Assistant Professor at Nanjing Institute of Technology, China
Wang Xiang, is a distinguished researcher and master’s supervisor at the School of Energy and Power Engineering, Nanjing Institute of Engineering. He has over 20 years of expertise in mechanical vibration faults, fault diagnosis technology, and the development of vibration monitoring systems. Since 2007, he has been committed to teaching and research in the areas of steam turbines and digital electro-hydraulic control systems. His extensive practical and theoretical knowledge has benefited industries like power, chemical, and metallurgy through industry-university research collaborations. Wang has led over 50 research projects and published more than 20 academic papers, 9 of which are indexed in SCI and EI. His work has contributed to solving complex mechanical issues and ensuring the safe operation of key industrial equipment. He has been instrumental in developing advanced mechanical fault diagnosis systems and is well-regarded for his contributions to both academia and industry.
Professional Profile:
Education๐ย
Wang Xiang holds a Ph.D. in Engineering from Hohai University, which he completed in 2015, following a Master’s degree in Engineering from Southeast University, obtained in 2007. His educational background has provided him with a solid foundation in the principles of mechanical and electrical engineering, with a focus on fault diagnosis technology and mechanical vibration monitoring. At both institutions, he gained significant exposure to cutting-edge research in engineering, contributing to his profound understanding of complex mechanical systems and the practical applications of theoretical research. His academic training has been instrumental in shaping his career, enabling him to lead significant projects in mechanical fault diagnosis and vibration analysis. His research skills have been enhanced by his studies in advanced mechanical systems, ultimately leading him to his current teaching and research role at Nanjing Institute of Engineering.
Professional Experience ๐ผ
Wang Xiang has been serving as a faculty member at the School of Energy and Power Engineering, Nanjing Institute of Engineering since 2007. Over the past two decades, he has built a robust career in teaching, research, and industry collaboration, specializing in mechanical vibration faults and fault diagnosis systems for rotating machinery. His hands-on experience spans over 50 research projects and more than 20 power plants, including collaborations with renowned power generation companies such as Huaneng, Datang, and China Power Investment Corporation. His work involves fault diagnosis, system development, and on-site technical services for key industrial machinery, ensuring their optimal performance and safety. Wang has also contributed extensively to training technicians in thermal power plants, combining practical engineering solutions with academic research. He has led the implementation of advanced diagnostic tools like Emerson CSI systems and Bently DAIU-408 testers, enhancing the capabilities of mechanical health monitoring systems.
Researchย Interest๐
Wang Xiang’s research interests lie primarily in the areas of mechanical vibration faults, fault diagnosis technologies, and vibration monitoring systems for rotating machinery. His work focuses on the early detection and diagnosis of mechanical anomalies, with a particular emphasis on frequency spectrum analysis. He has made significant contributions to the development of advanced diagnostic tools, including mechanical health expert systems and multifunctional fault simulation test benches. His research aims to improve the reliability and safety of key industrial equipment by providing effective solutions for monitoring and diagnosing mechanical faults. Additionally, Wang’s research involves industry-university collaborations, where his practical engineering experience is combined with theoretical advancements to solve real-world challenges in sectors such as power generation, chemical processing, and metallurgy. His work continues to push the boundaries of mechanical fault diagnosis, focusing on enhancing system efficiency and ensuring the operational integrity of critical industrial systems.
Award and Honor
Wang Xiang has earned several accolades throughout his career, recognizing his contributions to both academia and industry. He has been honored with funding for over 50 research projects, with a total funding amount exceeding 10 million yuan, demonstrating his success in securing competitive research grants. His leadership in these projects has been acknowledged by national and provincial bodies, showcasing his ability to combine practical engineering expertise with advanced research. Wangโs academic work, especially his publications in prestigious journals indexed by SCI and EI, has also garnered attention, positioning him as a key figure in the field of mechanical fault diagnosis. His collaborations with major power generation companies have further cemented his reputation as an expert in mechanical vibration monitoring and fault diagnosis, making his contributions invaluable to both industrial operations and academic progress.
Conclusion
Wang Xiang’s impressive track record in research, teaching, and practical applications makes him a strong contender for the Best Researcher Award. His depth of experience in mechanical vibration faults and fault diagnosis, coupled with substantial funding and publication achievements, positions him as a leader in his field domestically. By expanding international collaborations and interdisciplinary research efforts, Wang Xiang could further elevate his profile and impact in the global research community.
Publications top noted๐
- Wang, X., Du, Y.
(2024). Fault Diagnosis of Wind Turbine Gearbox Based on Modified Hierarchical Fluctuation Dispersion Entropy of Tan-Sigmoid Mapping. Entropy, 26(6), 507.
Citations: 1
- Wang, X., Du, Y.
(2024). Fault Diagnosis Method for Wind Turbine Gearbox Based on Ensemble-Refined Composite Multiscale Fluctuation-Based Reverse Dispersion Entropy. Entropy, 26(8), 705.
Citations: 0
- Wang, X., Xu, W.
(2024). A Decoupled Approach to Enhance the Elrod Algorithm. Proceedings of the Institution of Mechanical Engineers, Part J: Journal of Engineering Tribology, 238(1), 86โ95.
Citations: 1
- Xu, W., Wang, X., Huang, T., Yang, J.
(2023). Efficient Implementation of Numerical Methods for Solving Bearing Cavitation Problems Using Symmetric System Solvers. Tribology International, 186, 108624.
Citations: 3
- Wang, X., Jiang, H.
(2023). Gearbox Fault Diagnosis Based on Refined Time-Shift Multiscale Reverse Dispersion Entropy and Optimised Support Vector Machine. Machines, 11(6), 646.
Citations: 2
- Wang, X., Wang, J., Xu, W.
(2022). Fault Diagnosis Method of Wind Turbine Gearbox Based on S-SLLE | ๅบไบS-SLLE็้ฃ็ตๆบ็ป้ฝฟ่ฝฎ็ฎฑๆ
้่ฏๆญๆนๆณ็ ็ฉถ. Taiyangneng Xuebao/Acta Energiae Solaris Sinica, 43(3), 343โ349.
Citations: 3
- Song, G., Zhao, S., Wang, X., Wang, H., Xiao, J.
(2022). An Efficient Biomass and Renewable Power-to-Gas Process Integrating Electrical Heating Gasification. Case Studies in Thermal Engineering, 30, 101735.
Citations: 7
- Wang, J., Wang, J., Bi, X., Wang, X.
(2016). Performance Simulation Comparison for Parabolic Trough Solar Collectors in China. International Journal of Photoenergy, 2016, 9260943.
Citations: 24
- Wang, X., Zheng, Y., Zhao, Z., Wang, J.
(2015). Bearing Fault Diagnosis Based on Statistical Locally Linear Embedding. Sensors (Switzerland), 15(7), 16225โ16247.
Citations: 132
