Hit papers significantly outperform the citation benchmark for their cohort. A paper qualifies
if it has ≥500 total citations, achieves ≥1.5× the top-1% citation threshold for papers in the
same subfield and year (this is the minimum needed to enter the top 1%, not the average
within it), or reaches the top citation threshold in at least one of its specific research
topics.
Deep Convolutional Transfer Learning Network: A New Method for Intelligent Fault Diagnosis of Machines With Unlabeled Data
2018993 citationsLiang Guo, Yaguo Lei et al.IEEE Transactions on Industrial Electronicsprofile →
An Intelligent Fault Diagnosis Method Using Unsupervised Feature Learning Towards Mechanical Big Data
2016984 citationsYaguo Lei, Feng Jia et al.IEEE Transactions on Industrial Electronicsprofile →
An intelligent fault diagnosis approach based on transfer learning from laboratory bearings to locomotive bearings
2019719 citationsBin Yang, Yaguo Lei et al.Mechanical Systems and Signal Processingprofile →
Deep normalized convolutional neural network for imbalanced fault classification of machinery and its understanding via visualization
2018498 citationsFeng Jia, Yaguo Lei et al.Mechanical Systems and Signal Processingprofile →
A neural network constructed by deep learning technique and its application to intelligent fault diagnosis of machines
2017443 citationsFeng Jia, Yaguo Lei et al.Neurocomputingprofile →
Peers — A (Enhanced Table)
Peers by citation overlap · career bar shows stage (early→late)
cites ·
hero ref
This map shows the geographic impact of Saibo Xing's research. It shows the number of citations coming from papers published by authors working in each country. You can also color the map by specialization and compare the number of citations received by Saibo Xing with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Saibo Xing more than expected).
This network shows the impact of papers produced by Saibo Xing. Nodes represent research fields, and links connect fields that are likely to share authors. Colored nodes show fields that tend to cite the papers produced by Saibo Xing. The network helps show where Saibo Xing may publish in the future.
Co-authorship network of co-authors of Saibo Xing
This figure shows the co-authorship network connecting the top 25 collaborators of Saibo Xing.
A scholar is included among the top collaborators of Saibo Xing based on the total number of
citations received by their joint publications. Widths of edges
represent the number of papers authors have co-authored together.
Node borders
signify the number of papers an author published with Saibo Xing. Saibo Xing is excluded from
the visualization to improve readability, since they are connected to all nodes in the network.
Yang, Bin, Yaguo Lei, Feng Jia, & Saibo Xing. (2019). An intelligent fault diagnosis approach based on transfer learning from laboratory bearings to locomotive bearings. Mechanical Systems and Signal Processing. 122. 692–706.719 indexed citations breakdown →
5.
Guo, Liang, Yaguo Lei, Saibo Xing, Tao Yan, & Naipeng Li. (2018). Deep Convolutional Transfer Learning Network: A New Method for Intelligent Fault Diagnosis of Machines With Unlabeled Data. IEEE Transactions on Industrial Electronics. 66(9). 7316–7325.993 indexed citations breakdown →
6.
Jia, Feng, Yaguo Lei, Na Lü, & Saibo Xing. (2018). Deep normalized convolutional neural network for imbalanced fault classification of machinery and its understanding via visualization. Mechanical Systems and Signal Processing. 110. 349–367.498 indexed citations breakdown →
Rankless uses publication and citation data sourced from OpenAlex, an open and comprehensive
bibliographic database. While OpenAlex provides broad and valuable coverage of the global
research landscape, it—like all bibliographic datasets—has inherent limitations. These include
incomplete records, variations in author disambiguation, differences in journal indexing, and
delays in data updates. As a result, some metrics and network relationships displayed in
Rankless may not fully capture the entirety of a scholar's output or impact.