Lecturer at Tarim University, China
Mingsheng Wang is a Lecturer at the College of Mechanical Electrification Engineering, Tarim University, China. His research primarily focuses on motor fault diagnosis, with expertise in vibration noise analysis, finite element modeling, and deep learning applications. He holds a Ph.D. in Mechanical Engineering from the prestigious Beijing Institute of Technology, where he developed innovative methodologies for fault diagnosis in permanent magnet synchronous motors (PMSMs). Mingsheng has contributed to national engineering projects and authored impactful publications in journals like IEEE Transactions on Power Electronics and Sensors. With advanced technical skills in tools like Matlab/Simulink, Maxwell, and LabView, he has been instrumental in building motor test benches and implementing fault diagnosis algorithms. His work aligns with advancing the reliability of electric motors, making significant contributions to the development of fault detection technologies in electric vehicles.
Professional Profile
Education
Mingsheng Wang completed his Ph.D. in Mechanical Engineering from Beijing Institute of Technology in 2024. His doctoral studies emphasized motor fault diagnostics and the application of deep learning in fault detection. He earned his Master’s degree in Agricultural Mechanization Engineering from Hebei Agricultural University in 2015, where he developed foundational expertise in agricultural mechanical systems. Additionally, he holds a Bachelor’s degree in Measurement and Control Technology and Instrument from the same institution, awarded in 2012. Throughout his academic journey, Mingsheng honed his technical and research skills, building a solid foundation in diagnostics, multi-physics field co-simulations, and reliability engineering.
Professional Experience
Mingsheng Wang has recently joined Tarim University as a Lecturer in the College of Mechanical Electrification Engineering. His role involves teaching and research in advanced motor fault diagnosis and electrification technologies. He has extensive research experience from his Ph.D. program, contributing to nationally significant projects such as the development of motor fault diagnosis systems and vibration noise analysis for silicon carbide systems. His prior work also includes evaluating reliability technologies for integrated controllers and studying the thermal performance of motor controllers. Mingsheng’s expertise spans practical and theoretical domains, where he has contributed to designing motor test benches, implementing data acquisition systems, and validating algorithms for intelligent fault diagnosis.
Research Interests
Mingsheng Wang’s research focuses on motor fault diagnosis, particularly in permanent magnet synchronous motors (PMSMs). His interests include vibration noise analysis, multi-physics field co-simulations, and the application of deep learning techniques in diagnostics. He has worked extensively on fault detection in electric motors, including bearing fault diagnosis and inter-turn short circuit faults, with applications in electric vehicles and advanced mechanical systems. His research aligns with enhancing motor reliability and optimizing system performance, addressing critical challenges in energy efficiency and system reliability. His recent projects delve into coupling fault information with motor vibration and current signals to develop intelligent diagnostic solutions.
Research Skills
Mingsheng Wang is skilled in designing and debugging motor fault test benches and building robust data acquisition systems. He has advanced expertise in finite element modeling, multi-physics field co-simulations, and deep learning applications in fault diagnostics. Proficient in tools like Matlab/Simulink, Maxwell, and LabView, he excels in analyzing co-simulation models and applying advanced algorithms such as convolutional neural networks and transfer learning for fault detection. His skills extend to vibration and noise signal processing, system pre-processing, and experimental setup, making him adept at bridging the gap between theoretical research and practical implementation.
Awards and Honors
While Mingsheng Wang’s CV does not explicitly mention awards, his achievements include significant contributions to national research projects and publishing in high-impact journals. His work in motor fault diagnosis, particularly his innovative approaches using deep learning and multi-physics analysis, has been well-recognized within academic circles. Publications in journals like Sensors and IEEE Transactions on Power Electronics highlight his research excellence and innovative contributions to the field of mechanical engineering and diagnostics. These accomplishments underline his standing as an emerging researcher poised for recognition in his field.
Conclusion
Mingsheng Wang is a strong candidate for the Best Researcher Award, particularly in the category of emerging researchers with significant contributions to motor fault diagnosis and reliability engineering. His technical skills, impactful research projects, and publications in high-ranking journals establish his excellence in the field. However, to further solidify his claim to this award, he could work on gaining more professional experience, building a broader research profile, and enhancing his international collaborations and outreach efforts. In conclusion, he is highly suitable for recognition in his research niche and could be an excellent recipient of this award, especially as an early-career researcher poised to make significant future contributions.
Publication Top Notes
- Intelligent Fault Diagnosis of Inter-Turn Short Circuit Faults in PMSMs for Agricultural Machinery Based on Data Fusion and Bayesian Optimization
Authors: Mingsheng Wang, Wuxuan Lai, Hong Zhang, 扬 刘 (Yang Liu), Qiang Song
Year: 2024