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1. Li, Y. and Tsung, F. (2009). “False Discovery Rate-Adjusted Charting Schemes for Multistage Process Monitoring and Fault Identification”. Technometrics, 51, 186-205. (SCI, impact factor= 1.711).
2. Jin, M., Li, Y. and Tsung, F. (2010) “Chart Allocation strategy for Serial-Parallel Multistage Manufacturing Processes”. IIE Transactions, 42(8), 577-588. (SCI, impact factor= 1.287).
3. Han, D, Tsung, F. and Li, Y. (2010) “A Nonlinear Filter Control Chart For Detecting Dynamic Changes”. Statistica Sinica, 20, 1077-1096. (SCI, impact factor= 0.956).
4. Han, D. and Tsung, F. , Li, Y. and Xian, J. (2010) “Detection of Changes in a Random Financial Sequence with a Stable Distribution ". Journal of Applied Statistics, 37(7), 1089-1111. (SCI, impact factor= 0.407).
5. Li, Y. and Tsung, F. (2011). “Chart Allocation Strategy for Serial Parallel_Multistage Manufacturing Processes with Multiple Faults”. Journal of the Chinese Institute of Industrial Engineers, 28(7),493-503.(EI)
6. Li, Y. and Tsung, F. (2011) “Detecting and Diagnosing Covariance Matrix Changes in Multistage Processes”. IIE Transactions, 43(4), 259-274. (SCI, impact factor=1.287).
7. Li, Y. and Tsung, F. (2012). “Multiple Attribute Control Charts with False Discovery Rate Control”. Quality and Reliability Engineering International, 28(8), 857-871. (SCI, impact factor=0.700)
8. Li, Y.; Liu, Y.; Zou, C. and Jiang, W. (2014).A Self-Starting Control Chart for High Dimensional Short-run Process. International Journal of Production Research （中科院二区）, 52, 2, 445-461.(SCI, impact factor=1.460)
9. Li, Y.; Su,Y. and Shu, L.(2014). An ARMAX model for forecasting the power output of a grid connected photovoltaic system. Renewable Energy （中科院一区）. 66, 78-89. (SCI, impact factor=2.989)
10. Huang,W.,Jiang,W.,Wei, Q. and Li, Y. (2015).Projection-based Process Monitoring based on Empirical Divergence. IEEE Intelligent Systems Special Issue on System Informatics，IEEE Intelligent Systems,2015,30(6)：13-16..(SCI,impact factor=2.340)
11. Li, Y.; Shu,L. and Tsung, F.(2016). A False Discovery Approach for Scanning Spatial Disease Clusters with Arbitrary Shapes. IIE Transactions. Vol.48, No.7, 684-698.
12. Li, Y, Yong He, Yan Su, Lianjie Shu(2016).Forecasting the daily power output of a grid-connected photovoltaic system based on multivariate adaptive regression splines, Applied Energy（中科院一区） , 180, 392–401.(SCI, impact factor=5.746)
13. Wang,Z., Li, Y.*, Zhou, X. (2017).A Statistical Control Chart for Monitoring High Dimensional Poisson Data Streams. Quality and Reliability Engineering International，33（2）,307–321. （SCI）
14. Li, Y. Liu, S. and Shu, L. (2018). Wind Turbine Fault Diagnosis Based on Gaussian Process Classifiers Applied to Operational Data. Renewable Energy （中科院一区）. 66:78–89. (impact factor=6.274)
15. Fan, Jinyu; Shu, LJ; Yang, AJ; Li, YT （2020） Phase I analysis of high-dimensional covariance matrices based on sparse leading eigenvalues. Journal of Quality Technology （中科院二区）. 2021, 53(4), 333-346. DOI: 10.1080/00224065.2020.1746212
16. Zhang, Y, Li, Y* and Zhang, GY(2020) Short-term wind power forecasting approach based on Seq2Seq model using NWP data. Energy（中科院一区） . 213, 15，118371 （impact factor=6.082）.
17. Li, Y. Pei, D. and Wu, Z. (2020) "A Multivariate Non-Parametric Control Chart Based on Run Test
Computers & Industrial Engineering" . Computers & Industrial Engineering （中科院二区）. 149, 106839。(impact factor=4.135)
18. Li, Y. Wu, Z. (2020) A condition monitoring approach of multi-turbine based on VAR model at farm level. Renewable Engergy （中科院一区）, 166, 66-80. (SCI, impact factor=2.989)
19.Li, Y. Jiang, W. Shu, L. (2021) Wind turbine fault diagnosis based on transfer learning and convolutional autoencoder with small-scale data. Renewable Energy（中科院一区）, 171, June 2021, Pages 103-115.
20. Yanrong LI,Shizhe PENG,Yanting LI,Wei JIANG (2020). A review of condition-based maintenance: Its prognostic and operational aspects[J]. Frontiers of Engineering Management, 2020, 7(3): 323-334.
21. Lei Y, Li Y*. A novel scheme of domain transfer in document-level cross-domain sentiment classification. Journal of Information Science. May 2021. doi:10.1177/01655515211012329
22. Yanting Li*, Xinghao Peng, Yu Zhang(2022). Forecasting wind power scenarios of multiple wind farms based on vine spatiotemporal copula[J], Renewable Energy （中科院一区）, 201, 950-960.
23. Guangyao Zhang, Yanting Li* and Wenbo Jiang, et al. (2022) A fault diagnosis method for wind turbines with limited labeled data based on balanced joint adaptive network. Neurocomputing(中科院一区), Volume 481, Pages 133-153. DOI10.1016/j.neucom.2022.01.067
24. Zhenyu Wu, Yanting Li*. Lanye Hu.(2022) A Synchronous Multiple Change-point Detecting Method for Manufacturing Process[J]. Computers & Industrial Engineering (中科院一区), 169: 108114. https://doi.org/10.1016/j.cie.2022.108114
25. Songkang Wen, Yanting Li* and Yan Su (2022). A New Hybrid Model for Power Forecasting of a Wind Farm Using Spatial-Temporal Correlations[J]. Renewable Energy（中科院一区）, 198, 155-168
26. Feng Xu, Lianjie Shu, Yanting Li (2023) Joint Diagnosis of High-dimensional Process Mean and Covariance Matrix based on Bayesian Model Selection. Technometrics, accepted.
27. Peng Wang, Yanting Li*. （2023） Probabilistic power curve estimation based on meteorological factors and density LSTM. Energy (中科院一区)， 269(15) 126768.
28. Zhenyu Wu, Li,Y*, Fugee Tsung, Ershun Pan (2023), Real-Time Monitoring and Diagnosis Scheme for IoT Enabled Devices using Multivariate SPC Techniques. IISE Transactions. 55(4). 348-362. https://doi.org/10.1080/24725854.2021.2000681.
29. Wenpo Huang, Lianjie Shu, Yanting Li*. (2023) A Phase I Change-point Method for High-dimensional Process with Sparse Mean Shifts, Naval Research Logistics,（中科院二区）https://doi.org/10.1002/nav.22095
【中文】
1. 何勇,李艳婷*（2017）基于向量自回归模型的移动通信基站流量预测[J].工业工程与管理,22(04)，79-84.
2. 娄璐，李艳婷*（2018）面向未知自相关过程的Bootstrap控制图设计，《工业工程》，21(4), 23-33.
3. 刘姝君,李艳婷*（2019）基于深度高斯过程的多元类别数据分布估计[J].计算机工程,45(2):160-166.
4. 龙威,李艳婷*（2019）基于多元泊松时间序列的累积和控制图设计[J].工业工程与管理,24(4):105-112.
5. 张乔威，李艳婷*（2019）基于R-Vine Copula的多维混合型数据控制图设计.工业工程，22（5）126-149。
6. 龙威,李艳婷*（2020）基于多元泊松模型的累积和控制图设计[J].应用概率统计,36(3):221-237.
7. 张乔微,李艳婷*（2020）基于LOF算法的多维混合型数据控制图设计[J].工业工程, 23(3):145-153.
8. 裴德昭,李艳婷*(2020)基于游程检验的多元非参控制图[J].工业工程,23(02):124-132.
9. 杨立宁,李艳婷*(2021)基于SVD和ARIMA的时空序列分解和预测.计算机工程:47(3),53-61.
10. 蒋文博,胡澜也,宋斐,樊林玉,李艳婷*(2021).基于比例风险模型与机器学习混合方法的电梯故障预测。工业工程与管理：19-27.
11. 胡澜也,蒋文博,李艳婷*.(2021) 基于LightGBM的风力发电机故障诊断，太阳能学报，42(11),255-259。
12. 叶祎旎，李艳婷*(2022) 基于CNN-集成学习的多风机故障诊断。工业工程，25(1),136-143。
13. 赵宇，李艳婷*. (2022) 面向小样本高维数据的秩检验非参控制图 . 中国机械工程，33(09),1104-1114。
14. 郝澜宇, 李艳婷*, 潘尔顺.(2022) 基于Copula模型的多维数据空间扫描监测方法. 统计与决策, 38(14), 5-9。
15. 郝澜宇，周笛，李艳婷*，潘尔顺（2022）考虑二元 Copula 统计量的晶圆制造叠加误差监测[J/OL]．中国机械工程.
16. 彭星皓,李艳婷*.基于时空协方差函数的风能场景生成方法与应用[J].上海交通大学学报,已录用。
17. 王鹏,李艳婷*,张宇. 基于在线Lasso VAR和EGARCH的风场功率集成概率预测[J].上海交通大学学报, 已录用。
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