Background of Education and Work Experience

• 2020/04-, Lecturer, Mechanical Design and Theory, Chongqing University.

• 2017/08-2020/04, Postdoc., Mechanical Design and Theory, Chongqing University.

• 2012/09-2017/07, Ph.D., University of Science and Technology of China

• 2008/08-2012/07, B.E., University of Science and Technology of China

Research Field

• Vibration & acoustics signal processing

• Machine health monitoring and intelligent fault diagnosis

• Signal adaptive processing and deep learning

• Embedded acquisition and edge intelligent computing

• Desktop software development and intelligent monitoring system

Multi-source information intelligent detection technology and system research and development for rail transit, wind power, machine tools and other fields

Research and Honors

Research Projects

• 2019/01-2021/12, National Natural Science Foundation of China Youth Science Foundation of China, (51805051)

• 2020/01-2022/12, National Key Research and Development Program of Ministry of Science and Technology- Subproject (2019YFB2004302)

• 2020/06-2022/06, Chongqing technical innovation and application development special project subtopic(cstc2020jscx-msxmX0194)

• 2021/01-2023/12, Chongqing outbound Chongqing postdoctoral research project (2020LY10)

• 2020/01-2021/12, Operating expenses for basic scientific research in central colleges and universities, (2020CDJGFCD002)

• 2020/10-2021/10, “ Research on vibration monitoring system based on wireless ad hoc networks in the application of machine manufacturing”, Intel products (Chengdu) Co., Ltd. (H20201273)

• 2019/07-2022/06, Basic Science and Frontier Technology Research Special Project of Chongqing Science and Technology Planning Project-General Project, (cstc2019jcyj-msxmX0346)

• 2018/07-2020/06, Special Grant for Chongqing postdoctoral researcher research project (XmT2018038)

• 2018/01-2019/12, China Postdoctoral Program

Honors and Awards

• 2021, Anhui Natural Science Award, Second Prize (the 5th finisher)

• 2018, Outstanding Paper Award, Equipment Monitoring, Diagnosis and Maintenance Academic Conference, 2018

• 2017, President Award of Chinese Academy of Sciences (Excellence Award), Chinese Academy of Sciences, 2017

• 2017, Outstanding graduate of University of Science and Technology of China, 2017

• 2016, Best Paper Award (in Application), International Symposium on Flexible Automation, ISFA 2016

• 2016, National Scholarship for Graduate Studies (Doctor)

• 2014, National Scholarship for Graduate Studies (Master)

• 2007, National Math Competition, First Prize

Selected Publications

Journal Papers

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*, Wenbin Huang, Yimin Shao, Manifold Sensing-based Convolution Sparse Self-Learning for Defective Bearing Morphological Feature Extraction [J]. IEEE Transactions on Industrial Informatics, 2020

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 laserultra-sound transducer for Lamb wave based structural health monitoring [J], Smart Materials and Structures, 2020.

J-15ZhiboZhang, Siping Zhong, Wenbin Huang, Xiaoxi Ding*, A Wireless Demodulation Method 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 Image Synthesis for Machinery Fault Diagnosis [J]. IEEE Transactions on Instrumentation 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 via variational mode manifold reinforcement learning [J], Proceedings of the Institution of Mechanical Engineers Part C-Journal of Mechanical Engineering Science, 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. Transient feature self-enhancement via shift-invariant manifold sparse learning for rolling bearing health diagnosis. [J] Measurement 148 (2019): 106957.

J-10. Xiaoxi Ding*, Liming Wang, WenbinHuang. Feature Clustering Analysis Using Reference Model towards Machin ePerformance Degradation Assessment [J]. Shock and Vibration, 2020.

J-9. 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.

J-8. 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. (ESI 1%)

J-7. Xiaoxi Ding, Qingbo He. Timefrequency manifold sparse reconstruction: A novel method for bearing fault feature extraction [J]. Mechanical Systems and Signal Processing, vol. 80, pp: 392-413, 2016.

J-6. 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.

J-5. Qingbo He, Haiyue Song, Xiaoxi Ding. Sparse signal reconstruction based on time-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 based on locality preserving projections for rolling element bearing fault classification [J]. Journal of Sound and Vibration, vol. 335, pp: 367-383, 2015.

J-3. Qingbo He, Xiaoxi Ding, Pan Yuanyuan. Machine fault classification based on local discriminant bases and locality preserving projections [J]. Mathematical Problems in Engineering,2014.

J-2. LI Quanchang, He Qingbo, SHAO Yimin, DING Xiaoxi*, Fault Signal Enhancement of Rotating Machinery via Shift-Invariant Time-Frequency Manifold Self-learning [J], Journal of Vibration Engineering, 2019.

J-1. Ding Xiaoxi*, He Qingbo*. Machine fault diagnosis based on WPD and LPP [J], Journal of Vibration and Shock, vol. 33, no. 3, pp. 55–59, 2014.

Conference Papers

C-6. 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. (Best Paper Award (in Application))

C-5. Ding Xiaoxi*, Li Quanchang, Huang Wenbin, He Qingbo, Shao Yimin. Shift-Invariant Time-Frequency Manifold Learning towards Transient Feature Extraction in Rotating Machinery，2018 Equipment Monitoring, Diagnosis and Maintenance Academic Conference (Outstanding Paper Award).

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 clustering chart using a reference model for featureless bearing performance degradation assessment. 2018 Society for Machinery Failure Prevention Technology (MFPT),July 17-20, 2018 in Virginia Beach, VA. USA.

C-2.Xiaoxi Ding, Qingbo He*.Two Class Model Based on Nonlinear Manifold Learning forBearing Health Monitoring. 2016 IEEE International Instrumentation and Measurement 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 Monitoring and Diagnostic Engineering Management, Xi’an, China on 20-22 August 2016.

Book Chapters

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.