Guoqiang Li | Engineering | Innovative Research Award

Published on by China Scientist

Dr. Guoqiang Li | Engineering | Innovative Research Award 

Dr. Guoqiang Li is a Lecturer and Master’s Supervisor at the School of Marine Engineering. He received his Ph.D. in Mechanical Engineering from Huazhong University of Science and Technology, following a Bachelor’s degree from Dalian Maritime University. His research focuses on the reliability analysis, anomaly detection, and intelligent fault diagnosis of offshore electromechanical equipment. He has led several national and provincial research projects and has expertise in industrial big data, AI algorithms, and smart operation platforms. Dr. Li is also a recipient of multiple science and teaching awards and has authored officially published textbooks.

Dr. Guoqiang Li | Jimei University | China

Profile

SCOPUS ID

Education

  • Dr. Guoqiang Li holds a Bachelor’s degree from Dalian Maritime University and earned both his Master’s and Doctoral degrees in Mechanical Engineering from Huazhong University of Science and Technology. His advanced training laid a strong foundation in engineering principles, particularly in the context of mechanical reliability and intelligent systems applied to offshore environments.

Experience

  • Dr. Li is currently serving as a Lecturer and Master’s Supervisor at the School of Marine Engineering. Since joining academia, he has been involved in teaching, mentoring graduate students, and spearheading innovative research. His contributions extend beyond his university role, having participated in national and provincial-level research initiatives and collaborated with major institutions such as Wuhan University of Technology. He has played both principal and collaborative roles in a variety of R&D projects focusing on marine power systems and intelligent control technologies.

Awards and Recognition

  • Dr. Li has received multiple accolades throughout his academic and research journey. These include prestigious Science and Technology Awards, recognition for Teaching Achievements, and the authorship of officially published textbooks. These honors underscore his excellence in both academic instruction and scientific innovation.

Skills and Certifications

  • His core competencies lie in reliability analysis, intelligent fault diagnosis, and predictive maintenance of offshore electromechanical systems. He is proficient in applying industrial big data analytics, artificial intelligence algorithms, and edge-cloud collaborative computing. Dr. Li is also skilled in the development of intelligent operation platforms and industrial internet systems that support real-time monitoring, diagnostics, and equipment self-regulation.

Research Focus

  • Dr. Li’s research centers on the intelligent monitoring and fault management of offshore equipment. He is especially interested in anomaly detection, condition assessment, and data-driven fault prediction. His work integrates cutting-edge technologies such as generative AI, deep reinforcement learning, and multi-source data fusion to enhance the autonomy and intelligence of marine mechanical systems. His goal is to develop systems capable of zero-sample learning, predictive maintenance, and self-healing control in complex maritime environments.

Conclusion

  • Dr. Guoqiang Li is a forward-thinking researcher and educator whose work lies at the intersection of artificial intelligence and marine engineering. With a firm academic grounding and an expanding portfolio of impactful projects, he continues to contribute to the advancement of intelligent fault diagnostics and system automation in offshore industries. His research and innovations are well-positioned to address the growing demand for smart, reliable, and efficient marine technologies.

Publications

  • Zero-sample fault diagnosis of rolling bearings via fault spectrum knowledge and autonomous contrastive learning
    Authors: Guoqiang Li, Meirong Wei, Defeng Wu, Yiwei Cheng, Jun Wu
    Journal: Expert Systems with Applications

  • Wavelet knowledge-driven transformer for intelligent machinery fault detection with zero-fault samples
    Authors: Guoqiang Li, Meirong Wei, Haidong Shao, Pengfei Liang, Chaoqun Duan
    Journal: IEEE Sensors Journal

  • Zero-fault sample wavelet knowledge-driven industrial robot fault detection
    Authors: Guoqiang Li, Meirong Wei, Defeng Wu, et al.
    Journal: Journal of Instrumentation

  • Deep reinforcement learning-based online domain adaptation method for fault diagnosis of rotating machinery
    Authors: Guoqiang Li, Jun Wu, Chao Deng, Xuebing Xu, Xinyu Shao
    Journal: IEEE/ASME Transactions on Mechatronics

  • Convolutional neural network-based Bayesian Gaussian mixture for intelligent fault diagnosis of rotating machinery
    Authors: Guoqiang Li, Jun Wu, Chao Deng, Zuoyi Chen, Xinyu Shao
    Journal: IEEE Transactions on Instrumentation and Measurement

Mr. Wajid Khan | Information Engineering | Best Researcher Award | 1128

Published on by China Scientist

Mr. Wajid Khan | Information Engineering |  Best Researcher Award

Mr. Wajid Khan, Tianjin University , China

Professional Profile

Scopus

OrcID

EARLY ACADEMIC PURSUITS 📘

Wajid Khan holds a bachelor’s degree in electrical engineering. His undergraduate years were marked by a profound passion for the field and a commitment to academic excellence. Engaging in various research activities, Wajid demonstrated his dedication and produced a notable publication that highlights his early contributions to electrical engineering.

PROFESSIONAL ENDEAVORS 💼

Currently, Wajid is pursuing his MS in Electrical Information and Communication Engineering at Tianjin University. Under the expert supervision of Professor Feng Renhai, he is delving deeper into his field, focusing on critical areas such as renewable energy and power system stability. This advanced education and hands-on research are shaping him into a knowledgeable and skilled professional.

 CONTRIBUTIONS AND RESEARCH FOCUS 📚

  • Wajid’s research is centered on cutting-edge topics, particularly renewable energy and power system stability. These areas are vital for sustainable development, and his work is contributing to addressing some of the most pressing challenges in the field. His dedication is evident through his involvement in high-impact research projects, highlighting his commitment to advancing knowledge and technology in electrical engineering.

IMPACT AND INFLUENCE 🌍

Working closely with Professor Feng Renhai has provided Wajid with invaluable experience and insight. This collaboration has allowed him to deepen his understanding of critical issues in electrical engineering and contribute meaningfully to the academic and professional community. His research not only advances theoretical knowledge but also has practical implications for solving real-world problems.

 ACADEMIC CITES 📑

Wajid’s academic journey is marked by significant achievements, including a notable publication during his undergraduate studies. This accomplishment underscores his dedication to research and his ability to contribute valuable insights to the field. His ongoing work continues to build on this foundation, promising further contributions and advancements.

PLEGACY AND FUTURE CONTRIBUTIONS 🌟

As Wajid continues his graduate studies, he is eager to expand his knowledge and make meaningful contributions to the field of electrical engineering. His goal is to drive innovation and have a positive impact on sustainable energy solutions. Wajid’s commitment to pushing the boundaries of knowledge ensures that his legacy will include significant advancements in electrical engineering and contributions to a more sustainable future.

 

NOTABLE PUBLICATIONS

Transient Stability Analysis of Electrical Power Systems using Polynomial Approximation based Galerkin Method

Authors: Li, Z., Khan, W., Wang, J., Wan, C., Feng, R.
Conference: 2023 5th International Conference on Power and Energy Technology, ICPET 2023
Pages: 1235–1240
Year: 2023

A Systematic Novel Implementation on Photovoltaic Power, Wind Energy and Solar Energy Models

Authors: Lei, Y., Khan, W., Wu, Y., Liu, Y., Feng, R.
Journal: Proceedings of SPIE – The International Society for Optical Engineering
Volume: 12594
Issue: N/A
Pages: 125940W
Year: 2023