Paper Title Number 2
Your Name, You. (2010). "Paper Title Number 2." Journal 1. 1(2).
CV
Education
- B.S. in Mechanical Engineering and Automation, Beijing University of Chemical Technology, Beijing, China, 2011
- Ph.D in Mechanical Design and Theory, Beijing University of Chemical Technology, Beijing, China, 2017
Work experience
- 2017-2020: Post-doctoral fellow
- Tsinghua University
- Duties included: **
- Supervisor: Professor Bin Liang
- 2020 - ~: Research Associate
- Aston University
- Duties included: **
- Supervisor: Professor Yuchun Xu
Skills
- Skill 1
- Skill 2
- Sub-skill 2.1
- Sub-skill 2.2
- Sub-skill 2.3
- Skill 3
Publications
Dynamic analysis of integrally geared compressors with varying workloads
Ming Zhang, Zhinong Jiang, Jinji Gao, "Dynamic Analysis of Integrally Geared Compressors with Varying Workloads", Shock and Vibration, vol. 2016, Article ID 2594635, 13 pages, 2016. https://doi.org/10.1155/2016/2594635
Research on variational mode decomposition in rolling bearings fault diagnosis of the multistage centrifugal pump
Zhang M, Jiang Z, Feng K. Research on variational mode decomposition in rolling bearings fault diagnosis of the multistage centrifugal pump[J]. Mechanical Systems and Signal Processing, 2017, 93: 460-493.
A Deep Transfer Model With Wasserstein Distance Guided Multi-Adversarial Networks for Bearing Fault Diagnosis Under Different Working Conditions
M. Zhang, D. Wang, W. Lu, J. Yang, Z. Li and B. Liang, "A Deep Transfer Model With Wasserstein Distance Guided Multi-Adversarial Networks for Bearing Fault Diagnosis Under Different Working Conditions," in IEEE Access, vol. 7, pp. 65303-65318, 2019, doi: 10.1109/ACCESS.2019.2916935.
Domain adaptation with multilayer adversarial learning for fault diagnosis of gearbox under multiple operating conditions
M. Zhang, W. Lu, J. Yang, D. Wang and L. Bin, "Domain Adaptation with Multilayer Adversarial Learning for Fault Diagnosis of Gearbox under Multiple Operating Conditions," 2019 Prognostics and System Health Management Conference (PHM-Qingdao), 2019, pp. 1-6, doi: 10.1109/PHM-Qingdao46334.2019.8943056.
Wasserstein distance guided adversarial imitation learning with reward shape exploration
M. Zhang et al., "Wasserstein Distance guided Adversarial Imitation Learning with Reward Shape Exploration," 2020 IEEE 9th Data Driven Control and Learning Systems Conference (DDCLS), 2020, pp. 1165-1170, doi: 10.1109/DDCLS49620.2020.9275169.
Boosting Few-Shot Learning with Task-Adaptive Multi-level Mixed Supervision
Wang, D., Ma, Q., Zhang, M., Zhang, T. (2021). Boosting Few-Shot Learning with Task-Adaptive Multi-level Mixed Supervision. In: Fang, L., Chen, Y., Zhai, G., Wang, J., Wang, R., Dong, W. (eds) Artificial Intelligence. CICAI 2021. Lecture Notes in Computer Science(), vol 13070. Springer, Cham. https://doi.org/10.1007/978-3-030-93049-3_15
A New Implementation of Digital Twins for Fault Diagnosis of Large Industrial Equipment
M. Zhang, N. Amaitik, Y. Xu, et al. A New Implementation of Digital Twins for Fault Diagnosis of Large Industrial Equipment. J Robot Mech Eng. 2021;1: pp 1-7.
Metric-based meta-learning model for few-shot fault diagnosis under multiple limited data conditions
Wang D, Zhang M, Xu Y, et al. Metric-based meta-learning model for few-shot fault diagnosis under multiple limited data conditions[J]. Mechanical Systems and Signal Processing, 2021, 155: 107510..
Remaining Useful Life Estimation for Turbofan Engine with Transformer-based Deep Architecture
Q. Ma, M. Zhang, Y. Xu, J. Song and T. Zhang, "Remaining Useful Life Estimation for Turbofan Engine with Transformer-based Deep Architecture," 2021 26th International Conference on Automation and Computing (ICAC), 2021, pp. 1-6, doi: 10.23919/ICAC50006.2021.9594150.
Resonance Impedance Shaping Control of Hip Robotic Exoskeleton
T. Xue et al., "Resonance Impedance Shaping Control of Hip Robotic Exoskeleton," 2021 IEEE International Conference on Systems, Man, and Cybernetics (SMC), 2021, pp. 888-893, doi: 10.1109/SMC52423.2021.9659270.
Predictive Maintenance for Remanufacturing Based on Hybrid-Driven Remaining Useful Life Prediction
Zhang M, Amaitik N, Wang Z, Xu Y, Maisuradze A, Peschl M, Tzovaras D. Predictive Maintenance for Remanufacturing Based on Hybrid-Driven Remaining Useful Life Prediction. Applied Sciences. 2022; 12(7):3218. https://doi.org/10.3390/app12073218.
Cost Modelling to Support Optimum Selection of Life Extension Strategy for Industrial Equipment in Smart Manufacturing
Amaitik, N., Zhang, M., Wang, Z. et al. Cost Modelling to Support Optimum Selection of Life Extension Strategy for Industrial Equipment in Smart Manufacturing. Circ.Econ.Sust. (2022). https://doi.org/10.1007/s43615-022-00154-0.
Dynamic Scheduling Method for Job-Shop Manufacturing Systems by Deep Reinforcement Learning with Proximal Policy Optimization
Zhang M, Lu Y, Hu Y, Amaitik N, Xu Y. Dynamic Scheduling Method for Job-Shop Manufacturing Systems by Deep Reinforcement Learning with Proximal Policy Optimization. Sustainability. 2022; 14(9):5177. https://doi.org/10.3390/su14095177.
Talks
Tutorial 1 on Relevant Topic in Your Field
Tutorial at UC-Berkeley Institute for Testing Science, Berkeley CA, USA
Talk 2 on Relevant Topic in Your Field
Talk at London School of Testing, London, UK
Conference Proceeding talk 3 on Relevant Topic in Your Field
Conference proceedings talk at Testing Institute of America 2014 Annual Conference, Los Angeles, CA
Domain Adaptation with Multilayer Adversarial Learning for Fault Diagnosis of Gearbox under Multiple Operating Conditions
Conference proceedings talk at 2019 Prognostics and System Health Management Conference (PHM-Qingdao), Qingdao, China
Remaining Useful Life Estimation for Turbofan Engine with Transformer-based Deep Architecture
Conference proceedings talk at 2021 26th International Conference on Automation and Computing (ICAC), Portsmouth, United Kingdom
Meta-learning guided Few-shot Learning Method for Gearbox Fault Diagnosis under Limited Data Conditions
Conference proceedings talk at The Efficiency and Performance Engineering Network 2021 (TEPEN 2021) and the sixth International Conference on Maintenance Engineering (IncoME-VI), Tianjin, China
Teaching
Service and leadership
- Currently signed in to 43 different slack teams