Search Results - "Xing, Saibo"

An intelligent fault diagnosis approach based on transfer learning from laboratory bearings to locomotive bearings by Yang, Bin, Lei, Yaguo, Jia, Feng, Xing, Saibo

“…•A feature-based transfer neural network is proposed for bearing fault diagnosis.•Diagnosis knowledge is transferred from laboratory bearings to locomotive…”
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Deep normalized convolutional neural network for imbalanced fault classification of machinery and its understanding via visualization by Jia, Feng, Lei, Yaguo, Lu, Na, Xing, Saibo

“…•Deep normalized convolutional neural network (DNCNN) is proposed for imbalanced fault classification of machinery.•DNCNN uses normalized layers to improve its…”
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A neural network constructed by deep learning technique and its application to intelligent fault diagnosis of machines by Jia, Feng, Lei, Yaguo, Guo, Liang, Lin, Jing, Xing, Saibo

“…In traditional intelligent fault diagnosis methods of machines, plenty of actual effort is taken for the manual design of fault features, which makes these…”
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Deep Convolutional Transfer Learning Network: A New Method for Intelligent Fault Diagnosis of Machines With Unlabeled Data by Guo, Liang, Lei, Yaguo, Xing, Saibo, Yan, Tao, Li, Naipeng

“…The success of intelligent fault diagnosis of machines relies on the following two conditions: 1) labeled data with fault information are available; and 2) the…”
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Distribution-Invariant Deep Belief Network for Intelligent Fault Diagnosis of Machines Under New Working Conditions by Xing, Saibo, Lei, Yaguo, Wang, Shuhui, Jia, Feng

“…As a deep learning model, a deep belief network (DBN) consists of multiple restricted Boltzmann machines (RBMs). Based on DBN, many intelligent fault diagnosis…”
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A label description space embedded model for zero-shot intelligent diagnosis of mechanical compound faults by Xing, Saibo, Lei, Yaguo, Wang, Shuhui, Lu, Na, Li, Naipeng

“…•A zero-shot intelligent diagnosis method for mechanical compound faults is proposed.•A label description space is built to represent the semantic relationship…”
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An Intelligent Fault Diagnosis Method Using Unsupervised Feature Learning Towards Mechanical Big Data by Lei, Yaguo, Jia, Feng, Lin, Jing, Xing, Saibo, Ding, Steven X.

“…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…”
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Adaptive Knowledge Transfer by Continual Weighted Updating of Filter Kernels for Few-Shot Fault Diagnosis of Machines by Xing, Saibo, Lei, Yaguo, Yang, Bin, Lu, Na

“…Deep learning (DL) based diagnosis models have to be trained by large quantities of monitoring data of machines. However, in real-case scenarios, machines…”
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A Transfer Learning Method for Intelligent Fault Diagnosis from Laboratory Machines to Real-Case Machines by Bin Yang, Yaguo Lei, Feng Jia, Saibo Xing

“…It is difficult to train a reliable intelligent fault diagnosis model for machines used in real cases (MURC) because there are not sufficient labeled data…”
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Deep convolution feature learning for health indicator construction of bearings by Liang Guo, Yaguo Lei, Naipeng Li, Saibo Xing

“…In the field of data-driven prognostics of bearings, considerable research effort has been taken to construct an effective health indicator. However, existing…”
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A method of automatic feature extraction from massive vibration signals of machines by Feng Jia, Yaguo Lei, Saibo Xing, Jing Lin

“…In the studies of intelligent fault diagnosis of machines, lots of effort goes into designing effective feature extraction algorithms. Such processes would…”
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A nonlinear degradation model based method for remaining useful life prediction of rolling element bearings by Yaguo Lei, Naipeng Li, Feng Jia, Jing Lin, Saibo Xing

“…This paper proposes a nonlinear degradation model based method for remaining useful life (RUL) prediction of rolling element bearings. First, a new nonlinear…”
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Intelligent fault diagnosis of rotating machinery using locally connected restricted boltzmann machine in big data era by Xing, Saibo, Lei, Yaguo, Jia, Feng, Lin, Jing

“…In intelligent fault diagnosis, unsupervised feature learning is a potential tool to replace the manual feature extraction in big data era. Therefore, we first…”
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