J
Jing Lin
Researcher at Beihang University
Publications - 187
Citations - 15209
Jing Lin is an academic researcher from Beihang University. The author has contributed to research in topics: Lamb waves & Computer science. The author has an hindex of 45, co-authored 167 publications receiving 10192 citations. Previous affiliations of Jing Lin include Xi'an Jiaotong University & Ningbo Institute of Technology, Zhejiang University.
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A review on empirical mode decomposition in fault diagnosis of rotating machinery
TL;DR: This paper attempts to survey and summarize the recent research and development of EMD in fault diagnosis of rotating machinery, providing comprehensive references for researchers concerning with this topic and helping them identify further research topics.
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Deep neural networks: A promising tool for fault characteristic mining and intelligent diagnosis of rotating machinery with massive data
TL;DR: The diagnosis results show that the proposed method is able to not only adaptively mine available fault characteristics from the measured signals, but also obtain superior diagnosis accuracy compared with the existing methods.
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Machinery health prognostics: A systematic review from data acquisition to RUL prediction
TL;DR: A review on machinery prognostics following its whole program, i.e., from data acquisition to RUL prediction, which provides discussions on current situation, upcoming challenges as well as possible future trends for researchers in this field.
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An Intelligent Fault Diagnosis Method Using Unsupervised Feature Learning Towards Mechanical Big Data
TL;DR: A two-stage learning method inspired by the idea of unsupervised feature learning that uses artificial intelligence techniques to learn features from raw data for intelligent diagnosis of machines that reduces the need of human labor and makes intelligent fault diagnosis handle big data more easily.
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A recurrent neural network based health indicator for remaining useful life prediction of bearings
TL;DR: A recurrent neural network based health indicator for RUL prediction of bearings with fairly high monotonicity and correlation values is proposed and it is experimentally demonstrated that the proposed RNN-HI is able to achieve better performance than a self organization map based method.