IntroductionHuaqing Wang is currently a Professor at the College of Mechanical and Electrical Engineering, Beijing University of Chemical Technology. Research Focus: The primary research focuses on fundamental theoretical methods and key technologies in the domains of signal analysis and characterization, performance assessment and early warning, intelligent fault diagnosis, and life prediction. The research objects include high-end power equipment, such as aviation engines, high-speed trains, and specialized reducers, along with their core components. Professional Experience: 2009.09-, Professor, College of Mechanical and Electrical Engineering, Beijing University of Chemical Technology; 1995.08-2005.09, Assistant and Lecturer, College of Mechanical and Electrical Engineering, Beijing University of Chemical Technology; Education Background: 2005.10-2009.03, Doctor Course of Graduate School of Bioresources, Mie University, Japan; 1999.09-2002.05, Master Course of College of Mechanical and Electrical Engineering, Beijing University of Chemical Technology, China; 1991.09-1995.07, Bachelor Course of College of Mechanical Engineering, Beijing University of Chemical Technology, China. Positions in Academic Organizations and Social Service: Ø Chairman of the Board of Supervisors of China Association of Plant Engineering; Ø Chairman of the Equipment Diagnostic Engineering Committee of China Association of Plant Engineering; Ø Vice Chairman of the Equipment operation and maintenance of the Chinese Mechanical Engineering Society; Ø Member of the Academic Committee of Hunan Provincial Key Laboratory of Health Maintenance for Mechanical Equipment； Ø Member of the Academic Committee of Beijing Key Laboratory of Electromechanical System Measurement and Control； Ø Member of the Technical Committee of Beijing Precision Measurement and Control Technology and Instrument Engineering Technology Research Center; Ø Member of the Academic Committee of Beijing University of Chemical Technology； Ø Editorial Board Member of Journal of Dynamics Monitoring and Diagnostics; Ø Editorial Board Member of Journal of Beijing University of Chemical Technology (Natural Science Edition); Education Work ExperienceSocial PositionSocial ActivitiesResearchIn the past three years, Professor Wang has presided over 1 National Key R&D Program, 2 National Natural Science Foundation of China, and some other projects in the field of equipment failure intelligent diagnosis and prediction. As the first or corresponding author, Prof. Wang has published 20 papers indexed by SCI, including 6 ESI highly cited papers. Prof. Wang has published 2 monographs, 5 invention patents were authorized with Prof. Wang as the first inventor, and 2 software copyrights were approved. Major awards and honors obtained are the second prize of Beijing Municipal Natural Science Award, the second prize of Science and Technology Award of China Society of Vibration Engineering, and the first prize of Science and Technology Progress Award of Petroleum and Chemical Industry Federation of China.
TeachingProf. Wang isa distinguished teacher and an outstanding supervisor for postgraduate students of Beijing University of Chemical Technology. He teaches three undergraduate courses and three postgraduate courses each year, all of which have received excellent course evaluations. Undergraduate Courses: Ø 2020-2023, ‘Mechanics of Materials’ (48 class hours / year, about 90 students / year, mandatory core course). Ø 2020-2023, ‘Statics’ (40 class hours / year, about 90 students/year, mandatory core course). Ø 2020-2023, ‘Preliminary Discussion in Safety Engineering’ (4 class hours / year, about 100 students / year, elective course). Postgraduate Courses: Ø 2020-2023, ‘Equipment Monitoring and Fault Diagnosis’ (24 class hours / year, about 80 master’s students / year, degree course). Ø 2020-2023, ‘Frontiers of Industry Development’ (4 class hours / year, about 50 master’s students / year, specialized elective course). Ø 2020-2023, ‘Progress and Discussions in Power Engineering Discipline’ (6 class hours / year, about 30 doctoral students / year, degree course). PostgraduatesFunding
Vertical ProjectHorizontal ProjectPublications[1] Tianjiao Lin, Liuyang Song*, Lingli Cui, Huaqing Wang*, Advancing RUL Prediction in Mechanical Systems: A Hybrid Deep Learning Approach Utilizing Non-Full Lifecycle Data, Advanced Engineering Informatics. 2024, 61 (2024):102524. [2] Liuyang Song, Pengyuan Hao, Shikuan Zhang, Changkun Han, Huaqing Wang*. A Semi-Supervised GCN Framework for Transfer Diagnosis Crossing Different Machines, IEEE Sensors Journal, 2024, 24(6):8326-8336 [3] Liuyang Song, Ye Jin, Tianjiao Lin, Shengkai Zhao, Zhicheng Wei, Huaqing Wang*. Remaining Useful Life Prediction Method Based on the Spatiotemporal Graph and GCN Nested Parallel Route Model, IEEE Transactions on Instrumentation and Measurement, 2024, 73:3511912 [4] Lingli Cui, Wenjie Li, Dongdong Liu*, Huaqing Wang*, A Novel Robust Dual Unscented Particle Filter Method for Remaining Useful Life Prediction of Rolling Bearings, IEEE Transactions on Instrumentation and Measurement, 73:3509009, 2024 [5] Changkun Han, Wei Lu, Huaqing Wang*, Liuyang Song*, Lingli Cui. Multistate fault diagnosis strategy for bearings based on an improved convolutional sparse coding with priori periodic filter group, Mechanical Systems and Signal Processing, 188 (2023) 109995. [6] L. Cui, W. Li, X. Wang, D. Zhao* and H. Wang*, Comprehensive Remaining Useful Life Prediction for Rolling Element Bearings Based on Time-Varying Particle Filtering, IEEE Transactions on Instrumentation and Measurement, vol. 71, pp. 1-10, 2022, Art no. 3510010[7] Han Changkun, Lu Wei, Wang Pengxin, Song Liuyang*,Wang Huaqing*. A recursive sparse representation strategy for bearing fault diagnosis, Measurement, 187, 110360, 2022. [8] Hongtao Xue∗, Meng Wu, Ziming Zhang, Huaqing Wang*. Intelligent diagnosis of mechanical faults of in-wheel motor based on improved artificial hydrocarbon networks, ISA Transactions. 120 (2022) 360–371, 2022. [9] Hongtao Xue*, Dianyong Ding, Ziming Zhang, Meng Wu, Huaqing Wang. A fuzzy system of operation safety assessment using multi-model linkage and multi-stage collaboration for in-wheel motor, IEEE Transactions on Fuzzy Systems, 2022.30(4):999-1013. [10] Pengxin Wang , Liuyang Song , Xudong Guo, Huaqing Wang* and Lingli Cui*, A High-Stability Diagnosis Model Based on a Multiscale Feature Fusion Convolutional Neural Network, IEEE Transactions on Instrumentation and measurement, 2021,70:3522709[11] Lingli Cui*, Yin Sun, Xin Wang , and Huaqing Wang*. Spectrum-Based, Full-Band Preprocessing, and Two-Dimensional Separation of Bearing and Gear Compound Faults Diagnosis,IEEE Transactions on Instrumentation and measurement,70:3513216,2021. [12] Song L. Y, Wang H.Q*, Chen P*. Automatic patrol and inspection method for machinery diagnosis robot - sound signal based fuzzy search approach. IEEE Sensors Journal, 20(15), 8276-8286[13] Wang H.Q*; Li S.; Song L.Y.; Cui L.L*; Wang P.X. An Enhanced Intelligent Diagnosis Method Based on Multi-Sensor Image Fusion via Improved Deep Learning Network, IEEE Transactions on Instrumentation and measurement, 69(6), 2648-2657, 2020. [14] Lingli Cui*, Xin Wang, Huaqing Wang*, Jianfeng Ma. Research on Remaining Useful Life Prediction of Rolling Element Bearings Based on Time-Varying Kalman Filter, IEEE Transactions on Instrumentation and measurement, 69(6), 2858-2867, June 2020. [15] Wang H.Q*; Ren B.Y.; Song L.Y.; Cui L.L*. A Novel Weighted Sparse Representation Classification Strategy based on Dictionary Learning for Rotating Machinery. IEEE Transactions on Instrumentation and measurement, 69(3):712-720, 2020 [16] Yansong Hao, Liuyang Song, Bangyue Ren, Huaqing Wang* and Lingli Cui*. Step-by-step Compound Faults Diagnosis Method for Equipment Based on Majorization-Minimization and Constraint SCA, IEEE/ASME Transactions on Mechatronics, 24(4): 2477-2487, 2019 Awards
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