Xi’an University of Posts and Telecommunications, China
Author Profile
Scopus
🎓 Early Academic Pursuits
Youming Wang’s journey in academia began with a strong foundation in mechanical engineering, earning his Master of Science degree from Xi’an University of Technology in 2006. His early academic endeavors reflected a growing curiosity about mechanical systems, particularly how they behave under stress, wear, and fluctuating environmental conditions. Driven by a desire to delve deeper into the intricacies of engineering systems, Wang continued his academic journey at Xi’an Jiaotong University, one of China’s most prestigious institutions, where he obtained his Ph.D. in Mechanical Engineering in 2010.
During his formative academic years, Wang developed a keen interest in the dynamic behavior of mechanical structures. His research during this period focused on laying a solid theoretical foundation in fault detection, signal processing, and vibration analysis—areas that would later define the trajectory of his career. His doctoral studies equipped him not only with rigorous technical expertise but also with a vision to address real-world mechanical challenges using cutting-edge engineering principles.
🛠️ Professional Endeavors
Following the completion of his Ph.D., Professor Youming Wang began his academic and research career at the Xi’an University of Posts and Telecommunications, where he currently serves as a full professor. In this role, he has been instrumental in shaping both the academic curriculum and research direction within the department of mechanical and electrical engineering.
His professional life is marked by a balance of scholarly excellence and practical engineering application. At the university, Wang has mentored numerous graduate students and led several projects, bridging the gap between theoretical research and industry needs. His role also includes collaboration with industrial partners to apply research in fault detection and vibration control to complex engineering systems in telecommunications and manufacturing sectors.
🔬 Contributions and Research Focus
Professor Wang’s research is centered on fault diagnosis, vibration control, and structural health management—fields that are crucial to the reliability and safety of modern mechanical systems. His work involves the development of diagnostic algorithms and predictive maintenance tools that allow engineers to detect faults in complex machinery before catastrophic failures occur.
By integrating advanced signal processing techniques, artificial intelligence, and sensor-based monitoring, Wang has contributed to the evolution of smart maintenance systems. These systems are especially important in industries where downtime due to mechanical failure can lead to significant financial losses or safety risks. His research has helped establish frameworks for real-time structural monitoring, ensuring early detection of issues in bridges, turbines, and high-precision equipment.
🏅 Accolades and Recognition
Over the years, Professor Wang has received widespread recognition for his contributions to mechanical engineering and applied diagnostics. Notably, he is a Fellow of the Chinese Communications Society, reflecting his interdisciplinary engagement with engineering and communications technologies. He is also a Director of the Fault Diagnosis Branch of the Chinese Society of Vibration Engineering, a role that underscores his leadership in advancing the field at the national level.
These distinctions highlight Wang’s professional esteem and validate the significance of his contributions to both academia and industry. His leadership roles also position him as a key figure in setting national research agendas and fostering innovation in structural health technologies.
🌏 Impact and Influence
The impact of Professor Wang’s work extends far beyond the university classroom or laboratory. His research has direct applications in several high-stakes industries, including transportation, energy, aerospace, and civil infrastructure. By enabling early fault detection and precision control, his methods help reduce maintenance costs, enhance operational safety, and extend the life span of critical assets.
Wang’s publications have influenced scholars and practitioners across the globe, contributing to a growing body of literature that blends traditional mechanical engineering with emerging digital tools such as machine learning and IoT-enabled diagnostics. His role in mentoring young researchers has also ensured the continuity and evolution of research in this vital area.
🌟 Legacy and Future Contributions
As Professor Youming Wang continues to advance in his career, his focus remains firmly on innovation and interdisciplinary integration. With the growing emphasis on smart cities, cyber-physical systems, and sustainable infrastructure, his expertise in fault diagnosis and structural health monitoring positions him to make lasting contributions to these emerging areas.
Looking ahead, Wang is likely to expand his work into AI-driven fault prediction systems, collaborative robotic diagnostics, and the development of integrated health management frameworks for next-generation infrastructure. His blend of academic rigor, technical proficiency, and visionary leadership ensures that his legacy will be marked by continued influence on both the engineering community and society at large.
📝Notable Publications
An extended iterative filtering and composite multiscale fractional-order Boltzmann-Shannon interaction entropy for rolling bearing fault diagnosis
Author(s): Y. Wang (Youming), X. Wang (Xianzhi), G. Chen (Gaige)
Journal: Applied Acoustics
Year: 2025
Res-TCEANet: An expansive attention mechanism with positional correspondence based on semi-supervised temporal convolutional network for RUL estimation
Author(s): Y. Wang (Youming), Y. Huang (Yirun)
Journal: Measurement: Journal of the International Measurement Confederation
Year: 2025
A fault diagnosis method of rolling bearings based on masking differential iterative filtering and Euclidean mixed entropy
Author(s): Y. Wang (Youming), X. Yang (Xing), S. Jiao (Shiting)
Journal: Measurement Science and Technology
Year: 2025
A Multilevel Attitude-Aware Denoising Network for Bearing Fault Diagnosis
Author(s): Y. Wang (Youming), Y. Kang (Yezi), Y. Huang (Yirun)
Journal: IEEE Transactions on Industrial Informatics
Year: 2025
A Fault Diagnosis Model Based on IFSS-CK-Means Algorithm for the Bolted Structure
Author(s): X. Yang (Xingdong), Y. Wang (Youming), X. Gao (Xiang), J. Tan (Jiyong)
Journal: Conference Paper
Year: (Not specified, assumed 2025 based on others)