哈尔滨工业大学

付松

发布日期:2024-05-10 浏览次数:

个人简介 科学研究 论文专利 学术主页 新建主栏目 基本信息 名称 付松,1991年生,机电工程学院助理教授/讲师,硕士生导师,主要从事复杂装备智能性能监控方面研究工作,主持国家自然科学基金青年项目、国家重点研发计划子课题、国家博士后面上基金项目、省博士后面上基金项目、省优秀博士论文重点资助项目等项目,在IEEE Transactions on Neural Networks and Learning Systems、Mechanical Systems and Signal Processing、Reliability Engineering & System Safety、Advanced Engineering Informatics等国际期刊发表SCI论文20篇,其中第一/通讯作者12篇,中科院一区Top期刊9篇,IEEE Trans 1篇,高被引论文(全球前1%)1篇,单篇论文(非综述)最高被引200余次,累计被引用400余次。 奖项荣誉 名称 1. 新时代龙江优秀博士学位论文 (2023年) 2.《计算机集成制造系统》期刊优秀论文(2021年) 3. 哈尔滨工业大学博士研究生国家奖学金(2021年) 4. 哈尔滨工业大学硕士研究生国家奖学金(2017年) 5. 哈尔滨工业大学优秀毕业生(2022年) 教育经历 名称 2018.09-2022.06 哈尔滨工业大学 机械工程 博士 2016.09-2018.06 哈尔滨工业大学 机械工程 硕士 2011.09-2015.06 武汉科技大学 机械工程及其自动化 本科 工作经历 名称 2022.07-至今 哈工大机电工程学院 助理教授 (钟诗胜教授团队) 团队成员 名称 钟诗胜,哈尔滨工业大学机电工程学院教授,博士生导师。2021年4月至今任哈尔滨工业大学(威海)常务副校长。一直从事智能制造技术,具体包CAD/CAPP/PDM/PLM、数控技术与装备、机电设备状态监测和故障诊断等。负责或完成包括国家重点研发计划项目、国家自然科学基金重点项目、国家863计划重点项目和欧盟科技计划项目在内的多个项目。个人主页:http://homepage.hit.edu.cn/zhongshisheng 林琳,哈尔滨工业大学教授,博士生导师,黑龙江省高层次人才。主持国家自然科学基金重点项目和面上项目3项,国家重点研发计划课题4项,省部级及企业横向课题20余项。个人主页:http://homepage.hit.edu.cn/linlin 郭丰,哈尔滨工业大学高级工程师。承研或参研科研项目8项,其中重点研发计划3项,国家自然科学基金1项,黑龙江省科技计划项目1项,其他重要项目3项。发表论文5篇,出版专著1部,发明专利9个,软件著作权2个。个人主页:http://homepage.hit.edu.cn/guof 研究领域 名称 1.装备运行机理建模及数据驱动的装备智能运维 2.制造业信息化、工业知识图谱构建与大数据挖掘 科研项目 名称 1.国家自然科学基金青年项目:航空发动机气路系统性能模型数字重构与早期故障诊断 方法研究(主持,52305570,30万,2024.01-2026.12,进行中) 2.国家自然科学基金重点项目:目标导向的航空发动机车间智能维修关键技术研究(参加,No.U2133202,220万,进行中) 3.国家重点研发计划:产品回收拆解再利用全流程管控平台研发与示范(参加,22022YFB3305800,1.0亿,2022.11-2025.10,进行中) 4.国家重点研发计划:制造大数据驱动的预测运行与精准服务技术及系统(参加,2019YFB1705300,2822万,2019.12-2022.11,结题) 5.航空发动机智能维修决策技术研究(参加,360万,结题) 6.航空发动机大数据中心建设 (参加,250万元,结题) SCI论文 名称 截至目前,以发表SCI论文20篇,其中第一/通讯作者12篇,中科院一区Top期刊9篇,IEEE Trans 1篇,高被引论文(全球前1%)1篇,单篇论文(非综述)最高被引200余次,累计被引400余次。 1.Shi S, Bao J, Fu S*, et al. Improving prediction of N2O emissions during composting using model-agnostic meta-learning[J]. Science of The Total Environment, 2024: 171357.(中科院大类1区, Top刊, IF=9.8) 2. Fu S*, L. Lin, Y. Wang, M. Zhao, F. Guo, S. Zhong, Y. Liu. High imbalance fault diagnosis of aviation hydraulic pump based on data augmentation via local wavelet similarity fusion[J]. Mechanical Systemsand Signal Processing, 2024, 209: 111115. (中科院大类1区,Top期刊,IF=8.4) 3. Fu S*, Lin L, Wang Y, et al. MCA-DTCN: A novel dual-task temporal convolutional network with multi-channelattention for first prediction time detection and remaining useful life prediction[J]. Reliability Engineering &System Safety, 2024, 241: 109696. (中科院大类1区,Top期刊,IF=8.1) 4. Lin L, Jinlei W*, Fu S*, et al. CATA-TCN: A Novel Framework for Remaining Useful Life Prediction of theAircraft Engines, Advanced Engineering Informatics, 2024. (中科院大类1区, Top 期刊,IF=8.8) 5. Wenhui H, Lin L*, Song F*, et al. Differential Contrast Guidance for aeroengine fault diagnosis with Limited Data, Journal of Intelligent Manufacturing, 2024. (中科院大类2区 , IF=8.3) 6. Lin L, He W*, Fu S*, et al. A progressive reconstruction modeling method for aeroengine performance space based on sequence correlation[J]. Measurement, 2024, 225: 113969. (中科院大类2区,Top期 刊,IF=5.6) 7. Lin L, He W, Fu S*, et al. Novel aeroengine fault diagnosis method based on feature amplification[J]. EngineeringApplications of Artificial Intelligence, 2023, 122: 106093.(中科院小类1区,Top期刊,IF=8.0) 8. Fu S, Zhong S, Lin L, et al. A novel time-series memory auto-encoder with sequentially updated reconstructions for remaining useful life prediction[J]. IEEE Transactions on Neural Networks and Learning Systems, 2021, 33(12):7114-7125. (中科院大类1区 , Top期刊, IF=10.4) 9. Zhong S, Fu S, Lin L. A novel gas turbine fault diagnosis method based on transfer learning with CNN[J]. Measurement, 2019, 137: 435-453. (中科院大类2区, Top期刊, IF=5.6, ESI高被引论文) 10. Fu S, Zhang Y, Lin L, et al. Deep residual LSTM with domain-invariance for remaining useful life prediction across domains[J]. Reliability Engineering & System Safety, 2021, 216: 108012. (中科院大类1区,Top期刊,IF=8.1) 11.Fu S, Zhong S, Lin L, et al. A re-optimized deep auto-encoder for gas turbine unsupervised anomaly detection[J].Engineering Applications of Artificial Intelligence, 2021, 101: 104199. (中科院小类1区,Top期刊,IF=8.0) 12. Liu Y, Fu S*, Lin L*, et al. DECVAE: Data augmentation via conditional variational auto-encoder with distribution enhancement for few-shot fault diagnosis of mechanical system[J]. Measurement Science and Technology, 2024,35(4): 046104. (中科院三区,IF=2.4) 13. Zu L, Lin L, Liu J, Song F, et al. SRSCL: A strong-relatedness-sequence-based fine-grained collective entity linking method for heterogeneous information networks[J]. Expert Systems with Applications, 2024, 238:121759. (中科院大类1区,Top期刊,IF=8.5) 14. Xia X, Fu X, Zhong S, Fu S, et al. A multi-agent convolution deep reinforcement learning network for aeroengine fleet maintenance strategy optimization[J]. Journal of Manufacturing Systems, 2023, 68: 410-425. (中科院大类1区, Top期刊, IF=12.1) 15. Lin L, He W, Guo F, Fu S, et al. A novel method for aeroengine performance model reconstruction based on CDAE model[J]. Advanced Engineering Informatics, 2023, 56: 101909. (中科院大类1区, Top期刊, IF=8.8) 16. Lin L, Zu L, Guo F, Fu S, et al. Using combinatorial optimization to solve entity alignment: An efficientunsupervised model[J]. Neurocomputing, 2023, 558: 126802. (中科院二区, Top期刊, IF=6.0) 17. Lin L, Tong C, Guo F, Fu S, et al. A Self-Attention Integrated Learning Model for Landing Gear PerformancePrediction[J]. Sensors, 2023, 23(13): 6219. (中科院三区, IF=3.9) 18.Lv Y, Lin L, Fu S, et al. A double-layer progressive architecture-based surrogate model for efficiency analysis of spiral shaft in shield machine[J]. Automation in Construction, 2024, 160: 105298.(中科院大类1区, Top刊, IF=10.3) 19.Zu L, Lin L, Fu S, et al. SelectE: Multi-scale adaptive selection network for knowledge graph representation learning[J]. Knowledge-Based Systems, 2024: 111554.(中科院大类1区, Top刊, IF=8.8) 20.Lin L, Zu L, Guo F, Fu S, et al. Using combinatorial optimization to solve entity alignment: An efficient unsupervised model[J]. Neurocomputing, 2023, 558: 126802.(中科院大类2区, Top刊, IF=5.6) 中文论文与会议论文 名称 1.付松,钟诗胜,林琳. 基于迁移学习的民航发动机小样本故障诊断[J].计算机集成制造系统, 2021. (EI源刊,CCF推荐,知网高被引, 优秀论文) 2. Zhong S, Fu S, Lin L, et al. A novel unsupervised anomaly detection for gas turbine using isolation forest [C]//2019 IEEE International Conference on Prognostics and Health Management (ICPHM). IEEE,2019 (EI会议) 3. Fu X, Xia X, Zhong S, Fu S, et al. A Novel Label Correction Method for Remaining Useful Life Prediction of Turbofan Engines[C]//2021 IEEE International Conference on Sensing, Diagnostics, Prognostics, and Control (SDPC). IEEE, 2021: 74-80. (EI 会议) 4. ZHONG S S, Fu S, FU X Y. Application of Deep Learning in Comprehensive Performance Evaluation of Aero Engines[C]//2017 2nd International Conference on Artificial Intelligence. Techniques and Applications (AITA 2017). 2017.(EI 会议) Journal Preprints 名称 1.Fu S*, Lin L, et al.DCSIAN: a novel deep cross-scale interactive attention network for fault diagnosis of aviation hydraulic pumps, Reliability Engineering & System Safety. (中科院大类1区,Top期刊,IF=8.1, Under Revision) 2. Fu S*, Lin L, et al. Multiscale Dynamically Parallel Shrinkage Network for Fault Diagnosis of Aviation Hydraulic Pumps. ISA TRANSACTIONS. (中科院大类1区,Top期刊,IF=7.3, Under Revision) 3. Fu S*, Lin L, et al. CLIME: Conditional Local Intrinsic Modes Expansion for Data Augmentation and Its Application in Highly Imbalanced Fault Diagnosis of Mechanica System, Journal of Intelligent Manufacturing. (中科院大类2区,IF=8.3, Under Revision) 4. Lin L*, Wenhui H, Fu S* et al. A novel method based on contrastive meta-learning for aeroengine fault diagnosis with limited data, Reliability Engineering & System Safety. (中科院大类1区,Top期刊,IF=8.1, Under Revision) 5. Lin L, Chang sheng T*, Fu S*, et al. A Prediction of Crack Propagation on Aircraft Wing via AK-TCN, Advanced Engineering Informatics. (中科院大类1区,Top期刊, IF=8.8, Under Revision) 专利 名称 授权专利: 1.钟诗胜,付松,林琳,付旭云,张永健. 一种重优化深度自动编码器及发动机自动检测系统, 2020104067078. 2.钟诗胜,付旭云,张永健,付松. 一种复杂装备故障诊断方法及系统, 2018101312475. 3.林琳,童昌圣,郭丰,钟诗胜,付松. 航空发动机装配过程的振动值预测方法,2022105191298. 受理专利: 1.付松,林琳,郭丰. 一种基于双深度残差LSTM的复杂装备剩余寿命的预测方法及系统,202211294744X. 2.付松,林琳,郭丰. 旋转机械设备小样本数据生成及故障诊断方法,2022114125952 3.林琳,童昌圣,付松. 一种基于自注意力集成学习的起落架性能预测方法,2023100636196. 4.林琳,何文辉,付松. 基于特征扩增的航空发动机故障诊断方法, 2023100188715. 5.林琳,何文辉,郭丰,付松. 基于DLBR的航空发动机性能模型重构方法,2022112739945. 友情链接 链接名称 谷歌学术主页 链接地址 https://scholar.google.com.hk/citations?hl=zh-CN&user=bYyRlTsAAAAJ 简单介绍 链接名称 知网主页 链接地址 https://au.cnki.net/author/personalInfo/000057998842 简单介绍 链接名称 ResearchGate主页 链接地址 https://www.researchgate.net/profile/Song-Fu-9 简单介绍

上一篇:陶冶     下一篇:李立青