性别： 男
毕业院校： 中国科学技术大学
所在单位： 机械与运载工程学院
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个人简介Personal Profile

姓 名： 丁晓喜
出生年月：1989/12

学历学位： 工学博士
技术职务： 讲师
电子邮箱：dxxu@cqu.edu.cn
办公地点：重庆大学A区机械传动国家重点实验室209


聚焦前沿、交叉融通、夯实应用
欢迎各学科优秀学子报考重大高端装备智能轴承团队！
研究经历
2020.04-至今 重庆大学机械工程 讲师
2017.08-2020.04 重庆大学机械工程 师资博士后
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


主持科研项目情况：
9. 2019/01-2021/12, National Natural Science Foundation of China Youth Science Foundation of China, (**),PI
8. 2020/01-2022/12, National Key Research and Development Program of Ministry of Science and TechnologySubproject (2019YFB**),PI
7. 2020/06-2022/06, Chongqing technical innovation and application development special project subtopic(cstc2020jscx-msxmX0194),PI
6. 2021/01-2023/12, Chongqing outbound Chongqing postdoctoral research project (2020LY10),PI
5. 2020/01-2021/12, Operating expenses for basic scientific research in central colleges and universities, (2020CDJGFCD002),PI
4. 2020/10-2021/10, Intel products (Chengdu) Co., Ltd. (H**),PI
3. 2019/07-2022/06, Basic Science and Frontier Technology Research Special Project of Chongqing Science and Technology Planning Project-General Project, (cstc2019jcyj-msxmX0346),PI
2. 2018/07-2020/06, Special Grant for Chongqing postdoctoral researcher research project (XmT**),PI
1. 2018/01-2019/12, China Postdoctoral Program,PI



获奖情况：
8. 2020,安徽省自然科学二等奖（第五完成人）
7. 2018, 优秀论文奖，全国设备监测诊断与维护学术会议，2018
6. 2017,中科院院长优秀奖，中国科学研究院
5. 2017, 优秀毕业生，中国科学技术大学
4. 2016,Best Paper Award (in Application),International Symposium on Flexible Automation, ISFA 2016（柔性制造学术会议）
3. 2016, 研究生国家奖学金（博士）
2. 2014, 研究生国家奖学金（硕士）
1. 2007, 全国数学竞赛一等奖，中国数学会


公开发表期刊论文
以第一作或通讯作者发表SCI/EI论文26篇，其中SCI论文16篇，包括中科院TOP期刊IEEE T Ind Inform IF=9.11等论文；截止目前论文已累计被引超过500余次，单篇最高引用超过200次，授权专利3项、专著章节1篇；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%）。
J-21. Yinjun Wang, Liling Zeng, Liming Wang, Yimin Shao, Yongxiang Zhang*,Xiaoxi Ding*, An Efficient Incremental Learning of Bearing Fault Unbalanced Dataset via Filter StyleGAN [J],IEEE Transactions on Instrumentation and Measurement, 2021.
J-20. Yinjun Wang,Xiaoxi Ding, Qiang Zeng, Liming Wang* and Yimin Shao*, Intelligent rolling bearing fault diagnosis via vision ConvNet [J],IEEE Sensors Journal, 2020
J-19. Lei Dai, Quanchang Li, Yijie Chen,Xiaoxi Ding*, Wenbin Huang, Yimin Shao, Complex scale feature extraction for gearbox via adaptive multi-mode manifold learning [J], Measurement, 2020.
J-18.QuanchangLi,Xiaoxi Ding*, Qingbo He, Wenbin Huang, Yimin Shao, Manifold Sensing-based Convolution Sparse Self-Learning for Defective Bearing Morphological Feature Extraction [J].IEEE Transactions on Industrial Informatics, 2020 (IF=9.11，中科院一区TOP期刊)
J-17.Xiaoxi Ding, Lun Lin, Dong He, Liming Wang, Wenbin Huang and Yimin Shao*. A Weight Multi-Net Architecture for Bearing Fault Classification under Complex Speed Conditions [J].IEEE Transactions on Instrumentation and Measurement. 2020
J-16Xiaoxi Ding; Wei Li; Jitao Xiong; Yanfeng Shen; Wenbin Huang*，A flexible laserultrasound transducer for Lamb wave based structural health monitoring [J],Smart Materials and Structures,2020
J-15ZhiboZhang, Siping Zhong, Wenbin Huang,Xiaoxi Ding*,A WirelessDemodulationMethod for Acoustic Emission Sensing [J], IEEE Sensors Journal, 2020
J-14. Xiaoxi Ding*,Qingbo He, Yimin Shao, WenbinHuang. Transient Feature Extraction Based on Time-Frequency Manifold ImageSynthesis for Machinery Fault Diagnosis [J].IEEE Transactions onInstrumentation and Measurement, 2019, 68(11): 4242-4252.
J-13.Quanchang Li,Xiaoxi Ding*,Tao Wang, Mingkai Zhang, Wenbin Huang, Yimin Shao,Time-frequency synthesis analysis for complex signal of rotating machinery viavariational mode manifold reinforcement learning [J], Proceedings of theInstitution of Mechanical Engineers Part C-Journal of Mechanical EngineeringScience, 2019: 7688.
J-12.Deqi Zhang,Xiaoxi Ding*, Wenbin Huang, Qingbo He, Yimin Shao. Transient signal analysis using paralleltime-frequency manifold filtering for bearing health diagnosis [J]. IEEE Access, 2019, 7: 175277-175289
J-11.Li, Quanchang,Xiaoxi Ding*,Wenbin Huang, Qingbo He, and Yimin Shao. Transientfeature self-enhancement via shift-invariant manifold sparse learning forrolling bearing health diagnosis. [J] Measurement 148 (2019): 106957.
J-10.Xiaoxi Ding*,Liming Wang, WenbinHuang. Feature Clustering Analysis Using Reference Model towards MachinePerformance Degradation Assessment [J]. Shock and Vibration, 2020.
J-9.Xiaoxi Ding*,Quanchang Li, Lun Lin, Qingbo He, Yimin Shao. Fast timefrequencymanifold learning and its reconstruction for transient feature extraction inrotating machinery fault diagnosis [J]. Measurement, vol. 141, pp: 380-395,2019.
J-8. Xiaoxi Ding, Qingbo He. Energy fluctuatedmultiscale feature learning with deep convnet for intelligent spindle bearingfault diagnosis [J].IEEE Transactions on Instrumentation and Measurement, vol.66, pp: 1926-1935, 2017. (ESI 1%高被引论文)
J-7.Xiaoxi Ding, Qingbo He. Timefrequency manifold sparse reconstruction: A novelmethod for bearing fault feature extraction [J].Mechanical Systems and SignalProcessing, vol. 80, pp: 392-413, 2016.
J-6.Qingbo He,Xiaoxi Ding. Sparse representation based on local timefrequencytemplate matching for bearing transient fault feature extraction [J].Journalof Sound and Vibration, vol. 370, pp: 424-443, 2016.
J-5.Qingbo He, Haiyue Song,Xiaoxi Ding. Sparse signal reconstruction based ontime-frequency manifold for rolling element bearing fault signature enhancement[J].IEEE Transactions on Instrumentation and Measurement, 2016, 65(2):482-491.
J-4.Xiaoxi Ding, Qingbo He, Nianwu Luo. A fusion feature and its improvement basedon locality preserving projections for rolling element bearing faultclassification [J].Journal of Sound and Vibration, vol. 335, pp: 367-383, 2015.
J-3.Qingbo He,Xiaoxi Ding, Pan Yuanyuan. Machine fault classification based onlocal discriminant bases and locality preserving projections [J]. MathematicalProblems in Engineering,2014.
J-2. 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.
J-1.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.


会议论文
C-6.Xiaoxi Ding, Qingbo He*. Shorttime smoothness spectrum: A novel demodulationmethod for bearing fault diagnosis. 2016 International Symposium on Flexible Automation,August 1-3, 2016 in Cleveland, Ohio. USA.(Best Paper Award (in Application))
C-5.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(优秀论文奖).
C-4.Quanchang Li,Xiaoxi Ding*,Wenbin Huang and Yimin Shao, Rotating machineryfault diagnosis with weighted variational manifold learning, World Congress onCondition Monitroing, Marina Bay Sands, Signapore on 2-5, December, 2019.
C-3.Xiaoxi Ding, Yimin Shao*, Qingbo He and Diego Galar. A subspace clusteringchart using a reference model for featureless bearing performance degradationassessment. 2018 Society for Machinery Failure Prevention Technology (MFPT),July 17-20, 2018 in Virginia Beach, VA. USA.
C-2.Xiaoxi Ding, Qingbo He*.TwoClass Model Based on Nonlinear Manifold Learning forBearing Health Monitoring. 2016 IEEE International Instrumentation andMeasurement Technology Conference, May 23-26, 2016 in Taiwan.
C-1.Qingbo He*,Xiaoxi Ding. Feature mining with convolutional neural network forbearing fault diagnosis. 29th International Congress on Condition Monitoringand Diagnostic Engineering Management, Xi’an, China on 20-22 August 2016.

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.



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