丁晓喜

姓        名: 丁晓喜

出生年月: 1989/12 

学历学位: 工学博士

技术职务: 师资博后/讲师

电子邮箱: dxxu@cqu.edu.cn

个人简历


主要研究方向

     机械设备智能诊断与健康监控平台

     数据挖掘与模式识别、机器学习

     压缩感知与大数据分析

     麦克风阵列检测   

毕业研究生去向


主要研究经历、荣誉称号、获奖情况、社会兼职等

    2017.08-至今         重庆大学                     机械工程                                 师资 

    2012.08-2017.07    中国科学技术大学     精密机械与精密仪器系         硕博

    2008.09-2012.07    中国科学技术大学     机械设计制造及其自动化     本科


承担科研项目情况:

    国家自然科学基金青年项目(主持)                 2019-2021

    重庆市基础与前沿研究计划项目(主持)         2019-2021

    中国博士后科学基金项目(主持)                     2018-2020

    重庆市博士后特别资助项目(主持)                 2018-2020


获奖情况:

2丁晓喜, 李泉昌,黄文彬,何清波,邵毅敏. 基于移不变时频流形学习的旋转机械瞬态特征提取, 2018年设备监测诊断与维护学术会议, 包头。(荣获优秀论文奖).

1Xiaoxi Ding, Qingbo He. Short-time smoothness spectrum: A novel demodulation method for bearing fault diagnosis. 2016 International Symposium on Flexible Automation, August 1-3, 2016 in Cleveland, Ohio. USA. (荣获Best Paper Award)

     截止目前论文已累计被引用260余次、其中单篇最高引用87次。2017年7月发表于IEEE TIM的论文从2019年9月至今获评ESI高被引论文(本领域排名前1%)。


公开发表期刊论文

     已在国内外重要学术期刊或国际重要学术会议上发表SCI/EI论文25余篇,参与编写1部英文专著章节,专利3项。

12. Quanchang Li, Xiaoxi Ding*, Tao Wang, Mingkai Zhang, Wenbin Huang, Yimin Shao, Time-frequency synthesis analysis for complex signal of rotating machinery via variational mode manifold reinforcement learning [J], Proceedings of the Institution of Mechanical Engineers Part C-Journal of Mechanical Engineering Science, 2019.

11. Deqi Zhang, Xiaoxi Ding*, Wenbin Huang, Qingbo He, Yimin Shao. Transient signal analysis using parallel time-frequency manifold filtering for bearing health diagnosis [J]. IEEE Access, 2019

10. 李泉昌,何清波,邵毅敏,丁晓喜*,基于移不变时频流形自学习的旋转机械故障信号特征增强 [J], 振动工程学报, 2019.

9.  Li, Quanchang, Xiaoxi Ding*, Wenbin Huang, Qingbo He, and Yimin Shao. Transient feature self-enhancement via shift-invariant manifold sparse learning for rolling bearing health diagnosis. [J] Measurement 148 (2019): 106957.

8.  Xiaoxi Ding*, Qingbo He, Yimin Shao, Wenbin Huang. Transient Feature Extraction Based on Time-Frequency Manifold Image Synthesis for Machinery Fault Diagnosis [J]. IEEE Transactions on Instrumentation and Measurement, 2019. 

7.  Xiaoxi Ding*, Quanchang Li, Lun Lin, Qingbo He, Yimin Shao. Fast time-frequency manifold learning and its reconstruction for transient feature extraction in rotating machinery fault diagnosis [J]. Measurement, vol. 141, pp: 380-395, 2019. 

6.  Xiaoxi Ding, Qingbo He. Energy-fluctuated multiscale feature learning with deep convnet for intelligent spindle bearing fault diagnosis [J]. IEEE Transactions on Instrumentation and Measurement, vol. 66, pp: 1926-1935, 2017. 

5.  Xiaoxi Ding, Qingbo He. Time-frequency manifold sparse reconstruction: A novel method for bearing fault feature extraction [J]. Mechanical Systems and Signal Processing, vol. 80, pp: 392-413, 2016.

4.  Qingbo He, Xiaoxi Ding. Sparse representation based on local time–frequency template matching for bearing transient fault feature extraction [J]. Journal of Sound and Vibration, vol. 370, pp: 424-443, 2016.

3.  Xiaoxi Ding, Qingbo He, Nianwu Luo. A fusion feature and its improvement based on locality preserving projections for rolling element bearing fault classification [J]. Journal of Sound and Vibration, vol. 335, pp: 367-383, 2015.

2. 丁晓喜, 何清波*. 基于WPD和LPP的设备故障诊断方法研究 [J], 振动与冲击, vol. 33, no. 3, pp. 55–59, 2014.

1.  Qingbo He, Xiaoxi Ding, Pan Yuanyuan. Machine fault classification based on local discriminant bases and locality preserving projections [J]. Mathematical Problems in Engineering,2014.


专著

1. Q. He*, X. Ding, “Time-Frequency Manifold for Machinery Fault Diagnosis”, in Book: Structural Health Monitoring: An Advanced Signal Processing Perspective, Eds: R. Yan, X. Chen and S. C. Mukhopadhyay, Springer, 2017.