Junyu Guo

1.3k total citations · 2 hit papers
58 papers, 878 citations indexed

About

Junyu Guo is a scholar working on Mechanical Engineering, Control and Systems Engineering and Mechanics of Materials. According to data from OpenAlex, Junyu Guo has authored 58 papers receiving a total of 878 indexed citations (citations by other indexed papers that have themselves been cited), including 19 papers in Mechanical Engineering, 16 papers in Control and Systems Engineering and 13 papers in Mechanics of Materials. Recurrent topics in Junyu Guo's work include Machine Fault Diagnosis Techniques (14 papers), Reliability and Maintenance Optimization (11 papers) and Gear and Bearing Dynamics Analysis (9 papers). Junyu Guo is often cited by papers focused on Machine Fault Diagnosis Techniques (14 papers), Reliability and Maintenance Optimization (11 papers) and Gear and Bearing Dynamics Analysis (9 papers). Junyu Guo collaborates with scholars based in China, Portugal and United Kingdom. Junyu Guo's co-authors include He Li, Yulai Yang, Jiang Wang, Zhiyuan Wang, Hong‐Zhong Huang, Mohammad Yazdi, Cheng‐Geng Huang, Lin Wang, Hooi Siang Kang and Yan‐Feng Li and has published in prestigious journals such as Applied Energy, Cell stem cell and International Journal of Hydrogen Energy.

In The Last Decade

Junyu Guo

49 papers receiving 863 citations

Hit Papers

A hybrid prognosis scheme for rolling bearings based on a... 2024 2026 2025 2024 2024 25 50 75
What are hit papers?

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.

  1. A hybrid prognosis scheme for rolling bearings based on a novel health indicator and nonlinear Wiener process
    2024 88 citations Junyu Guo, Zhiyuan Wang et al. Reliability Engineering & System Safety profile →
  2. A parallel deep neural network for intelligent fault diagnosis of drilling pumps
    2024 60 citations Junyu Guo, Yulai Yang et al. profile →

Peers — A (Enhanced Table)

Peers by citation overlap · career bar shows stage (early→late) cites · hero ref

Name h Career Trend Papers Cites
Junyu Guo China 19 304 265 189 135 125 58 878
Wei Liang China 13 266 0.9× 243 0.9× 170 0.9× 86 0.6× 92 0.7× 63 775
Koroush Shirvan United States 25 385 1.3× 121 0.5× 113 0.6× 103 0.8× 153 1.2× 122 1.9k
Taotao Zhou China 15 228 0.8× 334 1.3× 131 0.7× 84 0.6× 250 2.0× 35 861
Xiangdi Kong China 11 260 0.9× 497 1.9× 166 0.9× 171 1.3× 313 2.5× 21 1.0k
Xingyu Zhao China 13 196 0.6× 414 1.6× 94 0.5× 147 1.1× 76 0.6× 90 1.2k
Yongbo Zhang China 18 112 0.4× 152 0.6× 179 0.9× 186 1.4× 117 0.9× 114 1.0k
Jianhong Chen China 14 241 0.8× 196 0.7× 153 0.8× 69 0.5× 33 0.3× 95 702
Kristoffer McKee Australia 17 257 0.8× 169 0.6× 165 0.9× 58 0.4× 82 0.7× 42 981

Countries citing papers authored by Junyu Guo

Since Specialization
Citations

This map shows the geographic impact of Junyu Guo'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 Junyu Guo with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Junyu Guo more than expected).

Fields of papers citing papers by Junyu Guo

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Junyu Guo. 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 Junyu Guo. The network helps show where Junyu Guo may publish in the future.

Co-authorship network of co-authors of Junyu Guo

This figure shows the co-authorship network connecting the top 25 collaborators of Junyu Guo. A scholar is included among the top collaborators of Junyu Guo 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 Junyu Guo. Junyu Guo is excluded from the visualization to improve readability, since they are connected to all nodes in the network.

All Works

20 of 20 papers shown
1.
Zhang, Linhua, et al.. (2025). Failure analysis and reliability assessment of gold-plated fuzz buttons in elevated temperature. Microelectronics Reliability. 168. 115687–115687.
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Wang, Lin, et al.. (2025). Uncertainty-aware hourly load forecasting of hydrogen-blended natural gas with a hybrid deep learning model. International Journal of Hydrogen Energy. 127. 541–563.
4.
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Luo, Tao, Tao Cheng, Junyu Guo, et al.. (2025). Establishing dorsal-ventral patterning in human neural tube organoids with synthetic organizers. Cell stem cell. 32(7). 1071–1086.e8.
6.
Guo, Junyu, et al.. (2025). A hybrid fault diagnosis scheme for milling tools using MWN-CBAM-PatchTST network with acoustic emission signals. Nondestructive Testing And Evaluation. 40(12). 5864–5892. 4 indexed citations
7.
Guo, Junyu, et al.. (2025). A dual-channel transferable model for cross-domain remaining useful life prediction of rolling bearings under uncertainty. Measurement Science and Technology. 36(3). 36151–36151.
8.
Guo, Junyu, et al.. (2025). A hybrid data-driven prognostic scheme based on unsupervised health indicator construction and random-effects Wiener process. Computers & Industrial Engineering. 212. 111706–111706.
9.
Wang, Tianxin, et al.. (2025). A hybrid deep learning model towards flow pattern identification of gas-liquid two-phase flows in horizontal pipe. Energy. 320. 135141–135141. 5 indexed citations
10.
Wang, Lin, et al.. (2024). Numerical analysis of the effect of hydrogen doping ratio on gas transmission in low-pressure pipeline network. International Journal of Hydrogen Energy. 73. 868–884. 11 indexed citations
11.
Guo, Junyu, et al.. (2024). A dual‐channel transferable RUL prediction method integrated with Bayesian deep learning and domain adaptation for rolling bearings. Quality and Reliability Engineering International. 40(5). 2348–2366. 5 indexed citations
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Wang, Lin, et al.. (2024). GTFE-Net-BiLSTM-AM: An intelligent feature recognition method for natural gas pipelines. Gas Science and Engineering. 125. 205311–205311. 6 indexed citations
14.
Yang, Yuan‐Jian, et al.. (2024). A hybrid reliability assessment method based on health index construction and reliability modeling for rolling bearing. Quality and Reliability Engineering International. 40(8). 4131–4146. 1 indexed citations
15.
Wang, Lin, et al.. (2024). An integrated deep learning model for intelligent recognition of long-distance natural gas pipeline features. Reliability Engineering & System Safety. 255. 110664–110664. 21 indexed citations
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Guo, Junyu, et al.. (2023). A random forest regression with Bayesian optimization-based method for fatigue strength prediction of ferrous alloys. Engineering Fracture Mechanics. 293. 109714–109714. 33 indexed citations
18.
Wang, Lin, et al.. (2023). Investigation on the flow-induced vibration of tandem cylinders with varying diameters at subcritical Reynolds numbers. Applied Ocean Research. 138. 103669–103669. 7 indexed citations
19.
Wang, Lin, et al.. (2023). Experimental study of methane hydrate formation and agglomeration in waxy oil-in-water emulsions. Energy. 288. 129945–129945. 13 indexed citations
20.
Cooley, Daniel, Trent Perry, Junyu Guo, et al.. (2006). MR thermometry-based feedback control of efficacy and safety in minimum-time thermal therapies: Phantom andin-vivoevaluations. International Journal of Hyperthermia. 22(1). 29–42. 22 indexed citations

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.

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