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Jing Lin

Bio: Jing Lin is an academic researcher from Beihang University. The author has contributed to research in topics: Lamb waves & Fault (power engineering). 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.


Papers
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Journal ArticleDOI
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.

1,410 citations

Journal ArticleDOI
Feng Jia1, Yaguo Lei1, Jing Lin1, Xin Zhou1, Na Lu1 
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.

1,289 citations

Journal ArticleDOI
Yaguo Lei1, Naipeng Li1, Liang Guo1, Ningbo Li1, Tao Yan1, Jing Lin1 
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.

1,116 citations

Journal ArticleDOI
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.
Abstract: Intelligent fault diagnosis is a promising tool to deal with mechanical big data due to its ability in rapidly and efficiently processing collected signals and providing accurate diagnosis results. In traditional intelligent diagnosis methods, however, the features are manually extracted depending on prior knowledge and diagnostic expertise. Such processes take advantage of human ingenuity but are time-consuming and labor-intensive. Inspired by the idea of unsupervised feature learning that uses artificial intelligence techniques to learn features from raw data, a two-stage learning method is proposed for intelligent diagnosis of machines. In the first learning stage of the method, sparse filtering, an unsupervised two-layer neural network, is used to directly learn features from mechanical vibration signals. In the second stage, softmax regression is employed to classify the health conditions based on the learned features. The proposed method is validated by a motor bearing dataset and a locomotive bearing dataset, respectively. The results show that the proposed method obtains fairly high diagnosis accuracies and is superior to the existing methods for the motor bearing dataset. Because of learning features adaptively, the proposed method reduces the need of human labor and makes intelligent fault diagnosis handle big data more easily.

915 citations

Journal ArticleDOI
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.

798 citations


Cited by
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Journal ArticleDOI

[...]

08 Dec 2001-BMJ
TL;DR: There is, I think, something ethereal about i —the square root of minus one, which seems an odd beast at that time—an intruder hovering on the edge of reality.
Abstract: There is, I think, something ethereal about i —the square root of minus one. I remember first hearing about it at school. It seemed an odd beast at that time—an intruder hovering on the edge of reality. Usually familiarity dulls this sense of the bizarre, but in the case of i it was the reverse: over the years the sense of its surreal nature intensified. It seemed that it was impossible to write mathematics that described the real world in …

33,785 citations

Christopher M. Bishop1
01 Jan 2006
TL;DR: Probability distributions of linear models for regression and classification are given in this article, along with a discussion of combining models and combining models in the context of machine learning and classification.
Abstract: Probability Distributions.- Linear Models for Regression.- Linear Models for Classification.- Neural Networks.- Kernel Methods.- Sparse Kernel Machines.- Graphical Models.- Mixture Models and EM.- Approximate Inference.- Sampling Methods.- Continuous Latent Variables.- Sequential Data.- Combining Models.

10,141 citations

Journal ArticleDOI
TL;DR: The applications of deep learning in machine health monitoring systems are reviewed mainly from the following aspects: Auto-encoder and its variants, Restricted Boltzmann Machines, Convolutional Neural Networks, and Recurrent Neural Networks.

1,569 citations

Journal ArticleDOI
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.

1,410 citations

Journal ArticleDOI
Feng Jia1, Yaguo Lei1, Jing Lin1, Xin Zhou1, Na Lu1 
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.

1,289 citations