Li Lingjun
Associate Prof.
Office Tel: 0086-371-67781792
Mobile：0086-13526801783
E-mail: lingjun@zzu.edu.cn
Research Interests
Equipment diagnosis and reliability analysis
Education
Ph.D., XianJiaotong University, 2003
M.S., XianJiaotong University, 1995
B.S., XianJiaotong University, 1987
Publications
(1) LI LingJun，GONG Xiao Yun，ZHANG Heng，et al． Research of rolling bearing fault diagnosis based on vector spectrum and DSVDD ［J］．Journal of Mechanical Strength，2013，35 ( 2 ) : 152- 155．
(2) SONG Hua-lei, LI Ling-jun, HAN Jie, et al.. Rotating Machinery Full Vector Spectrum Band Condition Evaluation Method Research［J］. Machinery Design&Manufacture, 2013,(8):152~157.
(3) Su Wenfang, Li Lingjun, Han Jie，et al. Fault Diagnosis Method for Rolling Bearings Based on Full Vector Local Mean Decomposition［J］.Bearing, 2014，(11):61-64.
(4)CHEN Chao, LI Ling-jun, LEI Wen-ping, et al. Research and implementation of fault diagnosis expert system for rotating machinery based on multi-source information fusion［J］.Manufacturing Automation ,2014（19）：16-18,22.
(5)Su Wenfang, Li Lingjun, Han Jie，et al. Research of Gear Fault Diagnosis Method Based on Full Vector of Local Mean Decomposition［J］.MACHINE TOOL&HYDRAULICS, 2015,43, (3):182-184.
(6) LI Lingjun, CHEN Chao, HAN Jie，et al. The Prediction Method of Frequency Spectrum Based on Full Vector Support Vector Regression[J]. Journal of Zhengzhou University (Engineering Science)，2016，37(3):78-82.
(7) WEN Yong-liang, LI Ling-jun, JIN Bing. Application of Full Vector MEMD Energy Entropy in Bearing Fault Diagnosis［J］. Machinery Design&Manufacture,2017，316（6）：38-41.
(8) JIN Bind，MA Yanli，LI LingJun，et al. Method of Fault Diagnosis for Rolling Bearing Based on Full Vector Nose Assisted Multivariate EMD［J］.MACHINE TOOL&HYDRAULICS, 2017,.45( 19):189~193+198.
(9) LI Ling-jun, BAI Yun, HAN Jie, et al. Equipment Spectral Composition Prediction Based on Full Vector Support Vector Regression[J]. Machinery Design&Manufacture, 2017(12):60-63.
(10)LI Ling jun, JIN Bing, MA Yanli, et al. The Method of Degradation Feature Extraction of Rolling Bearing Based on MEMD and Multivariate Multiscale Entropy[J]. Journal of Zhengzhou University (Engineering Science),2018,39(04):86-91.
