陈茂银

陈茂银，男，1975年6月生，博士，中国石油大学（北京）信息科学与工程学院/人工智能学院教授，德国洪堡学者，博导/硕导。

1997年和2000年于山东曲阜师范大学获得学士和硕士学位；2003年于上海交通大学获得控制科学与工程学科博士学位。2003年至2005年，在清华大学自动化系进行博士后研究。2006年至2008年，在德国波茨坦大学物理系进行洪堡学者研究。2005年至2023年，在清华大学自动化系任职助理研究员、副教授/副研究员。

目前已经在Automatica、IEEE Transactions on Automatic Control、IEEE Transactions on Industrial Informatics、IEEE Transactions on Industrial Electronics、 IEEE Transactions on Cybernetics等国际期刊上发表和录用论文120多篇，其中Automatica 2篇、IEEE Trans系列期刊40余篇。

主持国家自然科学基金（青年、面上）项目4项，主持国家重点研发计划课题2项，主持**863项目1项，主持**973项目课题1项。曾以核心骨干身份参加国家自然科学基金委员会应急管理重点项目1项、国家自然科学基金委重大项目2项、国家自然基金创新群体1项。

曾获中国自动化学会自然科学奖一等奖（2011年，排名第一）及二等奖（2019年，排名第一）。连续入选（2020年-2022年）斯坦福大学和爱思唯尔联合发布的全球前2%顶尖科学家“终身科学影响力排行榜”和“年度科学影响力排行榜”。

招生专业： 控制科学与工程

Email: mychen@cup.edu.cn, maoyinchen@163.com

办公地点：主楼B1510

主要研究方向：

（1）工业过程低碳运行智能监测与控制

在碳达峰碳中和战略目标下，低碳运行一直是石化、冶金等行业的重大需求。围绕低碳运行，研究人工智能驱动的故障诊断技术、工业过程全流程运行的质量优化与协同控制问题。

（2）面向故障诊断的可解释人工智能

尽管故障诊断已经取得了较大发展，但仍然无法突破当前故障诊断领域的理论与技术瓶颈。以深海油气系统微弱故障/异常检测为出发点，研究可解释的人工智能领域与方法。

（3）全自主故障诊断机器人

现有故障诊断方法完全依靠人类进行设计与实现的。将研究全自主故障诊断机器人，期望实现诊断方法的全自主设计与实现，进而实现人类最少干预下的故障诊断。

主要论著：

[1]W Wu, W Yi, J Li, M Chen, X Zheng. Automatic Identification of Human Subgroups in Time-Dependent Pedestrian Flow Networks. IEEE Transactions on Multimedia, 2024, DOI: 10.1109/TMM.2023.3262975

[2]J Zhang, J Xiao, M Chen, X Hong. Multimodal Continual Learning for Process Monitoring: A Novel Weighted Canonical Correlation Analysis With Attention Mechanism. IEEE Transactions on Neural Networks and Learning Systems, 2024, DOI: 10.1109/TNNLS. 2023.3331732

[3]X Liu, M Chen, D Zhou, L Sheng. Fault-Tolerant Control of Stochastic High-Order Fully Actuated Systems. IEEE Transactions on Cybernetics, 2024, DOI: 10.1109/TCYB.2023. 3320441

[4]M Wang, M Xie, Y Wang, M. Chen. A Deep Quality Monitoring Network for Quality-Related Incipient Faults. IEEE Transactions on Neural Networks and Learning Systems. 2024, DOI: 10.1109/TNNLS.2023.3322625

[5]J Zhang, D Zhou, M Chen, X Hong, Continual learning-based probabilistic slow feature analysis for  monitoring multimode nonstationary processes, IEEE Transactions on Automation Science and Engineering, 2024, doi: 10.1109/TASE.2022.3219125

[6]M Wang, D Zhou, M Chen, Hybrid Variable Monitoring Mixture Model for Anomaly Detection in Industrial Processes, IEEE Transactions on Cybernetics 54 (2024) 319 - 331

[7]J Zhang, M. Chen,X Hong. Monitoring multimode nonlinear dynamic processes: an efficient sparse dynamic approach with continual learning ability, IEEE Transactions on Industrial Informatics 19 (2023) 8029 - 8038

[8]W Wu , W Yi, J Li, M Chen, X Zheng. Simulating the Evacuation Process Involving Multitype Disabled Pedestrians. IEEE Transactions on Computational Social System 10 (2023) 2400 - 2410

[9]J Zhang, D Zhou, M Chen, Self-learning sparse PCA for multimode process monitoring. IEEE Transactions on Industrial Informatics 19 (2023) 29-39

[10]M Wang, D Zhou, M Chen. Hybrid variable monitoring: An unsupervised process monitoring framework with binary and continuous variables. Automatica 147 (2023) 110670

[11]M Wang, D Zhou, M Chen. Anomaly Monitoring of Nonstationary Processes with Continuous and Two-valued Variables. IEEE Transactions on Systems, Man and Cybernetics: Systems 53 (2023) 49-58

[12]M Wang, D Zhou) M Chen. Adjustable Multimode Monitoring with Hybrid Variables and Its Application in a Thermal Power Plant. IEEE Transactions on Industrial Informatics 19 (2023) 1425-1435

[13]J Zhang, D Zhou, M Chen, Xia Hong. Continual learning for multimode dynamic process monitoring with applications to an ultra-supercritical thermal power plant. IEEE Transactions on Automation Science and Engineering 20 (2023) 137-150

[14]J Zhang, D Zhou, M Chen. Adaptive cointegration analysis and modified RPCA with continual learning ability  for monitoring multimode nonstationary processes. IEEE Transactions on Cybernetics 53 (2023) 4841-4854

[15]M Wang, D Zhou, M Chen. Recursive Hybrid Variable Monitoring for Fault Detection in Nonstationary Industrial Processes. IEEE Transactions on Industrial Informatics 18 (2022) 7296 - 7304

[16]D Liu, M Wang, M Chen. Feature Ensemble Net: A Deep Framework for Detecting Incipient Faults in Dynamical Processes. IEEE Transactions on Industrial Informatics 18 (2022) 8618-8628

[17]D Wu, D Zhou, M Chen. Probabilistic Stationary Subspace Analysis for Monitoring Nonstationary Industrial Processes with Uncertainty. IEEE Transactions on Industrial Informatics 18 (2022) 3114-3125

[18]D Wu, D Zhou, M Chen. Performance-Driven Component Selection in the Framework of PCA for Process Monitoring: A Dynamic Selection Approach. IEEE Transactions on Control Systems Technology 30 (2022) 1171 - 1185

[19]J Zhang, D Zhou, M Chen. Monitoring multimode processes: a modified PCA algorithm with continual learning ability. Journal of Process Control 103 (2021) 76-86

[20]D Wu, D Zhou, M Chen. Output-relevant common trend analysis for KPI-related nonstationary process monitoring with applications to thermal power plants. IEEE Transactions on Industrial Informatics 17 (2021) 6664–6675

[21]D Liu, J Shang, M Chen. Principal Component Analysis-based Ensemble Detector for Incipient Faults in Dynamic Processes. IEEE Transactions on Industrial Informatics 17 (2021) 5391-5401

[22]J Sang, J Zhang, D Zhou, M Chen,X Tai. Incipient Fault Detection for Air Brake System of High-speed Trains, IEEE Transactions on Control Systems Technology 29 (2021) 2026-2037

[23]J Shang, M Chen, T Chen, Optimal Linear Encryption Against Stealthy Attacks on Remote State Estimation, IEEE Transactions on Automatic Control 66 (2021) 3592-3607

[24]M Wang, D Zhou, M Chen, Y Wang. Anomaly Detection in the Fan System of a Thermal Power Plant Monitored by Continuous and Two-valued Variables, Control Engineering Practice 102 (2020) 104522

[25]Y Wang, D Zhou, M Chen, M Wang. Weighted part mutual information related component analysis for quality-related process monitoring, Journal of Process Control 88 (2020) 111–123

[26]J Shang, D Zhou, M Chen, H. Zhang, Incipient sensor fault diagnosis in multimode processes using conditionally independent Bayesian learning based recursive transformed component statistical analysis, Journal of Process Control 77 (2019) 7-19

[27]T Guo, D Zhou, J Zhang, M Chen, X Tai. Fault detection based on robust characteristic dimensionality reduction. Control Engineering Practice 84 (2019) 125-138

[28]J Shang, M Chen, H Zhang, H Ji, D Zhou, H Zhang, M Li. Increment-based recursive transformed component statistical analysis for monitoring blast furnace iron-making processes: An index-switching scheme. Control Engineering Practice 77 190-200,2018

[29]J Shang, M Chen, H Ji, D Zhou. Isolating incipient sensor fault based on recursive transformed component statistical analysis. Journal of Process Control 64 (2018) 112-122

[30]J Shang, M Chen, H Ji, D Zhou, H Zhang, M Li. Dominant trend based logistic regression for fault diagnosis in nonstationary processes. Control Engineering Practice 66 (2018) 156-168

[31]M Chen, J Shang, Recursive Spectral Meta-Learner for Online Combining Different Fault Classifiers. IEEE Transactions on Automatic Control 63 (2018) 586-593

[32]J Shang, M Chen, Recursive dynamic transformed component statistical analysis for fault detection in dynamic processes. IEEE Transactions on Industrial Electronics 65 (2018) 578-588

[33]J Shang, M Chen, D Zhou. Dominant trend based logistic regression for fault diagnosis in non-stationary processes. Control Engineering Practice 66 (2017) 156–168

[34]J Shang, M Chen, H Ji, D Zhou. Recursive Transformed Component Statistical Analysis for Incipient Fault Detection. Automatica 80 (2017) 313-327

[35]H Zhang, C Jia, M Chen. Remaining Useful Life Prediction for Degradation Processes with Dependent and Non-Stationary Increments. IEEE Transactions on Instrumentation & Measurement 70 (2021) 3519212

[36]H Zhang, M Chen, J Shang, et al.. Stochastic Process-based Degradation Modeling and RUL Prediction: From Brownian Motion to Fractional Brownian Motion. Science China 64 (2021) 171201

[37]H Zhang, D Zhou, M Chen, J. Shang, FBM-Based Remaining Useful Life Prediction for Degradation Processes With Long-Range Dependence and Multiple Modes, IEEE Transactions on Reliability 68 (2019) 1021-1033

[38]X Xi, M Chen, D Zhou, Remaining useful life prediction for multi-component systems with hidden dependencies, Science China Information Sciences 62 (2019) 22202

[39]H Zhang, D Zhou, M Chen, X Xi, Predicting remaining useful life based on a generalized degradation with fractional Brownian motion, Mechanical Systems and Signal Processing 115 (2019) 736-752,2019

[40]X Xi, M Chen, H Zhang, D Zhou. An improved non-Markovian degradation model with long-term dependency and item-to-item uncertainty. Mechanical Systems and Signal Processing 105 (2018) 467-480

[41]M Chen, Y Jiang, D Zhou. Decentralized maintenance for multistate systems with heterogeneous components. IEEE Transactions on Reliability 67 (2018) 701-714

[42]X Xi, M Chen, D Zhou. Remaining useful life prediction for multi-component systems with hidden dependencies. Science China Information Sciences 105 (2018) 467-480

[43]H Zhang, M Chen, X Xi, D Zhou. Remaining Useful Life Prediction for Degradation Processes with Long-range Dependence, IEEE Transactions on Reliability 66 (2017) 1368-1379

[44]X Xi, M Chen, D Zhou. Remaining Useful Life Prediction for Degradation Processes with Memory Effects, IEEE Transactions on Reliability 66 (2017) 751-760

[45]L Zhao, M Chen, D Zhou. General (N,T,tau) Opportunistic Maintenance for mutli-component systems with evident and hidden failures. IEEE Transactions on Reliability 65 (2016) 1298-1312

[46]M Chen, H Fan, C Hu, D Zhou. Maintaining Partially Observed Systems With Imperfect Observation and Resource Constraint. IEEE Transactions on Reliability 63 (2014) 881-890

[47]M Wei, M Chen, D Zhou. Multi-sensor information based remaining useful life prediction with anticipated performance. IEEE Transactions on Reliability 62 (2013) 183-198

[48]M Chen,C Xu, D Zhou. Maintaining systems with dependent failure modes and resource constraints. IEEE Transactions on Reliability 61 (2012) 440-451

[49]X Lu, M Chen, D Zhou. Exact results on the statistically expected total cost and optimal solutions for extended periodic imperfect preventive maintenance. IEEE Transactions on Reliability 61 (2012) 426-439

[50]X Lu, M Chen, M Liu, D Zhou. Optimal imperfect periodic preventive maintenance for systems in the time-varying environment. IEEE Transactions on Reliability 61 (2012) 378-388

[51]H Fan, C Hu, M Chen, D Zhou. Cooperative predictive maintenance of systems with dependent failure modes and resource constraint. IEEE Transactions on Reliability. 60 (2011) 144-157

[52]M Chen, Synchronization in complex dynamical networks with random sensor delay. IEEE Trans. Circuits and Systems Part II. 57 (2010) 46-50

[53]Y Shang, M Chen, J Kurths. Generalized synchronization of complex networks. Physical Review E 80 (2009) 027201

[54]P Li, M Chen,Y Wu, J Kurths. Matrix measure criterion for synchronization in coupled map networks. Physical Review E 79 (2009) 067102

[55]M Chen, Shang Y, Zou Y, Kurths J. Synchronization in the Kuramoto model: A dynamical gradient network approach. Physical Review E 77 (2008) 027101

[56]M Chen. Chaos synchronization in complex networks. IEEE Trans. on Circuits and Syst. I: Regular Paper. 55 (2008) 1335

[57]M Chen. Synchronization in time-varying networks: a matrix measure approach. Physical Review E 76 (2007) 016104

[58]M Chen, J Kurths. Chaos synchronization and parameter estimation from a scalar output signal. Physical Review E 76 (2007) 027203

[59]M Chen, Kurths J. Synchronization of time-delayed systems. Physical Review E 76 (2007) 036212

[60]M Chen, Zhou D. Synchronization in uncertain complex networks. Chaos 16 (2006) 013101.

[61]M Chen. Some simple synchronization criteria for complex dynamical networks. IEEE Trans. on Circuits and Syst. II: Express Brief 53 (2006) 1185

主要科研项目

[1]工业过程微小故障检测的特征集成网络方法，国家自然科学基金面上项目，2024-2027，负责人

[2]人-车-路协同疏导与管控技术研究及应用示范, 国家重点研发计划课题，2020-2022，负责人

[3]大数据驱动的故障检测：改进的PCA与PLS方法，国家自然科学基金面上项目，2019-2022，负责人

[4]危险化学品全生命周期信息感知与传输技术，国家重点研发计划课题，2018-2020，负责人

[5]受物理安全感知约束的群系统协同编队，国家自然科学基金面上项目，2015-2018，负责人

[6]基于动态优化策略的复杂网络研究，国家自然科学基金青年基金，2009-2011，负责人

[7]大型发电机组异常工况智能预测与自愈控制研究，国家自然科学基金委员会应急管理项目，2018-2020，主要参加者

[8]高速列车信息控制系统实时故障诊断与应用验证，国家自然科学基金委重大项目，2015-2019，主要参加者

[9]大型高炉非正常工况诊断与安全运行方法及实现技术，国家自然科学基金委重大项目2013-2017，主要参加者

[10]飞行器威胁目标识别与图像鲁棒匹配理论与方法，973项目课题，2010-2014，主要参加者

[11]复杂系统控制与信息处理中的若干关键问题研究与应用，国家自然科学基金创新研究群体，2011-2013，主要参加者

[12]复杂工程系统故障预测与预测维护理论及关键技术研究，国家自然科学基金重点项目，2008-2011，主要参加者

奖励及荣誉：

[1]2020年-2022年，连续入选全球前2%顶尖科学家“终身科学影响力”和“年度科学影响力”排行榜

[2]复杂信息物理群系统的同步控制与运行安全监测理论，中国自动化学会自然科学奖二等奖（2019,排名第一）

[3]复杂网络化系统的滤波、诊断与协同控制，中国自动化学会自然科学奖一等奖（2011，排名第一）

[4]德国洪堡学者（2006-2018）

[5]清华大学优秀博士后（2005）

[6]上海市优秀博士论文（2005）