Name: Yu Fajun
Gender: Male
Professional Title: Associate Professor
Degree: Ph.D.
Education Experience:
1. September 2001–June 2005, Bachelor's degree in Electronic Science and Technology, Zhengzhou University
2. September 2005–June 2008, Master's degree in Measurement and Instrumentation, Zhengzhou University
3. September 2013–December 2016, Ph.D. in Control Science and Engineering, Wuhan University of Science and Technology
Research Directions:
1. Information processing and intelligent fault diagnosis
2. Deep learning and equipment remaining useful life prediction
Academic Research Project:
1. Henan Higher Education Key Research Project: Quantized Sparse Diagnosis of Early Faults in Key Rotating Machinery Components, July 2017–June 2019, Principal Investigator, completed.
2. Henan Higher Education Key Research Project: Research on Sparse Blind Separation Technology for Single-Channel Signals, July 2013–June 2015, Principal Investigator, completed.
3. China National Textile and Apparel Council Guidance Project: Research on Intelligent Bearing Diagnosis for High-Speed Precision Textile Machinery Based on Transfer Learning, October 2020–December 2022, Principal Investigator, completed.
4. China National Textile and Apparel Council Guidance Project: Data-Driven Early Progressive Fault Identification and Remaining-Life Prediction for Spinning Spindle Drive Components, August 2023–November 2025, Principal Investigator, ongoing according to webpage.
5. Industry-sponsored project: Development of an Information Management System for Condition Parameter Monitoring of Induction Machine Fault Test Equipment, January 2024–May 2026, Principal Investigator, ongoing according to webpage.
Representative Papers:
1. Fajun Yu, Liang Liao et al. A novel 1D-CNN based diagnosis method for rolling bearing with dual-sensor vibration data fusion. Mathematical Problems in Engineering, 2022(07): 8986900. SCI.
2. Fajun Yu, Fengxing Zhou et al. Transform-domain sparse representation based classification for machinery vibration signals. Journal of Vibroengineering, 2018, 20(02): 979–987. SCI.
3. Yu Fajun*, Zhou Fengxing. Early Bearing Fault Diagnosis Based on Tunable Q-Factor Wavelet Transform and Spectral Kurtosis. Journal of Central South University (Science and Technology), 2015, 46(11): 4122–4128. EI.
4. Yu Fajun, Zhou Fengxing*, Yan Baokang. Sparse Feature Extraction for Early Bearing Faults Based on Dictionary Learning. Journal of Vibration and Shock, 2016, 35(6): 181–186. EI.
Contact:
Email: yufj@zut.edu.cn
