Biography:

Dr. Hu Yang is an Associate Research Fellow and doctoral supervisor at the Hangzhou International Innovation Institute, Beihang University. He received his Ph.D. from Politecnico di Milano, Italy, in 2015. His research focuses on Prognostics and Health Management (PHM) of complex systems, artificial intelligence, and modeling & simulation of equipment systems-of-systems. As principal investigator he has led 18 national and ministerial-level projects, published 30+ papers in leading journals such as Reliability Engineering & System Safety and Mechanical Systems and Signal Processing (1600+ citations, h-index 16), authored 2 monographs, and was selected for the Young Elite Scientists Sponsorship Program of CAST in 2020.

Research Interests:

  • Prognostics and Health Management of complex systems
  • Artificial intelligence and industrial big data
  • Modeling and simulation of equipment systems-of-systems
  • Intelligent operation and maintenance and predictive maintenance decision optimization

Research Projects:

Selected Projects as Principal Investigator

  • 2025.09 – 2026.09　Integrated operations and maintenance control software development for asset systems based on multimodal data, Shanghai Yiliu Technology Co., Ltd., RMB 1.05 million, Task Leader (ranked 1/8)
  • 2025.10 – 2026.06　Development of a reliability testing and validation system for representative products, China Electronic Product Reliability and Environmental Testing Research Institute, RMB 428,000, Executive Project Leader (ranked 1/6)
  • 2023.02 – 2024.12　Research and validation of precision support for new aircraft, RMB 3 million, Task Leader (ranked 1/8)
  • 2023.01 – 2024.12　Research on the standards system for aircraft health management systems, RMB 500,000, Task Leader (ranked 1/7)
  • 2021.03 – 2023.11　Research on next-generation intelligent support systems for aviation equipment, RMB 8 million, Project Leader
  • 2022.05 – 2025.05　Research on key technologies for agile support of aviation equipment, RMB 3 million, Project Leader
  • 2020.03 – 2022.03　Research on situational awareness of support resources and equipment selection models, RMB 3.8 million, Project Leader
  • 2020.03 – 2022.03　Aircraft health management and intelligent support technology, Young Elite Scientists Sponsorship Program by CAST, RMB 450,000, No. YESS20200302
  • 2019.08 – 2021.08　Concept study of military intelligent agents and swarm-intelligence algorithms, Science and Technology Commission Innovation Special Zone Project, RMB 750,000, Project Leader
  • 2018.01 – 2020.12　Self-learning of equipment health indicators and generalized PHM modeling based on deep learning in industrial big-data environments, National Natural Science Foundation of China Young Scientists Fund, RMB 220,000, No. 61703431
  • 2022.09 – 2023.09　Algorithmic models for AI-based equipment maintenance decision-making, Pre-research Rapid Support Project, RMB 210,000

Professional Experience:

  • 2024 – Present　Associate Research Fellow and Doctoral Supervisor, Smart Civil Aviation Science and Technology Innovation Center, Hangzhou International Innovation Institute, Beihang University
  • 2016.12 – 2024　Engineer at a research institute, working on equipment PHM, integrated support, and digital and intelligent support technologies
  • 2015.09 – 2016.10　Research Assistant, School of Engineering, Zurich University of Applied Sciences, Winterthur, Switzerland, working on predictive maintenance and machine-learning-based signal processing

Education:

  • 2012.10 – 2015.11　Ph.D. in Energy Engineering, School of Energy, Politecnico di Milano, Italy, under the supervision of Professor Enrico Zio; recipient of the 2015 Excellent Doctoral Dissertation Award of Politecnico di Milano
  • 2010.09 – 2012.06　M.Sc. in Military Equipment Studies, College of Systems Engineering, National University of Defense Technology, under the supervision of Professor Pengcheng Luo; recipient of the 2014 Outstanding Master's Thesis Award of Hunan Province
  • 2006.09 – 2010.06　B.Eng. in Management Engineering, College of Systems Engineering, National University of Defense Technology

Teaching:

  • Probability & Statistics — International Graduate Students, taught in English
  • Design and Simulation of Aviation System Health Management — Graduate Students
  • Fleet Maintenance and Simulation Experiments — Graduate Students
  • Military Theory — Undergraduate Students

Publications:

Hu, Y / 胡杨 is retained as published; * indicates the corresponding author.

Journal Articles

  1. Zhang, D., Hu, Y*, Zhang, S. & Zhang, Y. Distributed hierarchical reinforcement learning for dynamic maintenance scheduling of large-scale airline fleets. Reliability Engineering & System Safety 256, 112249 (2026). [Link]
  2. Yang, J., Hu, Y*, Yu, Z., Chen, F. & Xu, X. In-depth coordination and extension: Decentralized onboard conflict resolution of UAVs in the low altitude airspace. IEEE Transactions on Intelligent Vehicles 9(1) (2024). [Link]
  3. Liu, Y., Li, X. & Hu, Y*. Differentiable neural architecture search for domain adaptation in fault diagnosis. Mechanical Systems and Signal Processing 202, 110639 (2023). [Link]
  4. Hu, Y*, Miao, X., Si, Y., Pan, E. & Zio, E. Prognostics and health management: A review from the perspectives of design, development and decision. Reliability Engineering & System Safety 217, 108063 (2022). [Link]
  5. Li, X., Zheng, J., Li, M., Ma, W. & Hu, Y*. One-shot neural architecture search for fault diagnosis using vibration signals. Expert Systems with Applications 190, 116027 (2022). [Link]
  6. Li, X., Zheng, J., Li, M., Ma, W. & Hu, Y*. Frequency-domain fusing convolutional neural network: A unified architecture improving effect of domain adaptation for fault diagnosis. Sensors 21(2), 1–26 (2021). [Link]
  7. Hu, Y, Miao, X., Zhang, J., Liu, J.* & Pan, E. Reinforcement learning-driven maintenance strategy: A novel solution for long-term aircraft maintenance decision optimization. Computers & Industrial Engineering 153, 107056 (2021). [Link]
  8. Li, X., Hu, Y*, Zheng, J., Li, M. & Ma, W. Central moment discrepancy based domain adaptation for intelligent bearing fault diagnosis. Neurocomputing 429, 12–24 (2021). [Link]
  9. Liu, J., Hu, Y* & Yang, S. A SVM-based framework for fault detection in high-speed trains. Measurement 172, 108779 (2021). [Link]
  10. Lin, Y. H., Ruan, S. J., Tao, F. & Hu, Y*. Dynamic mode transfer scheduling for degrading standby system considering load-sharing characteristic. IEEE Systems Journal 15(4), 5405–5416 (2021). [Link]
  11. Hu, Y*, Baraldi, P., Di Maio, F., Liu, J. & Zio, E. A method for fault diagnosis in evolving environment using unlabeled data. Proceedings of the Institution of Mechanical Engineers, Part O: Journal of Risk and Reliability 235(1), 33–49 (2021). [Link]
  12. Michau, G., Hu, Y, Palmé, T. & Fink, O.*. Feature learning for fault detection in high-dimensional condition monitoring signals. Proceedings of the Institution of Mechanical Engineers, Part O: Journal of Risk and Reliability 234(1), 104–115 (2020). [Link]
  13. Li, X. D., Hu, Y*, Li, M. T. & Zheng, J. H. Fault diagnostics between different type of components: A transfer learning approach. Applied Soft Computing 86, 105950 (2020). [Link]
  14. Lin, Y., Li, X. & Hu, Y*. Deep diagnostics and prognostics: An integrated hierarchical learning framework in PHM applications. Applied Soft Computing 72, 555–564 (2018). [Link]
  15. Hu, Y*, Baraldi, P., Di Maio, F. & Zio, E. A systematic semi-supervised self-adaptable fault diagnostics approach in an evolving environment. Mechanical Systems and Signal Processing 88, 413–427 (2017). [Link]
  16. Hu, Y, Palmé, T. & Fink, O.*. Fault detection based on signal reconstruction with auto-associative extreme learning machines. Engineering Applications of Artificial Intelligence 57, 105–117 (2017). [Link]
  17. Hu, Y, Baraldi, P., Di Maio, F. & Zio, E.*. Online performance assessment method for a model-based prognostic approach. IEEE Transactions on Reliability 65(2), 718–735 (2016). [Link]
  18. Cremona, M. A., Liu, B., Hu, Y, Bruni, S. & Lewis, R. Predicting railway wheel wear under uncertainty using universal kriging. Reliability Engineering & System Safety 154, 49–59 (2016). [Link]
  19. Hu, Y*, Baraldi, P., Di Maio, F. & Zio, E. A particle filtering and kernel smoothing-based approach for new design component prognostics. Reliability Engineering & System Safety 134, 19–31 (2015). [Link]
  20. Luo, P. & Hu, Y. System risk evolution analysis and critical event identification. Reliability Engineering & System Safety 114, 21–28 (2013). [Link]
  21. Miao, X., Hu, Y*, Qian, Z. & Lu, T. Research on the demonstration of intelligent support systems for aviation equipment. Measurement & Control Technology 39(6) (2020). [In Chinese] [Link]

Conference & Others

  1. Hu, Y, Palmé, T. & Fink, O. Deep health indicator extraction: A method based on auto-encoders and extreme learning machines. Annual Conference of the PHM Society (2016). [Link]
  2. Liu, J., Zio, E. & Hu, Y. Particle filtering for prognostics of a newly designed product with a new parameters initialization strategy. IEEE Access 6 (2018). [Link]

Monographs

  1. Hu Yang et al. Intelligent Prognostics: Equipment Prognostics and Health Management — Lifecycle Interpretation and Practice. Publishing House of Electronics Industry, June 2025, ISBN 978-7-121-24762-0. [In Chinese]
  2. Monograph 2 — bibliographic details to be supplemented.

Invention Patents

  1. Hu Yang, Han Danyang, Chen Xinhang et al. Digital-twin-based method and equipment for generating PHM model training data. Application No. 202511156367.7, September 2025.
  2. Hu Yang, Miao Xuewen, He Qingjie et al. Method and device for determining performance indicators of aircraft prognostics and health management. 202310827847.6, February 2024.
  3. Miao Xuewen, Hu Yang, Lin Min et al. Optimization method for determining test resources for multi-aircraft communication and navigation equipment. 202318005084.8, January 2024.
  4. Miao Xuewen, Hu Yang, Ren Chaoxu et al. ATML-oriented multi-signal-flow modeling and simulation method. 202318005087.1, December 2023.
  5. Miao Xuewen, Hu Yang, Ren Chaoxu et al. ATML-oriented automatic generation method for diagnostic test programs. 202318005086.7, December 2023.

Software Copyrights

  1. Hu Yang, Miao Xuewen, Zhang Jun et al. Aviation Equipment Operational Support Simulation Software. 2021R11L1050664, February 2021.
  2. Hu Yang, Miao Xuewen, Zhang Jun et al. Aviation Equipment Reliability, Maintainability, Supportability, Testability, Safety, and Environmental Adaptability and Integrated Support Data Management System. 2021R11L1051074, February 2021.

Awards & Honors:

  • 2020　Young Elite Scientists Sponsorship Program by CAST
  • 2022　Second Prize, Natural Science Award of the Chinese Society of Aeronautics and Astronautics (ranked 1st)
  • 2019　Second Prize, Military Science and Technology Progress Award (ranked 5th)
  • 2022　Second Prize, Military Science and Technology Progress Award (ranked 8th)
  • 2015　Excellent Doctoral Dissertation, Politecnico di Milano
  • 2014　Outstanding Master's Thesis of Hunan Province

Recruitment & Lab:

The research group recruits master's students, doctoral students, and postdoctoral researchers. Students who are interested in PHM, intelligent operations and maintenance, industrial big data, and complex-system modeling and simulation, and who are diligent and innovative, are warmly encouraged to apply.

Programs at Hangzhou International Innovation Institute

  • Doctoral Supervisor: Safety Science and Engineering (0837; Reliability Systems Engineering)
  • Doctoral Supervisor: Mechanical Engineering (0855); Electronic Information (0854)
  • Doctoral Supervisor: Control Science and Engineering (0811; Industrial Internet and Knowledge-Driven Automation)
  • Master's Supervisor: Mechanical Engineering (0855); Electronic Information (0854)
  • Master's Supervisor: Control Science and Engineering (0811); Low-Altitude Intelligent Transportation Engineering (9904; Low-Altitude Safety Assurance Technology)

Contact: yang_hu@buaa.edu.cn

Student Supervision:

As primary supervisor, Dr. Hu advises professional master's students, dual-degree master's students, and international master's students. As co-supervisor, he jointly advises doctoral students and postdoctoral researchers and supports academic writing and algorithm-framework development. Students are encouraged to think independently, innovate, and uphold academic integrity.

Current and Supervised Graduate Students (Selected)

  • Dongcan Liu — Ph.D. Student in Control Science and Engineering
  • Xinhang Chen, Jun Deng, and Jing Li — Professional Master's Students in Electronic Information
  • Kunlong Huang and Yanyan Wu — Professional Master's Students in Artificial Intelligence / Electronic Information
  • Yongpeng Qi — Professional Master's Student in Transportation
  • Linhan Zhang and Zhihuan Wei — Professional Master's Students in Mechanical Engineering
  • Pedro Martin and Meiyezhe — International Master's Students