个人简介
游科顺，男，中共党员，1997年4月生，江西抚州人，2025年6月毕业于江西理工大学，获工学博士学位，主要研究方向为数智化赋能的复杂装备智能运维与服役可靠性技术。以第一作者在Reliability Engineering & System Safety、IEEE Internet of Things Journal等国际权威期刊发表SCI论文20多篇，其中6篇入选ESI全球Top 1%高被引论文，1篇入选ESI全球Top 0.1%热点论文。主持并结题省部级项目1项，参与国家自然科学基金、江西省重点研发项目等多项课题，获国家发明专利及软件著作权十余项。长期担任IEEE Transactions on Industrial Informatics等十余个国际期刊审稿人，入选2025年度斯坦福大学全球前2%顶尖科学家年度影响力榜单。致力于智能诊断、数字孪生、故障预测等前沿研究。
主讲课程
机器学习，人工智能类课程，欢迎对AI交叉学科感兴趣的同学报考
主要论文
代表作（6篇ESI全球1%高被引和1篇ESI全球0.1%热点、H指数14）学术主页：https://scholar.google.com/citations?hl=zh-CN&user=sybCxY0AAAAJ;https://orcid.org/0000-0001-5603-3707.
[1] Y. Keshun,W. Puzhou and G. Yingkui, "Toward Efficient and Interpretative Rolling Bearing Fault Diagnosis via Quadratic Neural Network With Bi-LSTM," in IEEE Internet of Things Journal, 11(13): 23002-23019, (2024), doi: 10.1109/JIOT.2024.3377731. (ESI全球Top 1%高被引文章，中科院一区top期刊, IF:10.6，JCR Q1)
[2] Keshun Y,Guangqi Q, Yingkui G. Optimizing prior distribution parameters for probabilistic prediction of remaining useful life using deeplearning[J].Reliability Engineering & System Safety, 2024, 242: 109793. (中科院一区top期刊，IF：11.0，JCR Q1)
[3]You Keshun，Wang Puzhou, Huang Peng，Gu Yingkui，A sound-vibrational physical-information fusion constraint-guided deep learningmethod for rolling bearing fault diagnosis [J].Reliability Engineering & System Safety, 253 (2025): 110556 (ESI全球Top 1%高被引文章，中科院一区top期刊，IF：11.0，JCR Q1)
[4] Keshun Y,Chenlu L, Yanghui L, et al. DTMPI-DIVR: A digital twins for multi-margin physical information via dynamic interaction of virtual and real sound-vibration signals for bearing fault diagnosis without real fault samples[J].Expert Systems with Applications, 2025, 292: 128592. (中科院一区top期刊，IF：7.5，JCR Q1)
[5] Keshun,Y., Zengwei, L. & Yingkui, G.A performance-interpretable intelligent fusion of sound and vibration signals for bearing fault diagnosis via dynamic CAME [J].Nonlinear Dynamics,112, 20903–20940 (2024).https://doi.org/10.1007/s11071-024-10157-1.(中科院二区top期刊，IF：5.2，JCR Q1)
[6] Keshun Y,Chengyu W, Huizhong L.Research on intelligent implementation of the beneficiation process of shaking table[J].Minerals Engineering, 2023, 199: 108108. (ES1 Top 1%高被引文章，中科院二区top期刊， IF：4.9，JCR Q1)
[7]Y. Keshun, Q. Guangqi and G. Yingkui, "A 3-D Attention-Enhanced Hybrid Neural Network for Turbofan Engine Remaining Life Prediction Using CNN and BiLSTM Models," in IEEE Sensors Journal, vol. 24, no. 14, pp. 21893-21905, 15 July 15, 2024, doi:10.1109/JSEN.2023. 3296670. (ESI全球Top 1%高被引文章，中科院二区Top期刊，IF：4.3，JCR Q1）
[8]You Keshun, Lian Zengwei, Gu Yingkui，A Novel Rolling Bearing Fault Diagnosis Method Based on Time-Series Fusion Transformer with Interpretability Analysis [J].Nondestructive Testing and Evaluation,2024，1-27，doi: https://doi.org/10.1080/10589759.2024.2425813.(中科院二区期刊，IF：4.2，JCR Q2)
[9] Keshun Y, Yingkui G, Yanghui L, et al. A novel physical constraint-guided quadratic neural networks for interpretable bearing fault diagnosis under zero-fault sample[J].Nondestructive Testing and Evaluation, 2025: 1-31. (中科院二区期刊，IF：4.2，JCR Q2), doi:https://doi.org/10.1080/10589759.2025.2534429.
[10]Keshun Y,Guangqi Q, Yingkui G. Remaining useful life prediction of lithium-ion batteries using EM-PF-SSA-SVR with gamma stochastic process[J].Measurement Science and Technology, 2023, 35(1): 015015. (ESI全球Top 0.1%热点文章和Top 1%高被引文章，中科院三区，IF:2.9，JCR Q2)
[11] Keshun Y,Huizhong L. Feature detection of mineral zoning in spiral slope flow under complex conditions based on improved yolov5 algorithm[J].Physica Scripta, 2023, 99(1): 016001. (ESI全球Top 1%高被引文章，中科院三区，IF:2.9，JCR Q2)
[12]You K, Liu H. Research on optimization of control parameters of gravity shaking table [J].Scientific Reports, 2023, 13(1): 1133. (中科院三区，IF:4.6，JCR Q1)
[13] You K,Qiu G, Gu Y. An efficient lightweight neural network using BiLSTM-SCN-CBAM with PCA-ICEEMDAN for diagnosing rolling bearing faults[J].Measurement Science and Technology, 2023, 34(9): 094001.（中科院三区，IF:2.9，JCR Q2）
[14] Liu H,You K. Optimization of dewatering process of concentrate pressure filtering by support vector regression[J].Scientific Reports, 2022, 12(1): 7135. (中科院三区，IF：4.6, JCR Q1)
[15] You K,Qiu G, Gu Y. Rolling bearing fault diagnosis using hybrid neural network with principal component analysis[J]. Sensors, 2022, 22(22): 8906. (中科院三区，IF:3.9，JCR2区）
[16]Keshun Y,Huizhong L. Intelligent deployment solution for tabling adapting deep learning[J]. IEEEAccess, 2023, 11: 22201-22208.(中科院三区期刊，IF:3.9，JCR Q2)
[17] Jiahao L, Shuixian L,Keshun Y*, et al. An end-end arrhythmia diagnosis model based on deep learning neural network with multi-scale feature extraction[J].Physical and Engineering Sciences in Medicine, 2023, 46(3): 1341-1352. (中科院四区，IF:5.0，JCR Q2，通讯作者)
专利、软件著作
[1]游科顺, 古莹奎, 邱光琦.一种基于随机退化过程的锂离子电池RUL预测方法[P].江西省:CN117630682A, 2024-03-01（国家发明专利）
[2] 关开荣, 游科顺,等.一种便携式采摘装置[P].江西省:CN110214549B,2024-03-12（国家发明专利）
[3] Liu, Huizhong,You, Keshun et al. Gravity Concentration System and Chromite Ore Dressing Method[P].AU2021102528A4,2021-07-01（澳大利亚境外发明专利）
[4]关开荣,游科顺,等.一种便携式采摘装置[P].江西省:CN210406247U,2020-04-28（国家实用新型专利）
[5]游科顺，连增卫，古莹奎,汽车锂电池RUL在线评估系统V1.0，2023SR0865002
[6]游科顺，连增卫，古莹奎,涡扇发动机可靠性在线评估系统V1.0，2023SR0865237
[7]游科顺，连增卫，古莹奎,一种端到端滚动轴承故障智能诊断系统V1.0, 2023SR0865003
[8]游科顺，刘惠中,选矿摇床分选过程实时监测及智能化系统V1.0, 2021SR1218595
[9]游科顺，古莹奎，基于振动信号融合的可解释智能故障诊断系统V1.0，2024SR0794502
[10]游科顺，古莹奎，林阳辉，王浦舟，一种旋转机械轴承故障信号仿真与分析系统V1.0，2024SR1120827
[11]游科顺，古莹奎，多元信号实时监测的旋转机械故障预警系统V1.0，2025SR0239541
[12]游科顺，古莹奎，林阳辉，王浦舟，一种冶金旋转机械设备数字化监测和管理平台V1.0，2025SR0254066
[13]游科顺，古莹奎，一种基于虚拟与现实动态交互的数字孪生轴承故障诊断方法，实质性审查（国家发明专利）
