T
Tao Yan
Researcher at Xi'an Jiaotong University
Publications - 21
Citations - 2820
Tao Yan is an academic researcher from Xi'an Jiaotong University. The author has contributed to research in topics: Prognostics & Computer science. The author has an hindex of 9, co-authored 17 publications receiving 1427 citations.
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Journal ArticleDOI
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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Deep Convolutional Transfer Learning Network: A New Method for Intelligent Fault Diagnosis of Machines With Unlabeled Data
TL;DR: A new intelligent method named deep convolutional transfer learning network (DCTLN) is proposed, which facilitates the 1-D CNN to learn domain-invariant features by maximizing domain recognition errors and minimizing the probability distribution distance.
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Deep separable convolutional network for remaining useful life prediction of machinery
TL;DR: The experimental results show that the proposed deep separable convolutional network (DSCN) is able to provide accurate RUL prediction results based on the raw multi-sensor data and is superior to some existing data-driven prognostics approaches.
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Machinery health indicator construction based on convolutional neural networks considering trend burr
TL;DR: A convolutional neural network based HI construction method considering trend burr is proposed, which aims to automatically construct HIs and achieves better results in terms of trendability, monotonicity and scale similarity.
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Recurrent convolutional neural network: A new framework for remaining useful life prediction of machinery
TL;DR: Experimental results demonstrate the effectiveness and superiority of RCNN in improving the accuracy and convergence of RUL prediction, and more importantly, RCNN is able to provide a probabilistic RUL Prediction result, which breaks the inherent limitation of CNNs and facilitates maintenance decision making.