Fucai Li | Structural Health Monitoring and Fault Diagnosis | Best Researcher Award

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Dr. Fucai Li | Structural Health Monitoring and Fault Diagnosis | Best Researcher Award

Prof. Fucai Li is a distinguished academic at Shanghai Jiao Tong University 🇨🇳, specializing in vibration and ultrasonic signal processing, structural health monitoring, and intelligent sensor systems 🔍. With extensive global research experience across China 🇨🇳, Japan 🇯🇵, and Australia 🇦🇺, he has significantly contributed to mechanical engineering through innovative diagnostics and smart material systems 🛠️. He has published over 150 peer-reviewed papers and led numerous national and industrial research projects 📚🔬. His work bridges academia and industry, with collaborations involving giants like Bao Steel and Shanghai Electric ⚙️.

Profile

Education 🎓

Prof. Li earned his Ph.D. in Engineering from Xi’an Jiaotong University in 2003 🎓, where he also completed his Bachelor’s degree in Mechanical Engineering in 1998 🏫. His strong academic foundation from one of China’s premier technical universities laid the groundwork for a career focused on high-impact research 🧠. Throughout his education, he developed core expertise in mechanics, signal processing, and automation, setting the stage for innovations in sensor technologies and structural diagnostics 📡🔧.

Experience 👨‍🏫

Prof. Li currently serves as Professor at Shanghai Jiao Tong University (2015–present) 🏫. He was an Associate Professor there from 2009–2015 and held earlier roles including Assistant Professor (2003–2005) 🎓. His international experience includes research fellowships in Japan (JSPS, University of Tokyo, 2007–2009) 🇯🇵 and Australia (University of Sydney, 2005–2007) 🇦🇺. This global exposure enriched his expertise in structural innovation and smart systems 🌐. Across academia, he has shaped future engineers and researchers with cutting-edge knowledge and practical application insights 📘🔍.

Awards & Recognitions 🏅

Prof. Li has secured over 30 research grants from top national bodies like NSFC, Ministry of Science and Technology, and major industries including Bao Steel and Shanghai Electric 💼💡. He is widely recognized for his pioneering work in structural health monitoring and has published more than 150 influential journal papers 📑. His contributions to smart sensor networks and machine diagnostics have earned him national acclaim and trust from key industrial stakeholders 🤝. Through sustained innovation and impact, he has built a legacy of excellence in engineering research and collaboration 🏅.

Research Interests 🔬

Prof. Li’s research targets intelligent diagnostics and health monitoring of mechanical systems 🤖. His focus areas include vibration and ultrasonic signal analysis, fiber optic and piezo-electric sensor networks, and AI-driven fault detection systems 📊📡. He develops smart sensing technologies for predictive maintenance and infrastructure resilience, with applications ranging from aerospace to heavy machinery 🏗️✈️. His work integrates signal processing, materials science, and systems engineering to enable next-gen monitoring solutions. With over 150 publications and extensive funding, his research continues to push the frontier of smart mechanical engineering 🚀.

Publications 

Mingshuna Shun Jiang | Intelligent Sensors and Detection Technology | Best Researcher Award

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Prof. Mingshuna Shun Jiang | Intelligent Sensors and Detection Technology | Best Researcher Award

Mingshun Jiang is a professor at the School of Control Science and Engineering, Shandong University 🎓. He is a doctoral supervisor and a young expert of Mount Taishan Scholars 🌟. He serves as the director of the Shandong Engineering Research Center for Intelligent Sensor and Detection Technology 🔬 and deputy director of the Institute of Intelligent Perception 🏛️. His research primarily focuses on intelligent sensors and detection technologies, with over 20 funded projects, including the National Natural Science Foundation and the National Key R&D Program 🏆. He has authored 60+ high-level academic papers in renowned journals 📑. His innovative contributions aim at monitoring complex structural states in high-end equipment 🚀. With extensive industry collaborations, his work has applications in aerospace, rail transit, and military technology 🛰️🚆.

Profile

Education 🎓

Mingshun Jiang earned his doctoral degree in Control Science and Engineering from Shandong University 🎓. His academic journey focused on developing intelligent sensor systems and detection methodologies 📡. His research expertise was cultivated through interdisciplinary learning, integrating control science, artificial intelligence, and structural health monitoring 🤖. His doctoral research emphasized advanced ultrasonic-guided wave detection and probabilistic diagnostic imaging techniques 🏗️. Jiang’s educational background provided him with expertise in designing smart sensor networks, optimizing detection mechanisms, and enhancing structural health monitoring systems ⚙️. With strong mathematical and engineering foundations, he developed novel algorithms for real-time damage localization and predictive maintenance 📊. His continuous learning and research efforts have been instrumental in bridging technological gaps in aerospace, rail transit, and high-end industrial applications 🚆✈️.

Experience 👨‍🏫

Mingshun Jiang has extensive research and academic experience, currently serving as a professor at Shandong University 🏛️. He has led over 20 major research projects, including the National Natural Science Foundation and National Key R&D Program 🌍. As the director of the Shandong Engineering Research Center, he focuses on intelligent sensor development and detection technologies 🔍. His research has been successfully applied in aerospace, rail transit, and high-end industrial monitoring 🚀🚆. He has supervised numerous doctoral students and collaborated with various enterprises on engineering solutions 🏗️. Jiang has also played a key role in technical verification and real-world applications of his research findings 📡. His leadership in academia and industry-driven research has established him as a leading expert in intelligent perception and structural health monitoring 🏆.

Awards & Recognitions 🏅

Mingshun Jiang has received multiple prestigious recognitions, including being a young expert of Mount Taishan Scholars in Shandong Province 🌟. His work has been supported by national and provincial funding agencies, highlighting his contributions to intelligent sensor technology 🏆. He has been awarded numerous grants under the National Natural Science Foundation and National Key R&D Program 🎖️. Jiang’s research achievements have been recognized through invited talks at leading academic conferences and industry collaborations 🤝. He has served as an executive director of the China Inspection and Testing Society, further solidifying his reputation in the field 🔬. His high-impact publications in top-tier journals have earned him accolades for innovation and research excellence 📑. Jiang continues to receive recognition for his contributions to the monitoring of complex structural states in high-end equipment 🚀.

Research Interests 🔬

Mingshun Jiang’s research focuses on intelligent sensors, structural health monitoring, and detection technology 📡. His work integrates artificial intelligence, probabilistic diagnostic imaging, and ultrasonic-guided wave techniques for real-time damage localization and predictive maintenance 🏗️. Jiang has developed innovative methodologies for monitoring key structural indicators such as boundary loads, damage detection, and component failures 🚆. His research aims to bridge the gap between technological innovation and application in aerospace, rail transit, and industrial monitoring 🛰️. His team has successfully engineered high-end monitoring systems that have undergone technical validation and real-world implementation 🔍. Jiang’s expertise extends to developing smart sensing layers for structural health monitoring, contributing to safer and more efficient industrial systems ⚙️. Through his interdisciplinary research, he continues to advance intelligent perception systems for next-generation monitoring applications 🚀.

Publications 
  • Ruijie Song, Lingyu Sun, Yumeng Gao, Juntao Wei, Chang Peng, Longqing Fan andMingshun Jiang*. Unsupervised temperature-compensated damage localization method based on damage to baseline autoencoder and delay-based probabilistic imaging. Mechanical Systems and Signal Processing, 230: 112649, 2025.
  • Hong Zhang ,Feiyu Teng , Juntao Wei , Shanshan Lv , Lei Zhang , Faye Zhang  and Mingshun Jiang*. Damage Location Method of Pipeline Structure by Ultrasonic Guided Wave Based on Probability Fusion.  IEEE Transactions on Instrumentation and Measurement, 73, 9504914, 2024.
  • . LingyuSun , Juntao Wei , Chang Peng , Wei Hao , Feiyu Teng , Longqing Fan , Lei Zhang , Qingmei Sui  and Mingshun Jiang. Ultrasonic guided wave-based probabilistic diagnostic imaging method with Single-Path-Scattering sparse reconstruction for Multi-Damage detection in composite structures.  Mechanical Systems and Signal Processing, 223, 111858, 2024.
  • XiaoshuQin , Shanshan Lv , Changhang Xu , Jing Xie , Lei Jia , Qingmei Sui  and Mingshun Jiang*. Implications of liquid impurities filled in breaking cracks on nonlinear acoustic modulation response: Mechanisms, phenomena and potential applications.  Mechanical Systems and Signal Processing, 200, 110550, 2023.
  • Shanshan Lv , Juntao Wei  and Mingshun Jiang*. Damage localization method for plate-like composite structure based on valid path optimization and search point matching.  Mechanical Systems and Signal Processing, 182, 109562, 2023.

Partha Sengupta | Structural Health Monitoring | Best Researcher Award

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Dr.Partha Sengupta | Structural Health Monitoring | Best Researcher Award

 

AECOM,India

Profile

Education

He holds a Ph.D. in Civil Engineering from the Indian Institute of Engineering Science and Technology (IIEST), Shibpur (2018–2023), with a perfect CGPA of 10/10. His doctoral research focused on “Finite Element Model Updating of Structures in Bayesian Framework and Enhanced Model Reduction Techniques.” Prior to this, he completed his M.Tech in Civil Engineering from IIEST, Shibpur (2014–2016), earning a CGPA of 9.03/10, with a thesis on the “Application of Ground Penetrating Radar in Concrete Evaluation, Pavement Profile, and Utility Detection.” He obtained his B.Tech in Civil Engineering from West Bengal University of Technology (2010–2014) with a CGPA of 9.02/10.

Professional Experience

Dr. Partha Sengupta conducts research in Structural Health Monitoring, focusing on model updating within a Bayesian framework using an enhanced model reduction technique with incomplete modal and time history response data. His work involves developing an iterative model reduction technique in the frequency domain by eliminating stiffness terms from the transformation equation, effectively mapping the full model and predicting its dynamic responses. The modified equation depends on measured modal responses and invariant mass matrices, eliminating the need for repeated evaluations of stiffness terms typically required in structural health monitoring (SHM) updating algorithms. Furthermore, this model reduction approach is integrated with a sub-structuring scheme, making it applicable to large finite element models. Additionally, Dr. Sengupta has developed an improved Bayesian model updating technique within the Transitional Markov Chain Monte Carlo (TMCMC) framework in the frequency domain, incorporating modifications to enhance the TMCMC algorithm.

AWARDS & ACHIEVEMENTS:

He received the Professor Amiya K. Basu Research Award in Structural Dynamics from the Department of Civil Engineering, IIEST Shibpur, and an additional monthly stipend of ₹10,000 from MHRD in 2022, along with his institute fellowship. He was also awarded the Best Paper Award in “Control and Health Monitoring” at the International Conference on Materials, Mechanics, and Structures (ICMMS 2020) in Kozhikode, India. As a reviewer, he has contributed to the G20 C20 International Conference on Interdisciplinary Approaches in Civil Engineering for Sustainable Development, published by Springer Nature, as well as Engineering Structures and Computer Methods in Applied Mechanics and Engineering, prestigious SCI journals published by Elsevier. Additionally, he received the Ministry of Human Resource and Development (MHRD) Institute Fellowship for pursuing his Ph.D. and M.Tech, having qualified GATE 2014 with a 99 percentile.

Publications