Hua Li | Modern Signal Processing | Best Researcher Award

Published on by Shen Awards

Prof. Hua Li | Modern Signal Processing | Best Researcher Award

Subject Leader at Guizhou University, China.

Hua Li, an associate professor and Ph.D. in Engineering, is currently a postdoctoral fellow at Tsinghua University. With expertise in mechanical system health monitoring, modern signal processing, and big data fault diagnosis, he contributes significantly to the field. Serving as a peer review expert for prestigious journals and holding memberships in esteemed professional committees, Hua Li’s contributions are widely recognized. He has published over 20 academic papers and holds multiple invention patents, reflecting his commitment to innovation. Engaging in numerous national and provincial research projects, including those funded by the National Natural Science Foundation of China, he continues to push the boundaries of knowledge in his field. Hua Li’s extensive skill set in spatial computing, data analysis, modeling, and rendering further enhances his research capabilities, making him a valuable asset to the scientific community.

Professional Profiles:

Education:

Hua Li pursued his Ph.D. in Engineering and currently holds the position of associate professor. He has a diverse educational background, having engaged in advanced studies in mechanical engineering and related fields.

Research Experience:

Hua Li has extensive research experience in the field of mechanical engineering, particularly focusing on intelligent health monitoring and maintenance of mechanical systems, modern signal processing, and structural health monitoring. He has actively contributed to various research projects and academic endeavors, demonstrating his expertise and commitment to advancing knowledge in his field.

Research Interest:

Hua Li is an accomplished researcher with a strong focus on intelligent health monitoring and maintenance of mechanical systems. With extensive expertise in modern signal processing techniques, Hua’s work revolves around advancing fault diagnosis and prognosis methodologies. Additionally, he is deeply engaged in structural health monitoring, contributing significantly to ensuring the integrity and safety of various engineering structures. Leveraging advanced data analytics and machine learning algorithms, he strives to enhance condition monitoring and predictive maintenance practices. Through his innovative methodologies, Hua aims to improve the reliability and efficiency of mechanical systems, making valuable contributions to the field of engineering.

Award and Honors:

Hua Li has garnered recognition for his outstanding contributions to the field of engineering. His dedication and expertise have earned him prestigious awards and honors, including the Best Poster Award at the Transportation Research Board (TRB) in 2024. Additionally, he was honored with the Dr. and Mrs. Milton Leong Graduate Student Award for the 2023-24 academic year, acknowledging his exceptional performance as a graduate student in the Faculty of Science. These accolades underscore Hua’s commitment to excellence and his significant impact on the advancement of research in mechanical engineering and related disciplines.

Research Skills:

Hua Li possesses a diverse range of research skills that enable him to excel in his field. Proficient in spatial computing, he is adept at utilizing tools such as ArcGIS, PostgreSQL, and spatial syntax to analyze complex data related to mechanical systems and structural health monitoring. Additionally, his expertise in data analysis extends to Python, SPSS, and Excel, allowing him to extract valuable insights from research findings. Hua is skilled in modeling using software such as SketchUp and CAD, with a solid understanding of Rhino and Revit. Furthermore, he is proficient in rendering using various software like Photoshop, Illustrator, and Enscape, complemented by his familiarity with Premiere and Lumion. With his comprehensive skill set, Hua Li demonstrates a strong foundation in research methodology and data analysis, essential for conducting impactful research in engineering and related disciplines.

Publications:

  1. An optimized VMD method and its applications in bearing fault diagnosis
    • Authors: H. Li, T. Liu, X. Wu, Q. Chen
    • Year: 2020
    • Citations: 177
    • Journal: Measurement, 166, 108185
  2. Research on bearing fault feature extraction based on singular value decomposition and optimized frequency band entropy
    • Authors: H. Li, T. Liu, X. Wu, Q. Chen
    • Year: 2019
    • Citations: 120
    • Journal: Mechanical Systems and Signal Processing, 118, 477-502
  3. Application of EEMD and improved frequency band entropy in bearing fault feature extraction
    • Authors: H. Li, T. Liu, X. Wu, Q. Chen
    • Year: 2019
    • Citations: 99
    • Journal: ISA transactions, 88, 170-185
  4. A bearing fault diagnosis method based on enhanced singular value decomposition
    • Authors: H. Li, T. Liu, X. Wu, Q. Chen
    • Year: 2021
    • Citations: 82
    • Journal: IEEE Transactions on Industrial Informatics, 17 (5), 3220-3230
  5. Enhanced frequency band entropy method for fault feature extraction of rolling element bearings
    • Authors: H. Li, T. Liu, X. Wu, Q. Chen
    • Year: 2020
    • Citations: 54
    • Journal: IEEE Transactions on Industrial Informatics, 16 (9), 5780-5791
  6. Composite fault diagnosis for rolling bearing based on parameter-optimized VMD
    • Authors: H. Li, X. Wu, T. Liu, S. Li, B. Zhang, G. Zhou, T. Huang
    • Year: 2022
    • Citations: 41
    • Journal: Measurement, 201, 111637
  7. Research on test bench bearing fault diagnosis of improved EEMD based on improved adaptive resonance technology
    • Authors: H. Li, T. Liu, X. Wu, S. Li
    • Year: 2021
    • Citations: 40
    • Journal: Measurement, 185, 109986
  8. A weak fault feature extraction of rolling element bearing based on attenuated cosine dictionaries and sparse feature sign search
    • Authors: H. Zhou, H. Li, T. Liu, Q. Chen
    • Year: 2020
    • Citations: 36
    • Journal: ISA transactions, 97, 143-154
  9. Application of optimized variational mode decomposition based on kurtosis and resonance frequency in bearing fault feature extraction
    • Authors: H. Li, T. Liu, X. Wu, Q. Chen
    • Year: 2020
    • Citations: 34
    • Journal: Transactions of the Institute of Measurement and Control, 42 (3), 518-527
  10. Correlated SVD and its application in bearing fault diagnosis
    • Authors: H. Li, T. Liu, X. Wu, S. Li
    • Year: 2023
    • Citations: 18
    • Journal: IEEE Transactions on Neural Networks and Learning Systems, 34 (1), 355-365