R. Fuentes

558 total citations
18 papers, 366 citations indexed

About

R. Fuentes is a scholar working on Control and Systems Engineering, Civil and Structural Engineering and Mechanical Engineering. According to data from OpenAlex, R. Fuentes has authored 18 papers receiving a total of 366 indexed citations (citations by other indexed papers that have themselves been cited), including 9 papers in Control and Systems Engineering, 8 papers in Civil and Structural Engineering and 5 papers in Mechanical Engineering. Recurrent topics in R. Fuentes's work include Structural Health Monitoring Techniques (8 papers), Machine Fault Diagnosis Techniques (4 papers) and Probabilistic and Robust Engineering Design (3 papers). R. Fuentes is often cited by papers focused on Structural Health Monitoring Techniques (8 papers), Machine Fault Diagnosis Techniques (4 papers) and Probabilistic and Robust Engineering Design (3 papers). R. Fuentes collaborates with scholars based in United Kingdom, Chile and Denmark. R. Fuentes's co-authors include Elizabeth J. Cross, Keith Worden, Nikolaos Dervilis, Timothy J. Rogers, Gareth Pierce, Carmelo Mineo, Rajdip Nayek, Paul Gardner, R.S. Dwyer-Joyce and G. Manson and has published in prestigious journals such as Renewable Energy, Journal of Sound and Vibration and Mechanical Systems and Signal Processing.

In The Last Decade

R. Fuentes

18 papers receiving 360 citations

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
R. Fuentes United Kingdom 9 178 125 85 83 64 18 366
Konstantinos Tatsis Switzerland 13 295 1.7× 90 0.7× 90 1.1× 93 1.1× 71 1.1× 36 417
Yigit Yucesan United States 10 97 0.5× 164 1.3× 204 2.4× 82 1.0× 46 0.7× 16 438
Qiuhai Lu China 14 303 1.7× 139 1.1× 84 1.0× 168 2.0× 71 1.1× 27 412
Anna Kučerová Czechia 11 154 0.9× 50 0.4× 36 0.4× 132 1.6× 104 1.6× 32 354
Liangxian Gu China 13 109 0.6× 96 0.8× 56 0.7× 112 1.3× 128 2.0× 51 466
Arinan Dourado United States 9 69 0.4× 108 0.9× 106 1.2× 62 0.7× 50 0.8× 20 307
Rajdip Nayek India 9 172 1.0× 73 0.6× 64 0.8× 24 0.3× 104 1.6× 17 299
D.O. Smallwood United States 14 334 1.9× 123 1.0× 126 1.5× 154 1.9× 102 1.6× 47 486
Shengtong Zhou China 9 48 0.3× 112 0.9× 112 1.3× 56 0.7× 27 0.4× 23 292
Yves Govers Germany 11 364 2.0× 79 0.6× 95 1.1× 51 0.6× 292 4.6× 52 499

Countries citing papers authored by R. Fuentes

Since Specialization
Citations

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

Fields of papers citing papers by R. Fuentes

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of R. Fuentes

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

All Works

18 of 18 papers shown
1.
Fuentes, R., Rajdip Nayek, Paul Gardner, et al.. (2021). Equation discovery for nonlinear dynamical systems: A Bayesian viewpoint. Mechanical Systems and Signal Processing. 154. 107528–107528. 42 indexed citations
2.
Zhu, Yichen, Paul Gardner, David Wagg, et al.. (2021). Robust equation discovery considering model discrepancy: A sparse Bayesian and Gaussian process approach. Mechanical Systems and Signal Processing. 168. 108717–108717. 7 indexed citations
3.
Nayek, Rajdip, R. Fuentes, Keith Worden, & Elizabeth J. Cross. (2021). On spike-and-slab priors for Bayesian equation discovery of nonlinear dynamical systems via sparse linear regression. Mechanical Systems and Signal Processing. 161. 107986–107986. 37 indexed citations
4.
Gardner, Paul, R. Fuentes, Nikolaos Dervilis, et al.. (2020). Machine learning at the interface of structural health monitoring and non-destructive evaluation. Philosophical Transactions of the Royal Society A Mathematical Physical and Engineering Sciences. 378(2182). 20190581–20190581. 46 indexed citations
5.
Mineo, Carmelo, Momchil Vasilev, Bruce Cowan, et al.. (2020). Enabling robotic adaptive behaviour capabilities for new Industry 4.0 automated quality inspection paradigms. Insight - Non-Destructive Testing and Condition Monitoring. 62(6). 338–344. 22 indexed citations
6.
Fuentes, R., Paul Gardner, Carmelo Mineo, et al.. (2020). Autonomous ultrasonic inspection using Bayesian optimisation and robust outlier analysis. Mechanical Systems and Signal Processing. 145. 106897–106897. 14 indexed citations
7.
Fuentes, R., Nikolaos Dervilis, Keith Worden, & Elizabeth J. Cross. (2019). Efficient parameter identification and model selection in nonlinear dynamical systems via sparse Bayesian learning. Journal of Physics Conference Series. 1264(1). 12050–12050. 8 indexed citations
8.
Dervilis, Nikolaos, Lawrence A. Bull, Elizabeth J. Cross, et al.. (2019). A nonlinear robust outlier detection approach for SHM. ENLIGHTEN (Jurnal Bimbingan dan Konseling Islam). 107–114. 1 indexed citations
9.
Bull, Lawrence A., Keith Worden, R. Fuentes, et al.. (2019). Outlier ensembles: A robust method for damage detection and unsupervised feature extraction from high-dimensional data. Journal of Sound and Vibration. 453. 126–150. 38 indexed citations
10.
Fuentes, R., et al.. (2019). Detection of sub-surface damage in wind turbine bearings using acoustic emissions and probabilistic modelling. Renewable Energy. 147. 776–797. 43 indexed citations
11.
Fuentes, R., Carmelo Mineo, Gareth Pierce, Keith Worden, & Elizabeth J. Cross. (2018). A probabilistic compressive sensing framework with applications to ultrasound signal processing. Mechanical Systems and Signal Processing. 117. 383–402. 23 indexed citations
12.
Rogers, Timothy J., et al.. (2018). A Bayesian non-parametric clustering approach for semi-supervised Structural Health Monitoring. Mechanical Systems and Signal Processing. 119. 100–119. 68 indexed citations
13.
Fuentes, R., Keith Worden, Ifigeneia Antoniadou, et al.. (2017). Compressive Sensing for Direct Time of Flight Estimation in Ultrasound-based NDT. Nova Science Publishers (Nova Science Publishers, Inc.). 2 indexed citations
14.
Fuentes, R., Thomas P. Howard, Matthew Marshall, Elizabeth J. Cross, & R.S. Dwyer-Joyce. (2016). Observations on acoustic emissions from a line contact compressed into the plastic region. Proceedings of the Institution of Mechanical Engineers Part J Journal of Engineering Tribology. 230(11). 1371–1376. 3 indexed citations
15.
16.
Fuentes, R., et al.. (2014). Aircraft Parametric Structural Load Monitoring Using Gaussian Process Regression. HAL (Le Centre pour la Communication Scientifique Directe). 5 indexed citations
17.
Fuentes, R., et al.. (2013). An Approach to Fault Detection Using a Unified Linear Gaussian Framework. Structural Health Monitoring. 1 indexed citations
18.
Valencia, Álvaro, et al.. (2009). A Methodology for Controlling Slopping in Copper Converters by Using Lateral and Bottom Gas Injection. International Journal of Chemical Reactor Engineering. 7(1). 5 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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