丁晓喜 - 重庆大学 - 机械工程学院

个人简介

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 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). 1. Xiaoxi 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%）。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.

研究领域

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

近期论文

查看导师最新文章（温馨提示：请注意重名现象，建议点开原文通过作者单位确认）

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.