Shi Hongbo, Professor and Doctoral Supervisor. At ECUST, he is the secretary of the Party Committee of School of Information Science and Engineering and the vice director of Key Laboratory of Smart Manufacturing in Energy Chemical Process, Ministry of Education.

Prof. Shi has devoted decades to working on process modeling and advanced control technology in the process industry, the theory and methods of synthetic automatic systems, industrial fault detection and diagnosis technology, and process system engineering. He has published more than 200 papers in well-known journals at home and abroad in the above fields.

As the mainly to complete and the principal investigator, Prof. Shi participated in several national, provincial ministries, and enterprise programs for science and technology development. Among them, “Computer Control of Chemical Fertilizer Production Process” (Sichuan Chemical General Factory) won the second prize of Scientific and Technological Progress Award of the Ministry of Chemical Industry and the third prize of the State Scientific and Technological Progress Award. “CIMS Typical Application Project of Cangzhou Dahua Group”, which is Chinese National Programs for High Technology Research and Development, won the second prize of Hebei Provincial Science and Technology Progress, and “Multi-source information estimation theory and resource optimization method for complex environments” won the second prize of the 2019 Shanghai Natural Science Award. Furthermore, he received a series of honors, including the Shanghai Dawn Scholar in 2003, the Baosteel Excellent Teacher Award in 2012, and the first prize of Shanghai Teaching Achievement in 2013 and 2017 (as the first person to complete).

Department: Department of Automation, School of Information Science and Engineering

Address: 130 Meilong Road, Shanghai

Work Phone: 021-64252189

E-mail: hbshi@ecust.edu.cn

Research Interests:

Process modeling and advanced control technology in the process industry, the theory and methods of synthetic automatic systems, industrial fault detection and diagnosis technology, and process system engineering

Professional Services:

Standing Committee of Technical Committee on Process Control of Chinese Association of Automation

Member of Technical Committee on Fault Diagnosis and Safety of Technical Processes of Chinese Association of Automation

Member of Committee on Information Technology Application of The Chemical Industry and Engineering Society of China

Director of Shanghai Instrument and Control Society

Executive Director of Shanghai Microcomputer Applications Society

Teaching:

Automatic Control Principle, undergraduate course

Multivariable System Theory, graduate course

Research Program:

• Jan, 2021 to Dec. 2024, NSFC (National Natural Science Foundation of China) General Program, 62073141, Techniques and applications for the whole process monitoring, diagnosis and control based on key performance indicators, PI.

• Oct. 2020 to Sep. 2023, “Development and Demonstration of Key Technologies for Preventive Conservation and Risk Control of Museum Cultural Relics”, Shanghai Museum, Environmental Risk Assessment and Early Warning Prediction Techniques for Museum Cultural Relics, PI.

• Jan. 2017 to Dec. 2020, NSFC (National Natural Science Foundation of China) General Program, 61673173, An Integrated Approach to Chemical Process Fault Detection and State Estimation, PI.

• Jan. 2014 to Dec. 2017, NSFC General Program, 61374140, Fault Detection and Diagnosis Methods for Instability Modes in Process Industries, PI.

• Jan. 2011 to Dec. 2011, NSFC General Program, 61074079, Theory and Application of Data-driven Multi-modes Industrial Process Monitoring, PI.

• 2003, “Dawn” Program of Shanghai Education Commission, Polymerisation production process modeling and advanced control technology, PI.

• Chinese National Programs for High Technology Research and Development, 2002AA412120, Modelling, optimization and software development for several complex petrochemical plants, PI.

Publications:

• Yang Tao, Hongbo Shi*, Bing Song, Shuai Tan. Hierarchical latent variable extraction and multisegment probability density analysis method for incipient fault detection, IEEE Transactions on Industrial Informatics, 2022, 18 (4): 2244-2254.

• Lei Guo, Hongbo Shi*, Shuai Tan, Bing Song, Yang Tao. Multiblock Adaptive Convolution Kernel Neural Network for Fault Diagnosis in a Large-Scale Industrial Process[J]. Industrial & Engineering Chemistry Research, 2022, 61(14): 4879-4895.

• Jun Sun, Hongbo Shi*, Jiazhen Zhu, Bing Song, Yang Tao, Shuai Tan. Self-attention-based Multi-block regression fusion Neural Network for quality-related process monitoring[J]. Journal of the Taiwan Institute of Chemical Engineers, 2022, 133: 104140.

• Zhenyang Qu, Hongbo Shi*, Shuai Tan, Bing Song, Yang Tao. A flow-guided self-calibration Siamese network for visual tracking[J]. The Visual Computer, 2022: 1-13.

• Shuai Tan*, Fulin Gao, Hongbo Shi, Huaicheng Yan, Zheng Mu. Multi-Module Decision Fusion in Operational Status Monitoring[J]. IEEE Transactions on Control Systems Technology, 2022.

• Yutang Xiao, Hongbo Shi*, Boyu Wang, Yang Tao, Shuai Tan, Bing Song. Weighted Conditional Discriminant Analysis for Unseen Operating Modes Fault Diagnosis in Chemical Processes[J]. IEEE Transactions on Instrumentation and Measurement, 2022, 71: 1-14.

• Shuai Tan*, Aimin Wang, Hongbo Shi, Lei Guo. Rolling Bearing Incipient Fault Detection via Optimized VMD Using Mode Mutual Information[J]. International Journal of Control, Automation and Systems, 2022, 20(4): 1305-1315.

• Yumin He, Hongbo Shi*, Shuai Tan, Bing Song, Jiazhen Zhu. Multiblock temporal convolution network-based temporal-correlated feature learning for fault diagnosis of multivariate processes[J]. Journal of the Taiwan Institute of Chemical Engineers, 2021, 122: 78-84.

• Yang Tao, Hongbo Shi*, Bing Song, Xinggui Zhou, Shuai Tan. A Supervised Adaptive Resampling Monitoring Method for Quality Indicator in Time-Varying Process[J]. IEEE Transactions on Instrumentation and Measurement, 2021, 70: 1-10.

• Fulin Gao, Shuai Tan*, Hongbo Shi, Zheng Mu. A status-relevant blocks fusion approach for operational status monitoring[J]. Engineering Applications of Artificial Intelligence, 2021, 106: 104455.

• Majed Aljunaid, Yang Tao, Hongbo Shi*. A Novel Mutual Information and Partial Least Squares Approach for Quality-Related and Quality-Unrelated Fault Detection[J]. Processes, 2021, 9(1): 166.

• Yutang Xiao, Hongbo Shi*, Boyu Wang, Yang Tao, Shuai Tan, Bing Song. Adaptive Manifold Discriminative Distribution Alignment for Fault Diagnosis of Chemical Processes[J]. Industrial & Engineering Chemistry Research, 2021, 60(27): 9860-9870.

• Jiazhen Zhu, Hongbo Shi*, Bing Song, Yang Tao, Shuai Tan. Convolutional Neural Network based Feature Learning for Large-scale Quality-related Process Monitoring[J]. IEEE Transactions on Industrial Informatics, 2021.

• Bing Song, Hongbo Shi*, Shuai Tan, Yang Tao. Serial correlated–uncorrelated concurrent space method for process monitoring[J]. Journal of Process Control, 2021, 105: 292-301.

• Jiazhen Zhu, Hongbo Shi*, Bing Song, Yang Tao, Shuai Tan, Tianqing Zhang. Nonlinear process monitoring based on load weighted denoising autoencoder[J]. Measurement, 2021, 171: 108782.

• Mengyu Rong, Hongbo Shi*, Bing Song, Yang Tao. Multi-block dynamic weighted principal component regression strategy for dynamic plant-wide process monitoring[J]. Measurement, 2021, 183: 109705.

• Ebrahim Alnahari, Hongbo Shi*. A Tandem Running Strategy-Based Heat Transfer Search Algorithm and Its Application to Chemical Constrained Process Optimization[J]. Processes, 2021, 9(11): 1961.

• Fulin Gao, Shuai Tan*, Hongbo Shi, Yang Tao, Bing Song. Improved Ensemble Feature Selection Based on DT for KPI Prediction[J]. IEEE Access, 2021, 9: 136861-136871.

• Bing Song, Hongbo Shi*, Shuai Tan, Yang Tao. Multisubspace orthogonal canonical correlation analysis for quality-related plant-wide process monitoring[J]. IEEE Transactions on Industrial Informatics, 2020, 17(9): 6368-6378.

• Bing Song, Hongbo Shi*, Shuai Tan, Bo Zhao. Fault detection and diagnosis via standardized k nearest neighbor for multimode process[J]. Journal of the Taiwan Institute of Chemical Engineers, 2020, 106: 1-8.

• Jiazhen Zhu, Hongbo Shi*, Bing Song, Yang Tao, Shuai Tan. Information concentrated variational auto-encoder for quality-related nonlinear process monitoring[J]. Journal of Process Control, 2020, 94: 12-25.

• Jiazhen Zhu, Hongbo Shi*, Bing Song, Shuai Tan, Yang Tao. Deep neural network based recursive feature learning for nonlinear dynamic process monitoring[J]. The Canadian Journal of Chemical Engineering, 2020, 98(4): 919-933.

• Nanxi Li, Hongbo Shi*, Bing Song, Yang Tao. Temporal-Spatial Neighborhood Enhanced Sparse Autoencoder for Nonlinear Dynamic Process Monitoring[J]. Processes, 2020, 8(9): 1079.

• Jian Yang, Jingtao Dong, Hongbo Shi*, Shuai Tan. Quality monitoring method based on enhanced canonical component analysis[J]. ISA transactions, 2020, 105: 221-229.

• Ebrahim Alnahari, Hongbo Shi*, Khalil Alkebsi. Quadratic interpolation based simultaneous heat transfer search algorithm and its application to chemical dynamic system optimization[J]. Processes, 2020, 8(4): 478.

• Bo Zhao, Bing Song, Hongbo Shi*, Shuai Tan. Quality modeling and monitoring for the linear-nonlinear-coexistence process[J]. Journal of the Taiwan Institute of Chemical Engineers, 2020, 106: 51-61.

• Pengfei Huang, Yang Tao, Bing Song, Hongbo Shi*, Shuai Tan. Tensor sequence component analysis for fault detection in dynamic process[J]. The Canadian Journal of Chemical Engineering, 2020, 98(1): 225-236.

• Yang Tao, Hongbo Shi*, Bing Song, Shuai Tan. A novel dynamic weight principal component analysis method and hierarchical monitoring strategy for process fault detection and diagnosis[J]. IEEE Transactions on Industrial Electronics, 2019, 67(9): 7994-8004.

• Bing Song, Huaicheng Yan, Hongbo Shi*, Shuai Tan. Multisubspace elastic network for multimode quality-related process monitoring[J]. IEEE Transactions on Industrial Informatics, 2019, 16(9): 5874-5883.

• Yang Tao, Hongbo Shi*, Bing Song, Shuai Tan. Parallel quality-related dynamic principal component regression method for chemical process monitoring[J]. Journal of Process Control, 2019, 73: 33-45.

• Mengyu Rong, Hongbo Shi*, Shuai Tan. Large-scale supervised process monitoring based on distributed modified principal component regression[J]. Industrial & Engineering Chemistry Research, 2019, 58(39): 18223-18240.

• Bing Song, Xinggui Zhou, Shuai Tan, Hongbo Shi*, Bo Zhao, Mengling Wang. Process monitoring via key principal components and local information based weights[J]. IEEE Access, 2019, 7: 15357-15366.

• Majed Aljunaid, Hongbo Shi*, Yang Tao. Quality-related fault detection based on improved independent component regression for non-Gaussian processes[J]. IEEE Access, 2019, 7: 158594-158602.

• Yang Tao, Hongbo Shi*, Bing Song, Shuai Tan. Distributed supervised fault detection and diagnosis for a non-Gaussian process[J]. Industrial & Engineering Chemistry Research, 2019, 58(16): 6592-6603.

• Yang Tao, Hongbo Shi*, Bing Song, Shuai Tan. Parallel supervised additive and multiplicative faults detection for nonlinear process[J]. Journal of the Franklin Institute, 2019, 356(18): 11716-11740.

• Bo Zhao, Hongbo Shi*, Bing Song, Shuai Tan. Quality weakly related fault detection based on weighted Dual-Step feature extraction[J]. IEEE Access, 2019, 7: 7860-7871.

• Jian Yang, Bing Song, Shuai Tan, Hongbo Shi*. Concurrent monitoring of global-local performance indicators for large-scale process[J]. Journal of the Taiwan Institute of Chemical Engineers, 2019, 102: 9-16.

• Mengyu Rong, Hongbo Shi*, Fan Wang, Shuai Tan. Distributed process monitoring framework based on decomposed modified partial least squares[J]. The Canadian Journal of Chemical Engineering, 2019, 97(12): 3087-3100.

• Yang Tao, Hongbo Shi*, Bing Song, Shuai Tan. Operating performance assessment and non‐optimal cause identification for chemical process[J]. The Canadian Journal of Chemical Engineering, 2019, 97: 1475-1487.

• Chao Yang, Wen Yang*, Hongbo Shi. Communication-saving design by stochastic event triggers[J]. Journal of the Franklin Institute, 2019, 356(17): 10532-10546.

• Bing Song, Xinggui Zhou, Hongbo Shi*, Yang Tao. Performance-indicator-oriented concurrent subspace process monitoring method[J]. IEEE Transactions on Industrial Electronics, 2018, 66(7): 5535-5545.

• Bing Song, Hongbo Shi*. Fault detection and classification using quality-supervised double-layer method[J]. IEEE Transactions on Industrial Electronics, 2018, 65(10): 8163-8172.

• Bing Song, Hongbo Shi*. Temporal-spatial global locality projections for multimode process monitoring[J]. IEEE Access, 2018, 6: 9740-9749.

• Jian Yang, Zheng Lv, Hongbo Shi*, Shuai Tan. Performance monitoring method based on balanced partial least square and statistics pattern analysis[J]. ISA transactions, 2018, 81: 121-131.

• Wen Yang*, Chao Yang, Hongbo Shi, Ling Shi, Guanrong Chen. Stochastic link activation for distributed filtering under sensor power constraint[J]. Automatica, 2017, 75: 109-118.

• Chao Yang, Jiangying Zheng, Xiaoqiang Ren, Wen Yang, Hongbo Shi*, Ling Shi. Multi-sensor Kalman filtering with intermittent measurements[J]. IEEE Transactions on Automatic Control, 2017, 63(3): 797-804.

• Jian Yang, Mingshan Zhang, Hongbo Shi*, Shuai Tan. Dynamic learning on the manifold with constrained time information and its application for dynamic process monitoring[J]. Chemometrics and Intelligent Laboratory Systems, 2017, 167: 179-189.

• Chao Yang, Jingyi Liu, Wen Yang, Hongbo Shi*. Sensor scheduling for lifetime maximization in centralized state estimation[J]. Neurocomputing, 2017, 270: 43-53.

• Jian Yang, Hongbo Shi*. Enhanced process monitoring via time–space coordinated-locality preserving projection[J]. International Journal of System Control and Information Processing, 2017, 2(2): 99-112.

• Bing Song, Shuai Tan, Hongbo Shi*. Key principal components with recursive local outlier factor for multimode chemical process monitoring[J]. Journal of Process Control, 2016, 47: 136-149.

• Bing Song, Shuai Tan, Hongbo Shi*. Process monitoring via enhanced neighborhood preserving embedding[J]. Control Engineering Practice, 2016, 50: 48-56.

• Fan Wang, Shuai Tan, Yawei Yang, Hongbo Shi*. Hidden Markov model-based fault detection approach for a multimode process[J]. Industrial & Engineering Chemistry Research, 2016, 55(16): 4613-4621.

• Wen Yang*, Zidong Wang, Zongyu Zuo, Chao Yang, Hongbo Shi. Nodes selection strategy in cooperative tracking problem[J]. Automatica, 2016, 74: 118-125.

• Bing Song, Shuai Tan, Hongbo Shi*. Time–space locality preserving coordination for multimode process monitoring[J]. Chemometrics and Intelligent Laboratory Systems, 2016, 151: 190-200.

• Mengling Wang*, Chenkun Qi, Huaicheng Yan, Hongbo Shi. Hybrid neural network predictor for distributed parameter system based on nonlinear dimension reduction[J]. Neurocomputing, 2016, 171: 1591-1597.

• Fan Wang, HonglinZhu, Shuai Tan, Hongbo Shi*. Orthogonal nonnegative matrix factorization based local hidden Markov model for multimode process monitoring[J]. Chinese Journal of Chemical Engineering, 2016, 24(7): 856-860.

• Mengling Wang*, Joel A. Paulson, Huaicheng Yan, Hongbo Shi. An adaptive model predictive control strategy for nonlinear distributed parameter systems using the type-2 Takagi–Sugeno model[J]. International Journal of Fuzzy Systems, 2016, 18(5): 792-805.

• Wen Yang*, Ying Wang, Xiaofan Wang, Hongbo Shi.. Optimal controlled nodes selection for fast consensus[J]. Asian Journal of Control, 2016, 18(3): 932-944.