姜庆超
国家优秀青年科学基金获得者
上海市浦江学者
德国洪堡学者
姜庆超，男，1986年出生。工学博士。副研究员、硕士生导师。长期从事复杂工业过程智能建模、数据驱动分布式过程监测、过程优化运行研究工作，先后赴加拿大University of Alberta, 德国University of Duisburg-Essen, 以及香港The Hong Kong University of Science and Technology 访问学习。近五年以第一作者或通讯作者在IEEE TIE、IEEE TCST、AIChE J以及J Process Control等重要学术刊物上发表SCI收录论文30余篇。先后承担国家自然科学基金项目、上海市浦江人才计划项目等。
研究方向：
复杂工业过程建模、监测与优化，数据驱动过程故障诊断，人工智能及其工业应用，统计学习理论与方法。
国内外学术任职：
中国自动化学会技术过程的故障诊断与安全性专业委员会委员；
中国自动化学会数据驱动控制、学习与优化专业委员会委员；
中国自动化学会青年工作委员会委员；
IEEE Member
主讲课程：
《检测技术（双语）》、《工业控制装置与系统实验》、《自动化专业认识实习》
代表性学术论文有：
1.Qingchao Jiang, Furong Gao, Xuefeng Yan, and Hui Yi. Multiobjective Two-Dimensional CCA-Based Monitoring for Successive Batch Processes with Industrial Injection Molding Application. IEEE Transactions on Industrial Electronics, DOI: 10.1109/TIE.2018.2860571.
2.Qingchao Jiang, Furong Gao, Hui Yi, and Xuefeng Yan. Multivariate Statistical Monitoring of Key Operation Units of Batch Processes Based on Time-Slice CCA. IEEE Transactions on Control Systems Technology, DOI: 10.1109/TCST.2018.2803071.
3.Qingchao Jiang, Steven X. Ding, Yang Wang, and Xuefeng Yan. Data-Driven Distributed Local Fault Detection for Large-Scale Processes Based on the GA-Regularized Canonical Correlation Analysis. IEEE Transactions on Industrial Electronics, 2017, 64(10): 8148-8157.
4.Qingchao Jiang, Biao Huang, Steven X. Ding, and Xuefeng Yan. Bayesian Fault Diagnosis with Asynchronous Measurements and Its Application in Networked Distributed Monitoring. IEEE Transactions on Industrial Electronics, 2016, 63, 6316-6324.
5.Qingchao Jiang and Biao Huang. Distributed Monitoring for Large-Scale Processes Based on Multivariate Statistical Analysis and Bayesian Method. Journal of Process Control, 2016, 46, 75-83.
6.Qingchao Jiang, Biao Huang, and Xuefeng Yan. GMM and Optimal Principal Components-Based Bayesian Method for Multimode Fault Diagnosis. Computer & Chemical Engineering, 2016, 84, 338-349.
7.Qingchao Jiang, Xuefeng Yan, and Biao Huang. Performance-Driven Distributed PCA Process Monitoring Based on Fault-Relevant Variable Selection and Bayesian Inference. IEEE Transactions on Industrial Electronics, 2016, 63, 377-386.
8.Qingchao Jiang and Xuefeng Yan. Nonlinear Plant-Wide Process Monitoring Using MI-Spectral Clustering and Bayesian Inference-Based Multiblock KPCA. Journal of Process Control, 2015, 32, 38-50.
9.Qingchao Jiang and Xuefeng Yan. Plant-Wide Process Monitoring Based on Mutual Information-Multiblock Principal Component Analysis. ISA Transactions, 2014, 53(5): 1516–1527 (IF: 2.984)
10.Qingchao Jiang and Xuefeng Yan. Just-In-Time Reorganized PCA Integrated with SVDD for Chemical Process Monitoring. AIChE Journal, 2014, 60 (3): 949–965.