文龙,必赢766net手机版(武汉)机械与电子信息院,教授、博导,中共党员,汉族
联系方式:wenlong@cug.edu.cn,办公室:教二楼343
研究方向:
主要从事工业人工智能、深度学习、人工智能装备方面的研究。在IEEE Transactions等期刊上发表SCI论文50余篇,入选ESI热点论文4篇、ESI高被引论文5篇,Google学术引用超过5550余次,最高单篇(一作)引用2030余次,入选斯坦福大学2021/2022/2023/2024年度全球前2%顶尖科学家榜单。主持国家自然科学基金、中国博士后科学基金、装备预研基金等项目10余项,参与国家重点研发计划、湖北省科技重大专项等项目30余项。出版教材《深度学习》1部,获2022年教育部自然科学奖一等奖(排4)、2023年中国仿真学会创新技术一等奖(排3)、2024年湖北省科学技术进步奖一等奖(排4),学术专著1部(排3),授权发明专利9项。
主要学术兼职:
中国机械工程学会工业大数据与智能系统分会委员
《IET Collaborative Intelligent Manufacturing》副主编(ESCI检索)
《Sound & Vibration》编委(ESCI检索)
《Journal of Dynamics, Monitoring and Diagnostics》《机械强度》青年编委
《Entropy》《Sensors》《CMES-Computer Modeling in Engineering & Sciences》《Measure and Control》等SCI期刊上担任相关方向的客座编辑。
IEEE Transactions on Artificial Intelligence、IEEE Transactions on Neural Networks and Learning Systems、IEEE Transactions on Industrial Electronics、IEEE Transactions on Industrial Informatics、IEEE Transactions on Reliability、Engineering Applications of Artificial Intelligence、Journal of Intelligent Manufacturing等期刊审稿人
主要经历:
2022.07-至今 必赢766net手机版(武汉),教授
2019.12-至今 必赢766net手机版(武汉),博导
2019.07-2022.06 必赢766net手机版(武汉),特任教授
2016.06-2019.07 华中科技大学,博士后
2015.01-2016.04 中国船舶重工集团第七二三研究所
2010.09-2014.12 华中科技大学,机械科学与工程学院,获工学博士学位
2006.09-2010.06 华中科技大学,机械科学与工程学院,获工学学士学位
主要研究方向:
招生信息:
本课题组欢迎热爱科学研究、对工业人工智能感兴趣的同学加盟,具有良好编程能力,实验动手能力,机械电子工程、工业工程、自动化、计算机等相关理工科背景的本科生和研究生加入课题组。招收博士生(直博生、硕博连读、应届硕士等)和硕士生。
科研项目:
[1] 广东省自然科学基金面上项目,深海机器人推进器健康基线时变机理及其损伤评估模型研究,2024.01-2026.12,主持
[2] 深圳市自然科学基金面上项目,复杂强干扰下深海AUV早期弱故障识别及其劣化评估模型研究,2023.11-2026.11,主持
[3] 国家重点研发计划,面向机械加工的智能工厂建模仿真与优化工具软件,2023/12-2026/11,参与
[4] 武汉市科技计划项目,排水管道超高压清洗与管中破碎技术与装备,2023/06-2025/06,参与
[5] 国家重点实验室开放基金,高速传动齿轮健康状态监测与智能诊断,2023/01-2024/12,主持
[6] 国家重点研发计划,2019YFB1704603,基于数字孪生的电子产品生产调度与物料传输协同优化及决策技术,2019/12-2022/11,参与
[7] 国家自然科学基金,51805192,基于深度学习的智能车间机器故障状态预测方法研究,2019/01~2021/12,主持
[8] 中国博士后科学基金,2017M622414,基于深度迁移学习的在轨航天设备故障预测方法研究,主持
代表性论著与奖励:
[1] 教育部自然科学奖一等奖(排4),2022年。
[2] 湖北省科学技术进步奖一等奖(排4,已公示),2024年。
[3] 中国仿真学会科学技术奖创新技术一等奖(排3),2023年。
[4] 文龙,李新宇,深度学习,清华大学出版社,2022。
[5] 高亮,李新宇,文龙,《排序与调度:工艺规划与车间调度的智能算法》,清华大学出版社(书号:978-7-302-51964-5,十三五国家重点图书,排序与调度丛书), 2019.
[1] L Wen, XY Li, L Gao, YY Zhang, “A New Convolutional Neural Network based Data-Driven Fault Diagnosis Method,” IEEE Transactions on Industrial Electronics, 65(7): 5990-5998, 2018. (ESI热点论文,ESI高被引论文,Google学术引用2037余次)
[2] L Wen, L Gao, XY Li, “A New Deep Transfer Learning Based on Sparse Auto-Encoder for Fault Diagnosis,” IEEE Transactions on Systems, Man, and Cybernetics: Systems, 49(1): 136-144, 2019. (ESI热点论文,ESI高被引论文,Google学术引用1035余次)
[3] L Wen, XY Li, L Gao, “A Transfer Convolutional Neural Network for Fault Diagnosis Based on ResNet-50”, Neural Computing and Applications. 32(10), 6111-6124, 2020. (ESI热点论文,ESI高被引论文,Google学术引用637余次)
[4] L Wen, Y Wang and X Li, “A New Cycle-consistent Adversarial Networks with Attention Mechanism for Surface Defect Classification with Small Samples,” IEEE Transactions on Industrial Informatics, 18(12): 8988-8998, 2022. (ESI热点论文)
[5] L Wen, XY Li, L Gao, “A New Two-level Hierarchical Diagnosis Network based on Convolutional Neural Network,” IEEE Transactions on Instrumentation and Measurement, 69(2): 330-338, 2020. (ESI高被引论文,Google学术引用122余次)
[6] L Wen, X Xie, XY Li, L Gao, “A New Ensemble Convolutional Neural Network with Diversity Regularization for Fault Diagnosis,” Journal of Manufacturing Systems. 62: 964-971, 2020. (ESI高被引论文)
[7] L Wen*, YX Ye(叶雨星,硕士生), Lei Zuo(左磊,硕士生), “GAF-Net: A New Automated Segmentation Method Based on Multiscale Feature Fusion and Feedback module”, Pattern Recognition Letters, 187, 86-92, 2025.
[8] ZW Zhang(张智伟,硕士生), CB Wei(魏成彬,硕士生), SHW Xie(谢绍旺,硕士生), WM Zhang, L Wen*, “A New Multi-Sensor Feature Fusion KAN Network for Autonomous Underwater Vehicle Fault Diagnosis”, IEEE Transactions on Instrumentation and Measurement, 2024. (Accept)
[9] WT Hu(胡文韬,博士生), CY Tian(田成煜,博士生), L Wen*, HF Ding, “TD-NeRF: Transfer Learning and Diffusion Regulation Based NeRF for Scene Perception”, IEEE Transactions on Instrumentation and Measurement, 2024. (Accept)
[10] L Wen, Y Zhang (张杨,硕士生), WT Hu (胡文韬,博士生), XY Li, “The Survey of Industrial Anomaly Detection for Industry 5.0”, International Journal of Computer Integrated Manufacturing, 2024.
[11] SH Fan(范诗航,硕士生), SJ Guo(郭圣剑,硕士生), JH He(贺江波,硕士生), JN Wei, L Wen*, “A new Feature Pyramid Network with Bidirectional Jump Connection Network for Small Defect Detection on Solar PV Wafer”, IEEE Sensors Journal, 2024
[12] L Wen, SQ Su(苏少权,硕士生), XY Li, WP Ding, K Feng*, “GRU-AE-wiener: A generative adversarial network assisted hybrid gated recurrent unit with Wiener model for bearing remaining useful life estimation”, Mechanical Systems and Signal Processing, 220: 111663, 2024. (中科院一区)
[13] QW Wu(吴强威,硕士生), H Li(李辉,硕士生), CY Tian(田辰煜,硕士生), L Wen*, XY Li, “AEKD: Unsupervised Auto-Encoder Knowledge Distillation for Industrial Anomaly Detection”, Journal of Manufacturing Systems, 2024.
[14] L Zuo(左磊,硕士生), SQ Su(苏少权,硕士生), SH Fan(范诗航,硕士生), H Li(李辉,硕士生), L Wen*, XY Li, KG Xiong, “A New Dual-Branch Network with Global Information for the Surface Defect Detection on Solar PV Wafer,” IEEE Sensors Journal, 2024.
[15] L Wen, G Yang(杨广,硕士生), LX Hu(胡隆鑫,硕士生), CS Yang, K Feng, “A New Unsupervised Health Index Estimation Method for Bearings Early Fault Detection Based on Gaussian Mixture Model,” Engineering Applications of Artificial Intelligence, 128: 107562, 2024.
[16] L Zuo(左磊,硕士生), HY Xiao(肖洪勇,硕士生), L Wen*, L Gao, “A Pixel-level Segmentation Convolutional Neural Network Based on Global and Local Feature Fusion for Surface Defect Detection,” IEEE Transactions on Instrumentation and Measurement, 72, 1-10, Art no. 5029510, 2023.
[17] 吴强威(硕士生), 文龙. 基于联合对抗训练的深度学习表面缺陷检测方法. 机械工程学报, 2023, 59(12): 173-182.
[18] L Wen, SQ Su(苏少权,硕士生), B Wang(王斌,硕士生), J Ge, L Gao, K Lin, “A New Multi-sensor Fusion with Hybrid Convolutional Neural Network with Wiener Model for Remaining Useful Life Estimation”, Engineering Applications of Artificial Intelligence, 2023.
[19] B Wang (王斌,硕士生), L Wen*, XY Li, L. Gao, “Adaptive Class Center Generalization Network: A Sparse Domain-Regressive Framework for Bearing Fault Diagnosis Under Unknown Working Conditions”, IEEE Transactions on Instrumentation and Measurement, 2023.
[20] L Wen, Y Zhang (张杨,硕士生), L Gao, Xinyu Li, M. Li, “A New Multi-Scale Multi-Attention Convolutional Neural Network for Fine-Grained Surface Defect Detection”, IEEE Transactions on Instrumentation and Measurement, 2023.
[21] Y Wang (王优,硕士生), Wentao Hu, L Wen and L Gao, “A New Foreground-perception Cycle-consistent Adversarial Network for Surface Defect Detection with Limited High-noise Samples,” IEEE Transactions on Industrial Informatics. 2023. (中科院一区)
[22] L Wen, Y Wang (王优,硕士生) and X Li, “A New Cycle-consistent Adversarial Networks with Attention Mechanism for Surface Defect Classification with Small Samples,” IEEE Transactions on Industrial Informatics, 2022. (中科院一区)
[23] L Wen, Y Wang (王优,硕士生), XY Li, “A New Automatic Convolutional Neural Network Based on Deep Reinforcement Learning for Fault Diagnosis,” Frontiers of Mechanical Engineering, 2022.
[24] Y Zhang (张杨,硕士生), LR Qiu, YK Zhu, L Wen, XP Luo X, “A new childhood pneumonia diagnosis method based on fine-grained convolutional neural network,” Computer Modeling in Engineering & Sciences, 2022.
学生就业去向:
深造:加拿大西安大略大学(The University of Western Ontario),国防科技大学,哈尔滨工程大学(深圳)
就业:TCL科技集团,宁德时代,远景动力,武汉铁路局,杭州长川科技等
指导学生代表性成果:
1) 王优,硕士生,2022年度研究生国家奖学金.
2) 苏少权,硕士生,2022年第五届大数据驱动的智能制造学术会议优秀论文.
3) 范诗航,硕士生,2024年度研究生国家奖学金.