Dr. Zisheng Wang | Industrial Big Data | Best Researcher Award

 Dr, Zisheng Wang,Tsinghua University, China

Dr. Zisheng Wang is affiliated with Tsinghua University in China. His research interests include mechanical engineering, robotics, and automation. Dr. Wang has contributed significantly to the field through his research and publications, focusing on topics such as optimization, robotic design, and advanced manufacturing techniques. He is actively involved in academic activities and has a strong background in both theoretical research and practical applications in engineering.

Professional Profiles:


Education :

Doctorate (Ph.D. in Engineering)School of Mechanical Science and Engineering, Huazhong University of Science and Technology, Wuhan, China (2018-2023),Bachelor’s Degree in Engineering,School of Mechanical Engineering and Automation, Northeastern University, Shenyang, China (2014-2018)


  • Research Assistant,Department of Industrial Engineering, Tsinghua University, Beijing, China (December 2023 – Present)


  • Condition monitoring and fault detection
  • Signal processing
  • Deep reinforcement learning
  • Intelligent maintenance of complex equipment
  • Compound fault recognition
  • Time-frequency transform technology
  • Fault diagnosis of robot arm


  • Shuimu Tsinghua Scholar Project, Tsinghua University, Beijing, China, Grant: 2023SM233 (2024-2025)
  • Postdoctoral Fellowship Program of CPSF, China Postdoctoral Science Foundation, Grant: GZC20240820 (2024-2025)

Research Focus:

  • Autonomous recognition frameworks for compound faults in mechanical equipment
  • Deep reinforcement learning applications in fault recognition and maintenance
  • Multi-label fault recognition using machine learning algorithms
  • Transfer learning methods for fault recognition in different conditions

Publications :

  • An autonomous recognition framework based on reinforced adversarial open set algorithm for compound fault of mechanical equipment. Mechanical Systems and Signal Processing, 2024.
  • Multi-source information fusion deep self-attention reinforcement learning framework for multi-label compound fault recognition. Mechanism and Machine Theory, 2023.
  • Multi-label fault recognition framework using deep reinforcement learning and curriculum learning mechanism. Advanced Engineering Informatics, 2022.
  • A novel semi-supervised generative adversarial network based on the actor-critic algorithm for compound fault recognition. Neural Computing and Applications, 2022.
  • Alternative multi-label imitation learning framework monitoring tool wear and bearing fault under different working conditions. Advanced Engineering Informatics, 2022.
Zisheng Wang | Industrial Big Data | Best Researcher Award

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