Prof. Changyi XU | Fault Diagnosis | Best Researcher Award

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Prof. Changyi XU | Fault Diagnosis | Best Researcher Award

Prof. Changyi XU , Associate professor , Dalian University of Technology, China.

Dr. Changyi Xu is a dedicated Associate Researcher at the School of Control Science and Engineering, Dalian University of Technology, China . With a strong foundation in automation and physics, Dr. Xu brings extensive expertise in digital twins, fault diagnosis, and model-based system engineering (MBSE). His interdisciplinary approach integrates control science, embedded intelligence, and system modeling to address real-world engineering problems in aerospace and automation. He actively contributes to high-impact journals and international conferences while leading innovative research funded by national and provincial agencies.

Professional Profile

Scopus

ORCID

🎓 Education Background

Dr. Xu obtained his Ph.D. in Automation from the Institut National des Sciences Appliquées de Lyon, France in December 2020. Earlier, he earned his Master’s degree in Condensed Matter Physics from the University of Chinese Academy of Sciences in January 2016. His academic journey began with a Bachelor’s degree in Electronic Information Science and Technology from Jilin University in September 2012.

🧑‍🏫 Professional Experience

Since July 2021, Dr. Xu has been serving as an Associate Researcher at Dalian University of Technology, School of Control Science and Engineering. He has led and contributed to multiple government and industry-sponsored projects, including collaborations with the Ministry of Science and Technology, Liaoning Province, Beijing Institute of Control, and private sector firms. His work spans engine control systems, aerospace modeling, and smart platform development.

🏆 Awards and Honors

Dr. Xu has earned several prestigious accolades, including a national first prize and provincial special prize in the 18th “Challenge Cup” Innovation Competition, and a Gold and National Bronze Award at the Internet Innovation Competition for graduate students. He also received the Third Prize in the “Course Ideology and Politics” Teaching Competition at Dalian University of Technology, showcasing his commitment to both research and teaching excellence.

🔬 Research Focus

His core research interests include fault diagnosis of complex control systems, digital twin technologies, and Model-Based System Engineering (MBSE). Dr. Xu actively publishes in top-tier journals and contributes to major conferences, working on topics such as adaptive control, perovskite-based devices, drag-free satellite thrust control, and reinforcement learning in speed control systems. He also serves as a committee member in the Embodied Intelligence Committee of the Chinese Association of Automation.

🧩 Conclusion

Dr. Changyi Xu exemplifies interdisciplinary innovation in the field of control engineering. His advanced research in automation and digital modeling bridges theoretical advancements with real-world applications. Through international education, impactful publications, and consistent academic recognition, he continues to lead groundbreaking efforts in smart system control and intelligent engineering design.

📚 Publication Top Notes

  1. Adaptive Anti-Disturbance Bumpless Transfer Control for Switched Neural Network Systems – IEEE Transactions on Circuits and Systems I, 2024
    Cited by: 5 articles

  2. Micro-Newton scale variable thrust control technique and its noise problem for drag-free satellite platforms: a review – Journal of Zhejiang University – Science A, 2024
    Cited by: 7 articles

  3. A Novel Fused NARX-Driven Digital Twin Model for Aeroengine Gas Path Parameter Prediction – IEEE Transactions on Industrial Informatics, 2024
    Cited by: 3 articles

  4. Domain Adversarial Enhanced Multi-Channel Graph Networks for Aeroengine Gas Path Fault Diagnosis – IECON Proceedings, 2024
    Cited by: 2 articles

  5. Set-Membership State Estimation for 2-D Roesser Systems Based on Zonotope Radius and Its Application – IEEE Transactions on Circuits and Systems II, 2024
    Cited by: 4 articles

  6. Vacuum-Deposited Perovskite LED by Interface Defect Passivation With Better Color Stability – IEEE Photonics Technology Letters, 2024
    Cited by: 3 articles

  7. Control of perovskites crystallization via polymer scaffold towards pure blue Light-emitting diodes – Materials Letters, 2022
    Cited by: 9 articles

 

 

Assoc. Prof. Dr. Ying Zhao | fault diagnose | Best Researcher Award

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Assoc. Prof. Dr. Ying Zhao | fault diagnose | Best Researcher Award

IEEE, Dalian Maritime University, China

Dr. Ying Zhao is a distinguished Chinese academic and researcher in the field of intelligent control and automation, currently serving as an Associate Professor at the Department of Automation, Dalian Maritime University. With an active postdoctoral research role at Dalian University of Technology under the mentorship of Prof. Sun Ximing, he has significantly contributed to the domains of anti-disturbance and switching control systems. His research achievements are deeply rooted in cutting-edge technologies like cyber-physical systems, multi-agent navigation, and aero-engine systems.

Publication Profile

Scopus

🎓 Education Background

Dr. Zhao earned his Ph.D. in Control Theory and Control Engineering from Northeastern University in 2019 under the supervision of Prof. Zhao Jun, an IEEE Fellow. He previously completed his Master’s degree in Applied Mathematics from Liaoning Technical University in 2015 and his Bachelor’s degree in Mathematics and Applied Mathematics from the same university in 2012. His strong academic foundation in both mathematics and control engineering has guided his interdisciplinary research focus.

💼 Professional Experience

Since 2019, Dr. Zhao has been an Associate Professor at Dalian Maritime University, where he also mentors Master’s and PhD students. Concurrently, he has been pursuing postdoctoral research at the Dalian University of Technology under the guidance of Prof. Sun Ximing. His work has bridged both academia and industry through various collaborative and government-funded research projects in aerospace, robotics, and intelligent marine systems.

🏆 Awards and Honors

Dr. Zhao’s excellence in research and teaching has earned him numerous accolades, including the Liaoning Provincial Outstanding Thesis Advisor Award (2022), the Dalian Maritime University Teaching Excellence Award (2021 & 2023), and the prestigious China Postdoctoral International Exchange Fellowship (2020). These awards underscore his commitment to innovation and education in the field of control systems engineering.

🔬 Research Focus

Dr. Zhao’s research is centered on Intelligent Control for Autonomous Systems, focusing on switching and anti-disturbance control, networked cooperative control for unmanned vehicles, and robust cyber-physical applications. His work has led to the development of advanced control strategies that improve the performance and safety of multi-agent systems, unmanned surface vehicles (USVs), and aero-engine systems under complex dynamic environments.

✅ Conclusion

With a prolific research portfolio and a strong presence in both academic and industrial collaborations, Dr. Ying Zhao stands out as a leading figure in the field of control engineering. His work continues to advance intelligent systems, bringing innovation to real-world autonomous and aerospace applications.

📚 Top Research Publications with Highlights

  1. H∞ anti-disturbance event-triggered control for aero-engine systems via switched affine models
    Journal: IEEE Transactions on Aerospace and Electronic Systems, Year: 2024
    Cited by: 3 articles

  2. Time-event-memory triggered switching dynamic positioning for unmanned marine vehicles with mass-switched and dual-source disturbances
    Journal: IEEE Transactions on Intelligent Transportation Systems, Year: 2024, Vol. 25(12): 21210–21220
    Cited by: 5 articles

  3. H∞ anti-disturbance switching control for switched affine systems with its application to turbofan engine model
    Journal: IEEE Transactions on Circuits and Systems II: Express Briefs, Year: 2024
    Cited by: 2 articles

  4. Bumpless tracking switching control for interval type-2 switched positive T-S fuzzy systems
    Journal: IEEE Transactions on Fuzzy Systems, Year: 2024
    Cited by: 4 articles

  5. Dynamic memory event-triggered anti-disturbance control for switched cyber-physical systems and its application to switched RLC circuit
    Journal: IEEE Transactions on Circuits and Systems I, Year: 2024
    Cited by: 1 article

  6. Switching dynamic event-triggered prescribed performance control for underactuated ASVs
    Journal: IEEE Transactions on Vehicular Technology, Year: 2024
    Cited by: 2 articles

  7. Bumpless switching control for aircraft engine control systems with actuator fault based on switched LPV models
    Journal: IEEE Transactions on Industrial Electronics, Year: 2024
    Cited by: 3 articles

  8. Event-triggered adaptive anti-disturbance switching control for switched systems with dynamic neural network disturbance modeling
    Journal: IEEE Transactions on Neural Networks and Learning Systems, Year: 2023
    Cited by: 6 articles

  9. Event-triggered anti-disturbance control design for aeroengine systems via switched models
    Journal: AIAA Journal, Year: 2022, Vol. 60(9): 5448–5461
    Cited by: 9 articles

  10. Rate bumpless transfer control for switched linear systems and its application to aero-engine control design
    Journal: IEEE Transactions on Industrial Electronics, Year: 2020, Vol. 67(6): 4900–4910
    Cited by: 21 articles

 

Juan Tian | Fault Diagnosis | Best Dissertation Award

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Dr. Juan Tian | Fault Diagnosis | Best Dissertation Award

Senior experimentalist, Taiyuan University of Science and Technology, China

Juan Tian is a Senior Experimentalist currently working towards his Ph.D. in Control Science and Engineering at Taiyuan University of Science and Technology, China. His career spans across research and development in intelligent fault diagnosis and prognostics. Specializing in deep learning, transfer learning, and meta-learning, Juan has made significant contributions to industrial fault diagnostics. He has co-authored numerous research papers and actively participated in global academic conferences 🌍. His expertise lies in leveraging advanced machine learning techniques to solve real-world problems in fault diagnosis and health management of machinery ⚙️.

Publication Profile

Education

Juan Tian holds a Bachelor’s degree in Control Science and Engineering from Taiyuan University of Technology, which he completed in 2009. He is currently pursuing his Ph.D. in Control Science and Engineering at Taiyuan University of Science and Technology, China 📚.

Experience

Juan Tian has been actively involved in several research projects focused on fault diagnosis, health management, and predictive maintenance systems. His work is particularly prominent in the field of industrial equipment diagnostics, with ongoing projects funded by the National Natural Science Foundation of China and the Shanxi Provincial government 🛠️. His expertise extends to consultancy and industry projects, including his work on intelligent fault diagnosis for wind turbines 🌬️.

Awards and Honors

Juan Tian has contributed to several pioneering research projects, such as X-ray image segmentation for welding defects and cross-domain fault diagnosis for wind turbines. His publications have garnered international recognition, with his work being cited by leading journals in the field of engineering 🔬. He is also a reviewer for various prestigious journals, underlining his recognition in the academic community 🏅.

Research Focus

Juan Tian’s research focuses on intelligent fault diagnosis and prognostics, utilizing advanced machine learning techniques like deep learning and transfer learning. His work addresses key challenges such as diagnosing rotating machinery with incomplete data, particularly in complex industrial settings. His research has led to innovations in predictive maintenance and fault diagnosis systems for various industries 🧠🔧.

Conclusion

Juan Tian is a rising expert in the field of intelligent fault diagnosis, combining advanced machine learning methods to tackle industrial challenges. His academic and research contributions have shaped the development of practical diagnostic solutions, making him a leading figure in his field 🌟.

Publications

Fault Diagnosis With Robustness and Lightweight Synergy Under Noisy Environment, IEEE Sensors Journal, 2023 (SCI Indexed)

A Review of Rotation Mechanical Fault Diagnosis Research Based on Deep Domain Adaptation, 2023 IEEE Ninth International Conference on Big Data Computing Service and Applications, 2023 (Scopus Indexed)

Multi-sensor Based Graph Convolution Fault Diagnosis Method, 2024 36th Chinese Control and Decision Conference, 2024 (Scopus Indexed)

A Multi-Source Domain Adaptation Method for Bearing Fault Diagnosis with Dynamically Similarity Guidance on Incomplete Data, Actuators, 2025 (SCI Indexed)