Senior experimentalist, Taiyuan University of Science and Technology, China
Juan Tian is a Senior Experimentalist currently working towards his Ph.D. in Control Science and Engineering at Taiyuan University of Science and Technology, China. His career spans across research and development in intelligent fault diagnosis and prognostics. Specializing in deep learning, transfer learning, and meta-learning, Juan has made significant contributions to industrial fault diagnostics. He has co-authored numerous research papers and actively participated in global academic conferences 🌍. His expertise lies in leveraging advanced machine learning techniques to solve real-world problems in fault diagnosis and health management of machinery ⚙️.
Publication Profile
Education
Juan Tian holds a Bachelor’s degree in Control Science and Engineering from Taiyuan University of Technology, which he completed in 2009. He is currently pursuing his Ph.D. in Control Science and Engineering at Taiyuan University of Science and Technology, China 📚.
Experience
Juan Tian has been actively involved in several research projects focused on fault diagnosis, health management, and predictive maintenance systems. His work is particularly prominent in the field of industrial equipment diagnostics, with ongoing projects funded by the National Natural Science Foundation of China and the Shanxi Provincial government 🛠️. His expertise extends to consultancy and industry projects, including his work on intelligent fault diagnosis for wind turbines 🌬️.
Awards and Honors
Juan Tian has contributed to several pioneering research projects, such as X-ray image segmentation for welding defects and cross-domain fault diagnosis for wind turbines. His publications have garnered international recognition, with his work being cited by leading journals in the field of engineering 🔬. He is also a reviewer for various prestigious journals, underlining his recognition in the academic community 🏅.
Research Focus
Juan Tian’s research focuses on intelligent fault diagnosis and prognostics, utilizing advanced machine learning techniques like deep learning and transfer learning. His work addresses key challenges such as diagnosing rotating machinery with incomplete data, particularly in complex industrial settings. His research has led to innovations in predictive maintenance and fault diagnosis systems for various industries 🧠🔧.
Conclusion
Juan Tian is a rising expert in the field of intelligent fault diagnosis, combining advanced machine learning methods to tackle industrial challenges. His academic and research contributions have shaped the development of practical diagnostic solutions, making him a leading figure in his field 🌟.
Publications
Fault Diagnosis With Robustness and Lightweight Synergy Under Noisy Environment, IEEE Sensors Journal, 2023 (SCI Indexed)
A Review of Rotation Mechanical Fault Diagnosis Research Based on Deep Domain Adaptation, 2023 IEEE Ninth International Conference on Big Data Computing Service and Applications, 2023 (Scopus Indexed)
Multi-sensor Based Graph Convolution Fault Diagnosis Method, 2024 36th Chinese Control and Decision Conference, 2024 (Scopus Indexed)
A Multi-Source Domain Adaptation Method for Bearing Fault Diagnosis with Dynamically Similarity Guidance on Incomplete Data, Actuators, 2025 (SCI Indexed)