Personal Profile:
Zeng Ming, male, Han nationality, born in January 1988, holds a doctoral degree (graduated from Hunan University). He is currently an associate professor at the School of Mechanical Engineering and Electronic Information, China University of Geosciences (Wuhan), a member of the Dynamic Testing Committee of the Chinese Society for Vibration Engineering, and a reviewer for international journals such as Knowledge-Based System and Journal of Sound and Vibration. He has published more than 30 academic papers in domestic and international journals and conferences, including 20 papers in SCI indexed journals such as Mechanical Systems and Signal Processing, IEEE Transactions on Industrial Electronics and Knowledge-Based Systems.
Contact Information:
Email: zengming@cug.edu.cn
Office: 431 No. 2 Teaching Building
Main Research Fields:
Interpretable neural network; deep learning (such as graph neural networks); equipment fault diagnosis and intelligent maintenance; remaining service life prediction for mechanical equipment, new energy batteries, etc; edge computing (cloud edge collaboration); dynamic signal processing and analysis.
Welcome students interested in the above research directions to apply for master program.
Main Academic Achievements:
Representative papers published as first or corresponding author are listed below:
[1] Zeng Ming, Wang Hao, Cheng Yiwei, Wei Jianyu. A Compound Fault Diagnosis Model for Gearboxes Using Correlation Information Between Single Faults, Measurement Science and Technology, 2024, 35(3): 036202. (DOI: 10.1088/1361-6501/ad1312)
[2] Rao Fengpei, Zeng Ming, Cheng Yiwei, A Novel Interpretable Model via Algorithm Unrolling for Intelligent Fault Diagnosis of Machinery, IEEE Sensors Journal, 2024, 24(1). (DOI: 10.1109/JSEN.2023.3332755)
[3] Chen Xiao, Zeng Ming. Convolution-Graph Attention Network With Sensor Embeddings for Remaining Useful Life Prediction of Turbofan Engines, IEEE Sensors Journal, 2023, 23(14): 15786-15794. (DOI: 10.1109/JSEN.2023.3279365)
[4] Zeng Ming, Wu Feng, Cheng Yiwei. Remaining Useful Life Prediction via Spatio-Temporal Channels and Transformer, IEEE Sensors Journal, 2023, 23(23): 29176-29185. (DOI: 10.1109/JSEN.2023.3324330)
[5] Zeng Ming, Chen Zhen. SOSO Boosting of the K-SVD denoising algorithm for enhancing fault-induced impulse responses of rolling element bearings, IEEE Transactions on Industrial Electronics, 2020, 67(2): 1282- 1292.
[6] Zeng Ming, Zhang Weimin, Chen Zhen. Group-based K-SVD denoising for bearing fault diagnosis. IEEE Sensors Journal, 2019, 19( 15): 6335-6343.
[7] Zeng Ming, Yang Yu, Zheng Jinde, Cheng Junsheng. Maximum margin classification based on flexible convex hulls for fault diagnosis of roller bearings. Mechanical Systems and Signal Processing, 2016, 66-67: 533-545.
[8] Zeng Ming, Yang Yu, Luo Songrong, Cheng Junsheng. One-class classification based on the convex hull for bearing fault detection. Mechanical Systems and Signal
[9] Processing, 2016, 81: 274-293.
[10] Zeng Ming, Yang Yu, Zheng Jinde, Cheng Junsheng. Normalized complex Teager energy operator demodulation method and its application to fault diagnosis in a rubbing rotor system. Mechanical Systems and Signal Processing, 2015, 50-51: 380-399.
The main research projects as the principal investigator or participant are as follows:
[1] Shenzhen Science and Technology Innovation Commission, Free Exploratory Basic Research Program, 2021Szvup157, “Research on intelligent fault diagnosis of spindle rotary system of top drive drilling rig based on information interaction network”, 2021/07- 2024/06, PI
[2] National Natural Science Foundation of China, Youth Fund Program, 51705483, “Discriminative sparse decomposition and its application in mechanical fault feature extraction”, 2018/01- 2020/12, PI
[3] Ministry of Science and Technology, Subproject of National Key Research and Development Program, 2018YFC0603402, “Key technology and equipment development of 5000-meter geological core drilling machine”, 2018/07- 2021/06 , PI
[4] Fundamental Research Funds for the Central Universities, CUG170631, “ Research on the mechanical fault feature extraction method based on sparse decomposition”, 2016/07-2019/07, PI
