游科顺

个人简介

游科顺，男，中共党员，江西抚州人，工学博士，主要研究方向为装备数字孪生智能运维、动态信号监测与模式识别等。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7篇入选ESI全球Top 1%高被引论文，2篇入选ESI全球Top 0.1%热点论文。主持省部级项目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。

主讲课程

机器学习，信号处理及人工智能类课程，欢迎对AI交叉学科感兴趣的同学报考

主要论文

代表作

[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)

[2] Keshun Y, Guangqi Q, Yingkui G. Optimizing prior distribution parameters for probabilistic prediction of remaining useful life using deep learning[J]. Reliability Engineering & System Safety, 2024, 242: 109793. (中科院一区top)

[3] You Keshun， Wang Puzhou, Huang Peng， Gu Yingkui， A sound-vibrational physical-information fusion constraint-guided deep learning method for rolling bearing fault diagnosis [J].Reliability Engineering & System Safety, 253 (2025): 110556 (ESI全球Top 0.1% 热点文章和Top 1% 高被引文章，中科院一区top) 

[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)

[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. (ESI全球Top 1% 高被引文章，中科院二区top)

[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)

[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）

[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) 

[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), 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)

[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)

[12] 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）

主要的专利、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）

[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）