组项目接地方案分析及试验，中车唐山机车车辆有限公司

Ø  智能变电站中物联网无线传感单元电磁骚扰特征分析与滤波处理方法的研究，国家重点实验室  

Ø  基于激光扫描方法的螺纹参数检测技术研究，教育部中央高校基本科研业务费 

Ø  高速旋转机械振动检测的光纤传感技术研究，教育部中央高校基本科研业务费 

Ø  精密离心机测试系统关键技术研究，人才基金 

Ø  煤矿井下供电系统对安全监控系统的电磁干扰研究，煤炭科学研究院 

Ø  基于MMC高压柔性直流换流阀系统中关键设备的动态电磁特性研究，国家重点实验室开放课题 

Ø  煤矿安全监控装备电磁兼容关键技术合作研究，国际科技合作计划

教学工作

本科生：《电路》 
研究生：《电工理论与技术进展》 、《研究生学术写作能力训练》

论文/期刊

近年发表的主要论文：

30. Lu F, Tong Q*, Jiang X, et al. Prior knowledge embedding convolutional autoencoder: a single-source domain generalized fault diagnosis framework under small samples [J]. Computers in Industry, 2024, 164(2025)104169. (SCI)

29. Lu F, Tong Q*, Jiang X, et al. Envelope spectrum neural network with adaptive domain weight harmonization for intelligent bearing fault diagnosis under cross-machine scenarios [J]. Advanced Engineering Informatics, 2024, 62(2024)102787. (SCI)

28. Lu F, Tong Q*, Jiang X, et al. DPICEN: Deep Physical Information Consistency Embedded Network for Bearing Fault Diagnosis under Unknown Domain [J]. Reliability Engineering & System Safety, 2024, 252(2024) 110454. (SCI)

27. Lu F, Tong Q*, Jiang X, et al. Deep Multilayer Sparse Regularization Time-Varying Transfer Learning Networks With Dynamic Kullback–Leibler Divergence Weights for Mechanical Fault Diagnosis [J]. IEEE Transactions on Industrial Informatics, 2024: 1-11. http://dx.doi.org/10.1109/TII.2024.3438229. Early Access. (SCI) 

26. 路飞宇，佟庆彬*，姜学东等。一种基于条件度量迁移学习的机械故障诊断可解释方法. 仪器仪表学报，2024，45(7):250-262. (EI)

25. Feiyu Lu*, Qingbin Tong*, Xuedong Jiang, et al. Towards multi-scene learning: A Novel Cross-Domain Adaptation Model Based on Sparse Filter for Traction Motor Bearing Fault Diagnosis in High-Speed EMU. Advanced Engineering Informatics, 2024, 60 (2024) 102536. (SCI) 

24. Feng, Z.; Tong, Q*.; Jiang, X.; Lu, F.; Du, X.; Xu, J.; Huo, J. Deep Reconstruction Transfer Convolutional Neural Network for Rolling Bearing Fault Diagnosis. Sensors 2024, 24(2079):1-24. (SCI)

23. Feiyu Lu, Qingbin Tong*, Xuedong Jiang, et al. A deep targeted transfer network with clustering pseudo-label learning for fault diagnosis across different Machinesk. Mechanical Systems and Signal Processing, 2024, 213 (2024) :111344. (SCI) 

22. Qingbin Tong*, Shouxin Du, Xuedong Jiang, et al.  Interpretable parallel channel encoding convolutional neural network for bearing fault diagnosis[J]. Measurement Science and Technology, 2024, 35(2024): 066001. (SCI)

21. Yang J, Liu R, Tong Q*, et al. Multi-Objective Optimization of LCC-S-Compensated IPT System for Improving Misalignment Tolerance[J]. Applied Sciences, 2023, 13(6): 3666. (SCI)

20. Feiyu Lu, Qingbin Tong*, Ziwei Feng, Qingzhu Wan. Unbalanced Bearing Fault Diagnosis under Various Speeds based on Spectrum Alignment and Deep Transfer Convolution Neural Network. IEEE Transactions on Industrial Informatics, 2023, 19(7): 8295-8306. (SCI)

19. Tong Q*, Liu Z, Lu F, et al. A New De-Noising Method Based on Enhanced Time-Frequency Manifold and Kurtosis-Wavelet Dictionary for Rolling Bearing Fault Vibration Signal[J]. Sensors, 2022, 22(16): 6108.

18. Qingbin Tong*, Feiyu Lu, Ziwei Feng, et al. A Novel Method for Fault Diagnosis of Bearings with Small and Imbalanced Data Based on Generative Adversarial Networks. Applied Sciences, 2022, 12, 7346. (SCI)

17. Lu F, Tong Q*, Feng Z, et al. Explainable 1DCNN with demodulated frequency features method for fault diagnosis of rolling bearing under time-varying speed conditions[J]. Measurement Science and Technology, 2022, 33(9): 095022. (SCI)

16. An GP, Tong QB*, Zhang, YA, et al. A Parameter-Optimized Variational Mode Decomposition Investigation for Fault Feature Extraction of Rolling Element Bearings[J]. Mathematical Problems in Engineering, 2021, (2021).

15. Y Wang, Cao, J., Tong, Q., An, G., & Yan, H. Study on the thermal performance and temperature distribution of ball bearings in the traction motor of a high-speed EMU. Applied Sciences, 2020，10(12), 4373. (SCI)

14. Zhengwei Nie, Yuyi Lin, and Qingbin Tong. "Numerical simulations of two-phase flow in open-cell metal foams with application to aero-engine separators." International Journal of Heat and Mass Transfer，2018，127: 917-932. (SCI)

13. QINGBIN TONG*, QUAN YUAN, YUHUA ZHAO, FENG YUAN, ZHENGWEI NIE, YUYI LIN, JUNCI CAO, XIN WANG. A Method for Measurement of Aircraft Attitude Parameters Based on Sequence Screen-spot Imaging. IEEE Access, 2018, 6(1), 21566-21577. (SCI)

12. Shao Z, Shang Y, Tong Q, et al. Multiple color image encryption and authentication based on phase retrieval and partial decryption in quaternion gyrator domain[J]. Multimedia Tools & Applications, 2018:1-20. (SCI)

11. Qingbin Tong*, Junci Cao, BaozhuHan, DeliWang, Yuyi Lin,Weidong Zhang and Jianqiang Wang. A fault diagnosis approach for rolling element bearings based on dual-tree complex wavelet packet transform-improved intrinsic time-scale decomposition, singular value decomposition, and online sequential extreme learning machine. Advances in Mechanical Engineering, 2017, 9(12):1-12. (SCI)

10. Zhengwei Nie, Yuyi Lin, Qingbin Tong. Numerical investigation of pressure drop and heat transfer through open cell foams with 3D Laguerre-Voronoi model. International Journal of Heat and Mass Transfer, 2017, 113:819-839. (SCI)

9. Zhengwei Nie, Yuyi Lin, Qingbin Tong. Modeling structures of open cell foams. Computational Materials Science, 2017, 131:160-169. (SCI) 

8. QINGBIN TONG*; Junci Cao; Baozhu Han; Xiaodong Zhang; Zhengwei Nie;Jiamin Wang; Yuyi Lin; Weidong Zhang. A Fault Diagnosis Approach for Rolling Element Bearings Based on RSGWPT-LCD Bilayer Feature Screening and ExtremeLearning Machine. IEEE Access, 2017. 5(1), 5515-5530. (SCI)

7. Qingbin Tong*, Baozhu Han, Yuyi Lin, Weidong Zhang, Junci Cao and Xiaodong Zhang. A Fault Feature Detection Approach for Fault Diagnosis of Rolling Element Bearings Based on Redundant Second Generation Wavelet Packet Transform and Local Characteristic-Scale Decomposition. Journal of Vibration Engineering & Technologies, 2017, 5(1), 101-110. (SCI)

6. Qingbin Tong*; Zhanlong Sun; Zhengwei Nie; Yuyi Lin. Sparse decomposition based on ADMM dictionary learning for fault feature extraction of rollingelement bearing. JOURNAL OF VIBROENGINEERING, 2016, 18(8), 5204-5216. (SCI)

5. Qingbin Tong*; Baozhu Han; Yuyi Lin; Weidong Zhang. Multi-fault diagnosis for rolling element bearings based on intrinsic mode function screening and optimized least squares support vector machine. JOURNAL OF VIBROENGINEERING, 2016, 18(7), 4430-4448. (SCI)

4. Tong Q*, Jiao C, Huang H, et al. An automatic measuring method and system using laser triangulation scanning for the parameters of a screw thread. Measurement Science and Technology, 2014, 25(3): 035202. (SCI)

3. Tong Q*, Han B, Wang D, et al. A novel laser-based system for measuring internal thread parameters. Journal of Russian Laser Research, 2014, 35(3): 307-316. (SCI) 

2. Qing-bin T*. Accurate measurement method of the thickness of a quartz pendulous reed by combining polarized reflectance and vision image. Journal of Russian Laser Research, 2011, 32(6): 537-546. (SCI)

1. Qing-Bin T*, Hui-Ping M, Li-Hua L, et al. Measurements of radiation vibrations of turbomachine blades using an optical-fiber displacement-sensing system. Journal of Russian Laser Research, 2011, 32(3): 216-229. (SCI)

专著/译著

专利

1 发明专利：基于可解释1DCNN模型的滚动轴承故障诊断方法及系统. 发明人：佟庆彬, 路飞宇, 冯子微等。

软件著作权

获奖与荣誉

1、指导学生参加 “2011年第四届全国大学生节能减排社会实践与科技竞赛”的项目获国家三等奖 

2、荣获2013年度“电气支柱”奖

社会兼职

• 教育部国家级人才评审专家
  

• 留学基金委评审专家

• 教育部学位与研究生评估专家
• 《IEEE Transactions on Industrial Informatics》、《Mechanical Systems and Signal Processing》、《IEEE Transactions on Instrumentation & Measurement》、《IEEE Sensors Journal 》、《Measurement Science and Technology 》、《Chinese Optics Letters 》、浙江大学学报、北京理工大学学报、北京化工大学等期刊审稿人