姓        名： 丁晓喜

出生年月： 1989/12

学历学位： 工学博士

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

     主要面向机械装备多信息智能感知及诊断预警系统、边缘计算、智能识别以及软件数字化等学术前沿和工程需求，瞄准高端装备智能化核心关键技术攻关。

    主持负责了国家青年基金、国家重点子课题、中国博士后基金、中国舰船XX项目、核动力XX项目、重庆齿轮箱XX项目、Intel英特尔产品（成都）XX项目、徐工重工XX项目、成都天马XX项目等30余项。作为主研人员参加了国家重点项目、国家重点研发计划、国家科技重点专项等多项国家基础科研项目。作为骨干研究人员，解决了复杂工况下设备故障智能融合诊断关键理论，获安徽省自然科学二等奖（第5），服务于大功率高动载湿式离合传动系统关键技术，获中国机械工业科学技术发明一等奖（第8）、聚焦大型立磨健康状态评估预警关键技术，获中国机械工业科学技术进步二等奖（第4）。

     近年来，公开发表论文累计发表EI/SCI论文超过60余篇，SCI论文(一作/通讯)50+篇（中科院一区顶刊18篇），授权国家发明专利15项。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%，引用超400次）。受邀在《2018全国设备监测诊断与维护学术会议》\《TEPEN 2021》\《2022全国设备监测诊断与维护学术会议》\《WYSS2022高端装备系统动力学与智能诊断维护学术研讨会》\2023年Intel国际峰会论坛\2023年因特尔英才计划论文上做学术报告，担任IEEE TII、MSSP、EAAI、IEEE TIM、JSV、Meas.等国际期刊审稿人。

• 获奖情况：

• 13.2023,  2021~2023年连续三年进入美国斯坦福大学发布全球前2%顶尖科学家榜单（Top 2% of Scientists on Stanford List）

• 12.2023, 中国机械工业科学科技进步二等奖（第4完成人）；

• 11.2022，Best Paper Award, The Efficiency and Performance Engineering Network 2022, TEPEN 2022

• 10.2021, 中国机械工业科学技术发明一等奖（第8完成人）；

• 9. 2021，Best Paper Award, The Efficiency and Performance Engineering Network 2021, TEPEN 2021

8. 2020, 安徽省自然科学二等奖（第5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

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

A. SCI/EI期刊论文(一作或通讯) 50+篇，其中中科院一区顶刊18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%）

• 2024
  

[53] L. Ge, Y. Wang, X. Ding*, W. Huang, Y. Chen,L. Wang, “A multi-scale feature weighted transfer network for unlabeled rotating machinery fault diagnosis, IEEE Sensors Journal, 2024.

[52] H. Wang, X. Ding*, Z. Huang, W. Cheng, W. Yu, W. Huang, “Multi-stage exponential model based on subspace clustering distribution for bearing remaining useful life prediction”, Measurement Science and Technology, 2024.

[51] J. Xiao, X. Ding*, H. Pan, Y. Zhang, Q. He, Y. Shao, “Dual-band filtering and enhanced directional via tunable acoustic metamaterial antennas”, Smart Materials and Structures, 2024.

[50] Q. Wu, X. Ding*, W. Cheng, Y. Fan, “IoT-based Adaptive Multiplication-Convolution Sparse Denoising for Equipment Edge Condition Evaluation”, IEEE Internet of Things Journal, 2024.（中科院一区顶刊，IF=11.1）.

[49] R. Liu, X. Ding*, Y. Shao, “Prior-knowledge-guided mode filtering network for interpretable equipment intelligent diagnosis under varying speed conditions”, Advanced Engineering Informatics, 2024.（中科院一区顶刊，IF=8.8）

[48] J. Tang, J. Xiao, W. Chen, X. Li, C. Wei, X. Ding*, W. Huang*, “A prior knowledge-enhanced self-supervised learning framework using time-frequency invariance for machinery intelligent fault diagnosis with small samples”, Engineering Applications of Artificial Intelligence, 2024.（中科院一区顶刊，IF=8）

[47] Y. Xu, H. Xiang, X. Li, H. Yu, S. Chen, W. Huang*, X. Ding*, “Lamb waves-based PCF-DMA: An anti-interference synchronous independent data transmission scheme for multiple cross-space users”, Mechanical Systems and Signal Processing, 2024.（中科院一区顶刊，IF=8.4）

[46] R. Liu, X. Ding*, Y. Shao, W. Huang, “An interpretable multiplication-convolution residual network for equipment fault diagnosis via time-frequency filtering”, Advanced Engineering Informatics, 2024.（中科院一区顶刊，IF=8.8）

[45] R. Liu, X. Ding*, S. Wu, Q. Wu, Y. Shao, “Signal processing collaborated with deep learning: An interpretable FIRNet for industrial intelligent diagnosis”, Mechanical Systems and Signal Processing, 2024.（中科院一区顶刊，IF=8.4）

[44] Y. Pu, J. Tang, X. Li, C. Wei, W. Huang*, X. Ding*, “Single-Domain Incremental Generation Network for Machinery Intelligent Fault Diagnosis under Unknown Working Speeds”,  Advanced Engineering Informatics, 2024.（中科院一区顶刊，IF=8.8）

[43] Z. Li, X. Ding*, Z. Song, L. Wang, B. Qin*, W. Huang, “Digital twin-assisted dual transfer: a novel information-model adaptation method for rolling bearing fault diagnosis”,  Information Fusion, 2024.（中科院一区顶刊，IF=18.6）

[42] J. Tang, X. Ding*, C. Wei, J. Xiao R. Liu, M. Wang and W. Huang*, “HmmSeNet: A Novel Single Domain Generalization Equipment Fault Diagnosis under Unknown Working Speed Using Histogram Matching Mixup”,  IEEE Transactions on Industrial Informatics, 2024.（中科院一区顶刊，IF=12.3）

[41] R. Liu, X. Ding*, Q. Wu, Q. He and Y. Shao, “An Interpretable Multiplication-Convolution Network for Equipment Intelligent Edge Diagnosis”, IEEE Transactions on Systems, Man, and Cybernetics: Systems , 2024.（中科院一区顶刊，IF=9.5）

• 2023
  

[40] 吴启航，丁晓喜*，何清波，黄文彬, “齿轮箱故障边缘智能诊断方法及应用研究”,  仪器仪表学报, 2023.（国内重要期刊）

[39] X. Ding*, S. Wu, Y. Li, Y. Zhang, Q. He* and Y. Shao, “Parametric Doppler Correction for Wayside Array Acoustic Signal via Short-Time Reconstruction”,  Mechanical Systems and Signal Processing, 2023.（中科院一区顶刊，IF=8.4）

[38] Q. Wu, X. Ding*, Q. Zhang, R. Liu, S. Wu and Q. He, “An Intelligent Edge Diagnosis System Based on Multiplication-Convolution Sparse Network”, IEEE Sensors Journal, 2023.

[37] H. Pan, X. Ding*, H. Qiao, W. Huang, J. Xiao and Y. Zhang, “Metamaterial-based acoustic enhanced sensing for gearbox weak fault feature diagnosis”, Smart Materials and Structures, 2023.

[36] R. Wang, X. Ding*, D. He,Q. Li, X. Li, J. Tang and W. Huang, “Shift-invariant sparse filtering for bearing weak fault signal denoising”, IEEE Sensors Journal, 2023

[35] R. Liu, X. Ding*, S. Liu, Q. Wu and Y. Shao, “Sinc-based Multiplication-Convolution Network for Small-sample Fault Diagnosis and Edge Application”, IEEE Transactions on Instrumentation & Measurement, 2023.

[34] Y. Wang, Z. Zhang, Y. Du, P. Chen, X. Ding* and W. Yu, “A Differential Enhanced ConvNet for Rotating Machinery Diagnosis under Strong Noise”, IEEE Sensors Journal, 2023.

[33] R. Liu, X. Ding*, Y. Zhang, M. Zhang, and Y. Shao, "Variable-scale evolutionary adaptive mode denoising in the application of gearbox early fault diagnosis," Mechanical Systems and Signal Processing, Article vol. 185, 2023, Art no. 109773.（中科院一区顶刊，IF=8.4）

[32] M Xu, Y Han, X. Ding*, H Shao* and Y Shao, “Decision Self-regulating Network for Imbalanced Working Conditions Identification in the Application of Gearbox Intelligent Fault Diagnosis,”IEEE Transactions on Instrumentation and Measurement, pp. 1-1, 2023.

[31] Q. Wu, X. Ding*, L. Zhao, R. Liu, Q. He, and Y. Shao, "An Interpretable Multiplication-Convolution Sparse Network for Equipment Intelligent Diagnosis in Anti-aliasing and Regularization Constraint," IEEE Transactions on Instrumentation and Measurement, pp. 1-1, 2023.doi: 10.1109/TIM.2023.3269122.

[30] J. Tang, Q. Wu, X. Li, C. Wei, X. Ding*, W. Huang*, and Y. Shao, "An Efficient Sequential Embedding ConvNet for Rotating Machinery Intelligent Fault Diagnosis," IEEE Transactions on Instrumentation and Measurement, Article vol. 72, 2023, Art no. 2510713.

[29] H. Yi, X. Ding*, Q. Li, H. Wang, J. Tang, R. Liu, W. Huang, "Dual-kernel driven convolutional sparse learning for bearing transient feature enhancement," Measurement: Journal of the International Measurement Confederation, Article vol. 216, 2023, Art no. 112643.

[28] Y. Xu, Q. Li, W. Lin, Q. Wu, W. Huang*, and X. Ding*, "Lamb Waves-Based Sparse Distributed Penetrating Communication via Phase-Position Modulation for Enclosed Metal Structures," IEEE Transactions on Industrial Informatics, Article pp. 1-12, 2023.（中科院一区顶刊，IF=12.3）

[27] Y. Wang, L. Ge, C. Xue, X. Li, X. Meng, and X. Ding*, "Multiple local domains transfer network for equipment fault intelligent identification," Engineering Applications of Artificial Intelligence, Article vol. 120, 2023, Art no. 105791.（中科院一区顶刊，IF=8）

• 2022

[26] X. Ding, Y. Li, J. Xiao, Q. He, X. Yang, and Y. Shao, "Parametric Doppler correction analysis for wayside acoustic bearing fault diagnosis," Mechanical Systems and Signal Processing, Article vol. 166, 2022, Art no. 108375.（中科院一区顶刊，IF=8.4）

[25] Y. Wang, X. Ding*, R. Liu, and Y. Shao, "ConditionSenseNet: A Deep Interpolatory ConvNet for Bearing Intelligent Diagnosis under Variational Working Conditions," IEEE Transactions on Industrial Informatics, Article vol. 18, no. 10, pp. 6558-6568, 2022.（中科院一区顶刊，IF=12.3）

[24] X. Yang, L. Wang*, K. Ding, X. Ding*, “Vibration AM-FM sidebands mechanism of planetary gearbox with tooth root cracked planet gear”, Engineering Failure Analysis, Article vol. 137, 2022.

[23] J. Tang, G. Zheng, C. Wei, W. Huang*, and X. Ding*, "Signal-Transformer: A Robust and Interpretable Method for Rotating Machinery Intelligent Fault Diagnosis under Variable Operating Conditions," IEEE Transactions on Instrumentation and Measurement, Article vol. 71, 2022, Art no. 3511911.

[22] X. Yang, L. Wang*, K. Ding, and X. Ding*, "Vibration AM-FM sidebands mechanism of planetary gearbox with tooth root cracked planet gear," Engineering Failure Analysis, Article vol. 137, 2022, Art no. 106353.

[21] L. Zhang, J. Liu, S. Su, T. Lu, C. Xue, Y. Wang, X. Ding*, and Y. Shao, "Rotating Machinery Fault Identification via Adaptive Convolutional Neural Network," Journal of Sensors, Article vol. 2022, Art no. 6733676.

• 2021

[20] Q. Li, X. Ding*, Q. He, W. Huang, and Y. Shao, "Manifold Sensing-Based Convolution Sparse Self-Learning for Defective Bearing Morphological Feature Extraction," IEEE Transactions on Industrial Informatics, Article vol. 17, no. 5, pp. 3069-3078, 2021, Art no. 9220823.（中科院一区顶刊，IF=12.3）

[19] X. Ding, L. Lin, D. He, L. Wang, W. Huang, and Y. Shao*, "A Weight Multinet Architecture for Bearing Fault Classification under Complex Speed Conditions," IEEE Transactions on Instrumentation and Measurement, Article vol. 70, 2021, Art no. 9205598.

[18] L. Dai, Q. Li, Y. Chen, X. Ding*, W. Huang, and Y. Shao, "Complex scale feature extraction for gearbox via adaptive multi-mode manifold learning," Measurement: Journal of the International Measurement Confederation, Article vol. 174, 2021, Art no. 108688.

[17] Y. Wang, L. Zeng, L. Wang, Y. Shao, Y. Zhang*, and X. Ding*, "An Efficient Incremental Learning of Bearing Fault Imbalanced Data Set via Filter StyleGAN," IEEE Transactions on Instrumentation and Measurement, Article vol. 70, 2021, Art no. 9508205.

[16] L. Zhang, Y. Li, L. Dong, X. Yang, X. Ding*, Q. Zeng, L. Wang, and Y. Shao, “Gearbox Fault Diagnosis Using Multiscale Sparse Frequency-Frequency Distributions,” IEEE Access, 9 (2021) 113089-113099.

• 2020

[15] X. Ding, W. Li, J. Xiong, Y. Shen, and W. Huang*, "A flexible laser ultrasound transducer for Lamb wave-based structural health monitoring," Smart Materials and Structures, Article vol. 29, no. 7, 2020, Art no. 075006.

[14] X. Ding*, L. Wang, W. Huang, Q. He, and Y. Shao, "Feature Clustering Analysis Using Reference Model towards Rolling Bearing Performance Degradation Assessment," Shock and Vibration, Article vol. 2020, 2020, Art no. 6306087.

[13] Q. Li, X. Ding*, T. Wang, M. Zhang, W. Huang, and Y. Shao, "Time-frequency synthesis analysis for complex signal of rotating machinery via variational mode manifold reinforcement learning," Proceedings of the Institution of Mechanical Engineers, Part C: Journal of Mechanical Engineering Science, Article vol. 234, no. 7, pp. 1438-1455, 2020.

[12] Z. Zhang, S. Zhong, W. Huang, and X. Ding*, "A wireless demodulation method for acoustic emission sensing," IEEE Sensors Journal, Article vol. 20, no. 21, pp. 12671-12678, 2020, Art no. 9120018.

• 2019

[11] X. Ding*, Q. He, Y. Shao, and W. Huang, "Transient Feature Extraction Based on Time-Frequency Manifold Image Synthesis for Machinery Fault Diagnosis," IEEE Transactions on Instrumentation and Measurement, Article vol. 68, no. 11, pp. 4242-4252, 2019, Art no. 8621064.

[10] X. Ding, Q. Li, L. Lin, Q. He, and Y. Shao*, "Fast time-frequency manifold learning and its reconstruction for transient feature extraction in rotating machinery fault diagnosis," Measurement: Journal of the International Measurement Confederation, Article vol. 141, pp. 380-395, 2019.

[9] Q. Li, X. Ding*, W. Huang, Q. He, and Y. Shao, "Transient feature self-enhancement via shift-invariant manifold sparse learning for rolling bearing health diagnosis," Measurement: Journal of the International Measurement Confederation, Article vol. 148, 2019, Art no. 106957.

[8] 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.

[7] D. Zhang, X. Ding*, W. Huang, and Q. He, "Transient Signal Analysis Using Parallel Time-Frequency Manifold Filtering for Bearing Health Diagnosis," IEEE Access, Article vol. 7, pp. 175277-175289, 2019, Art no. 8917988.

• 2017-Before

[6] X. Ding and Q. He, "Energy-Fluctuated Multiscale Feature Learning with Deep ConvNet for Intelligent Spindle Bearing Fault Diagnosis," IEEE Transactions on Instrumentation and Measurement, Article vol. 66, no. 8, pp. 1926-1935, 2017, Art no. 7880628.（高倍引论文）

[5] Q. He and X. Ding, "Sparse representation based on local time-frequency template matching for bearing transient fault feature extraction," Journal of Sound and Vibration, Article vol. 370, pp. 424-443, 2016.(导师一作)

[4] X. Ding and Q. He, "Time–frequency manifold sparse reconstruction: A novel method for bearing fault feature extraction," Mechanical Systems and Signal Processing, Article vol. 80, pp. 392-413, 2016.（中科院一区顶刊，IF=8.4）

[3] X. Ding, Q. He, and N. Luo, "A fusion feature and its improvement based on locality preserving projections for rolling element bearing fault classification," Journal of Sound and Vibration, Article vol. 335, pp. 367-383, 2015.

[2] Q. He, X. Ding, and Y. Pan, "Machine fault classification based on local discriminant bases and locality preserving projections," Mathematical Problems in Engineering, Article vol. 2014, 2014, Art no. 923424.(导师一作)

[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.

• B. EI论文(一作或通讯) 10篇，其中最佳论文奖3次

[11] Yulan Li, Hongrui Yi, Hao Wang, Xiaoxi Ding*, Jiawei Xiao, Huafei Pan andYimin Shao, Parameterized Doppler Adaptive Correction for Wayside Parameterized, The Efficiency and Performance Engineering Network 2022(TEPEN 2022) (Best Paper Award)

[10] R. Liu, X. Ding*, Q. Wu, H. Xiang, H. Tan, and Y. Shao, "Sinc-based Multiplication-Convolution Network for Equipment Intelligent Edge Diagnosis under Small Samples," in 2022 International Conference on Sensing, Measurement and Data Analytics in the Era of Artificial Intelligence, ICSMD 2022 - Proceedings, 2022.

[9] H. Tan, X. Ding*, M. Hu, L. Zhao, and Y. Shao, "Optimal measurement points evaluation for friction plate via a comprehensive analysis of correlation and clustering," in 2022 International Conference on Sensing, Measurement and Data Analytics in the Era of Artificial Intelligence, ICSMD 2022 - Proceedings, 2022.

[8] Q. Wu, X. Ding*, Q. He, H. Xiang, H. Tan, and Y. Shao, "An Efficient Intelligent Edge Diagnosis System Based on WDCNN in the Application of Equipment Fault Classification," in 2022 International Conference on Sensing, Measurement and Data Analytics in the Era of Artificial Intelligence, ICSMD 2022 - Proceedings, 2022.

[7] Z. Chen, S. Liu, H. Yi, X. Ding*, X. Li, S. Wu, M. Hu, and Y. Shao, "Prediction Reliability Assessment Based on Mahalanobis Distance and GRU in the Application of Bearing RUL Analysis," Mechanisms and Machine Science, vol. 129 MMS, pp. 554-567, 2023.

[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))

[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).

[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.

[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.

[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.

[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.(导师一作)

• 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.