姓        名：丁晓喜 

电子邮箱：dxxu@cqu.edu.cn

     主要面向机械装备多信息智能监测预警系统、边缘计算、声学分析以及软件数字化平台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XX项目、重庆齿轮箱项目、Intel英特尔产品（成都）项目、徐工重工项目、成都天马项目等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SCI期刊论文(一作/通讯)50+篇（中科院一区顶刊20篇、JCR Q1论文44篇），最佳论文奖5篇，授权国家发明专利15项，ESI 1%高被引论文1篇（单篇引用超500次）。已连续3年入选全球前2%顶尖科学家年度科学影响力排行榜。包括中科院一区TOP期刊1篇Information Fusion(IF=18.6)、4篇IEEE Transactions on Industrial Informatics (IF=12)、7篇Mechanical Systems and Signal Processing (IF=8.5)、3篇Advanced Engineering Informatics（IF=8.8）等。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\2024中国振动工程学会青年学者论坛等作特邀报告，担任IEEE TII、ASOC、IJMS、MSSP、AEI、EAAI、IEEE TIM、JSV、Meas.等国际期刊审稿人。 

2024

[21] J. Xiao, X. Ding*, W. Huang, Q. He, Y. Shao, “Gear fault detection via directional enhancement of phononic crystal resonators”, Mechanical Systems and Signal Processing, 2024.（中科院一区顶刊，IF=8.4）

[20] J. Xiao, X. Ding*, Y. Wang, W. Huang, Q. He, Y. Shao, “Gear fault detection via directional enhancement of phononic crystal resonators”, International Journal of Mechanical Sciences, 2024.（中科院一区顶刊，IF=7.3）

[19] 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）. 

[18] 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）

[17] 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） 

[16] 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）

[153] 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）

[14] 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） 

[13] 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） 

[12] 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） 

[11] 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） 

[10] 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）

[9] 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）

2023

[8] 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）

[7] 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.（中科院一区顶刊，IF=8）

[6] 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） 

2022 

[5] 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.（中科院一区顶刊，IF=8.4） 

[4] 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）

2021 

[3] 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.（中科院一区顶刊，IF=12.3）

2017-Before

[2] 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（高被引论文，单篇引用超过500） 

[1] 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）