Personal profile

Research Interests

  • Predictive Maintenance and Condition Monitoring
  • Intelligent fault diagnosis and remaining useful life prediction
  • Remanufacturing and Refurbishment
  • Digital Twins for Remanufacturing and Predictive Maintenance
  • Machine Learning and Deep Learning
  • Transfer Learning and Few-shot Learning
  • Reinforcement Learning and Imitation Learning
  • Decision Making and Management
  • Robotic

Research Projects/Collaborations


  1. Intelligent fault diagnosis method and system with few-shot learning technique under small sample data condition, Funded by The Efficiency and Performance Engineering Network (TEPEN) International Collaboration Fund Award, Jan. 2023 ~ Jun. 2024, Principal Investigator
  2. Remanufacturing and Refurbishment Large Industrial Equipment, Funded by European Union’s Horizon 2020 Research and Innovation Programme, Oct.2019 ~ Sep.2023, Research Associate


Dr Zhang is currently a Research Associate at the College of Engineering and Physical Sciences, Aston University, UK. He has strong expertise and research profile in the areas of (1) Application of condition monitoring, health diagnosis, and failure prognostic in predictive maintenance systems, (2) advanced machine learning algorithm design with deep learning, transfer learning and few-shot learning. He has participated in 6 abundant research projects, as well as a strong track record in peer-reviewed leading international journals and conferences. He has published more than 25 high-quality papers in the fields of predictive maintenance and machine learning. The highest citation of his single paper has reached 275 in just 6 years’ time as checked on 13th Feb. 2023. Dr Zhang serves as a reviewer in a number of leading international journals, such as “Reliability Engineering & System Safety”, “Engineering Applications of Artificial Intelligence”, “Expert Systems with Applications”, “IEEE Transactions on Instrumentation and Measurement”, etc. He currently serves as an Associate Editor of “IEEE Transactions on Instrumentation and Measurement” and “Digital Manufacturing Technology” and a guest editor in special issue "Artificial Intelligence Explainability (XAI) and Interpretability: Exploring the Potential of XAI in Fault diagnosis and Cyber-Physical Systems" on Sensor, special issue " Smart Manufacturing and Autonomous Systems for Sustainable Development" on Sustainability, and special issue " Mixture of Human and Machine Intelligence in Digital Manufacturing" on Designs.


  • TJ Mechanical engineering and machinery
  • QA75 Electronic computers. Computer science


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