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: 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


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个人简历

ResearchGate: https://www.researchgate.net/profile/Xiaoxi_Ding

主要研究方向

机械设备智能诊断与健康预警平台

大数据挖掘技术与深度智能分析方法

基于边缘计算的数模驱动智能诊断技术与方法

面向轨道交通、风电、机床等领域的多源信息智能检测技术与系统研发

  

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

  • 研究经

 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, (51805051), PI
8. 2020/01-2022/12, National Key Research and Development Program of Ministry of Science and TechnologySubproject (2019YFB2004302), 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. (H20201273), 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 (XmT2018038), 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-16 Xiaoxi 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: 0954406219897688.

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.