PHD at Ocean University of China, China
Prof. Dr. Weihua Li is an esteemed academic leader, serving as the Academic Vice Principal at North China University of Water Resources and Electric Power. With a Ph.D. in Marine Chemistry, she is a Level-2 distinguished professor and a renowned expert in corrosion protection and durability enhancement of infrastructure. Her pioneering research has led to breakthroughs in self-healing coatings and corrosion inhibition technologies, applied in major projects like the Hong Kong-Zhuhai-Macao Bridge. Prof. Li’s exceptional contributions, including over 230 academic papers and 80 patents, have earned her prestigious awards and recognition as a leading figure in marine engineering and corrosion science.
Professional Profiles:
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Education
Ph.D. in Marine Chemistry, Ocean University of China Master’s in Analytical Chemistry, Qingdao University of Science and Technology Bachelor’s in Industrial Analysis, Qingdao Institute of Chemical Technology
Professional Experience
Academic Vice President, North China University of Water Resources and Electric Power Chief Scientist, Institute of the Chemistry of Henan Academy of Sciences Dean, Distinguished Professor, School of Chemical Engineering and Technology, Sun Yat-sen University Visiting Scholar, Technische Universität München, University of Manchester
Major Academic Achievements
Pioneered the theory of “targeted corrosion inhibition” Developed innovative techniques applied in significant infrastructure projects Published 8 monographs and over 230 academic papers Obtained 80 authorized national invention patents and contributed to industrial standards:
Awards & Honors
Recognized by national and provincial awards for outstanding scientific contributions and innovation leadership
Main Research Interests
Corrosion mechanism and theoretical innovation Durability of reinforced concrete structure Self-healing coating and theoretical innovation Corrosion inhibition technology and theoretical innovation Photocathodic protection and theoretical innovation
Research Focus:
Based on the provided publications, Dr. Weihua Li’s research focus primarily revolves around the development and application of advanced machine learning and deep learning techniques for fault diagnosis and condition monitoring of mechanical systems, particularly in the domain of rotary machinery. Her work spans various areas such as multisensor feature fusion, deep transfer learning, state-of-charge estimation of lithium-ion batteries, convolutional neural networks (CNNs), recurrent neural networks (RNNs), and adversarial transfer networks. Dr. Li’s contributions significantly advance the field of intelligent fault diagnosis by addressing challenges in feature extraction, domain adaptation, and compound fault diagnosis, ultimately enhancing the reliability and efficiency of machinery health monitoring systems.
Publications
- A perspective survey on deep transfer learning for fault diagnosis in industrial scenarios: Theories, applications and challenges, cited by: 385, Publication: 2022.
- A deep learning method for bearing fault diagnosis based on cyclic spectral coherence and convolutional neural networks, cited by: 334, Publication: 2020.
- State-of-charge estimation of lithium-ion batteries using LSTM and UKF, cited by: 256, Publication: 2020.
- Domain adversarial transfer network for cross-domain fault diagnosis of rotary machinery, cited by: 190, Publication: 2020.
- A two-stage transfer adversarial network for intelligent fault diagnosis of rotating machinery with multiple new faults, cited by: 136, Publication: 2020.
- Deep semisupervised domain generalization network for rotary machinery fault diagnosis under variable speed, cited by: 135, Publication: 2020.
- A novel weighted adversarial transfer network for partial domain fault diagnosis of machinery, cited by: 123, Publication: 2020.
- Deep adversarial capsule network for compound fault diagnosis of machinery toward multidomain generalization task, cited by: 121, Publication: 2020.
- A deep adversarial transfer learning network for machinery emerging fault detection, cited by: 96, Publication: 2020.
- A robust weight-shared capsule network for intelligent machinery fault diagnosis, cited by: 96, Publication: 2020.
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