Student Changchun University of Technology China
π Weijun Wang (ηε¨ηΊ) is a researcher in the field of Statistics, currently pursuing a Ph.D. at Changchun University of Technology, China. With expertise spanning fault diagnosis, data-driven designs, and dynamic systems, Weijun Wang has made significant contributions to high-speed train traction systems and other advanced engineering applications.
Profile
Scopus
Education
π Weijun Wang’s academic journey includes:
- Ph.D. in Statistics (2021βPresent): Changchun University of Technology.
- Master’s in Information and Communication Engineering (2018β2021): Changchun University of Technology.
- Bachelor’s in Electronic Information Engineering (2014β2018): Changchun University of Technology.
Experience
π©βπ¬ Weijun Wang has extensive experience in developing innovative fault detection methods, dynamic system models, and data-driven algorithms. Collaborating on multidisciplinary projects, she has co-authored several impactful journal and conference papers.
Research Interests
π¬ Weijun Wang’s research focuses on:
- Fault detection and diagnosis.
- Data-driven modeling for high-speed train traction systems.
- Dynamic systems, robust designs, and performance prediction.
Awards
π First Prize: National Finals at RAICOM, 2024.
π
Innovation Award: 3rd China Urban Rail Transit Science and Technology Innovation Competition, 2022.
Publications
A Martingale Posterior-Based Fault Detection and Estimation Method for Electrical Systems of Industry
Dynamic Fault Detection Method of Traction Systems in High-Speed Trains Based on Joint Observer
Data-Driven Robust Designs of Performance Prediction and Its Application in High-speed Trains
Enhanced Fault Diagnosis Using Broad Learning for Traction Systems in High-Speed Trains
State Estimation with Partial Random Walk
State-degradation-oriented fault diagnosis for high-speed train running gears system
Fault prediction of high-speed train running gears based on hidden markov model and analytic hierarchy process
Multi-sensor system filtering and fault detection under unbiased constraint and colored measurement noise