Intelligent health evaluation of rolling bearings based on subspace meta-learning
Multi-source Unsupervised Domain Adaptation for Machinery Fault Diagnosis under Different Working Conditions
Jun Zhu received the B.S. degree in mechanical engineering from Huazhong Agricultural University, Wuhan, China, in 2013, and the M.S. degree in mechatronic engineering from University of Science and Technology of China, Hefei, China, in 2016.He is pursuing the Ph.D. degree in industrial engineering at National University of Singapore, Singapore. His research interests include signal processing and data mining for machinery prognostics and health management.
Adversarial multi-domain adaptation for machine fault diagnosis with variable working conditions
Name: Qi Li
Nationality: P.R. China
Date of Birth: 1997.07.18
Affiliation: School of Mechanical and Electric Engineering, Soochow University.
Major: Control theory and control engineering
His research interests include machine learning (especially generative adversarial networks), data mining and transfer learning. Now, he is working on transfer learning-based fault diagnosis.
Fault prognosis using deep convolutional neural network and bootstrap-based method
Brief biography of the presenting author-Cheng-Geng Huang
Cheng-Geng Huang received the B.E. degree in information display and photoelectric technology and
the Ph.D. degree in mechanical engineering from the University of Electronic Science and Technology of China, Chengdu, China, in 2013 and 2020, respectively.
He is currently an Postdoc researcher with the Department of Intelligence Science and Technology, School of Intelligent Systems Engineering, Sun Yat-sen University, Guangzhou, China.
His research interests include machinery condition monitoring and data mining for machinery prognostics and health management.
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