Len Gelman

1.1k total citations · 1 hit paper
79 papers, 831 citations indexed

About

Len Gelman is a scholar working on Control and Systems Engineering, Mechanical Engineering and Mechanics of Materials. According to data from OpenAlex, Len Gelman has authored 79 papers receiving a total of 831 indexed citations (citations by other indexed papers that have themselves been cited), including 46 papers in Control and Systems Engineering, 28 papers in Mechanical Engineering and 23 papers in Mechanics of Materials. Recurrent topics in Len Gelman's work include Machine Fault Diagnosis Techniques (43 papers), Gear and Bearing Dynamics Analysis (17 papers) and Structural Health Monitoring Techniques (17 papers). Len Gelman is often cited by papers focused on Machine Fault Diagnosis Techniques (43 papers), Gear and Bearing Dynamics Analysis (17 papers) and Structural Health Monitoring Techniques (17 papers). Len Gelman collaborates with scholars based in United Kingdom, Poland and Italy. Len Gelman's co-authors include Andrew D. Ball, Hamid Reza Karimi, Ivan Petrunin, Sio-Iong Ao, Yue Yu, F. Combet, Tejas Patel, Ahmet Enis Çetin, Fulei Chu and Dezun Zhao and has published in prestigious journals such as Expert Systems with Applications, Sensors and Mechanical Systems and Signal Processing.

In The Last Decade

Len Gelman

75 papers receiving 804 citations

Hit Papers

MSIFT: A novel end-to-end mechanical fault diagnosis fram... 2025 2026 2025 5 10 15 20
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. MSIFT: A novel end-to-end mechanical fault diagnosis framework under limited & imbalanced data using multi-source information fusion
    2025 20 citations Yue Yu, Hamid Reza Karimi et al. Expert Systems with Applications profile →

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
Len Gelman United Kingdom 18 502 370 252 171 100 79 831
Zhiwen Liu China 18 610 1.2× 461 1.2× 164 0.7× 113 0.7× 115 1.1× 45 965
Hanling Mao China 16 276 0.5× 328 0.9× 222 0.9× 161 0.9× 111 1.1× 73 698
S. Radkowski Poland 11 498 1.0× 441 1.2× 152 0.6× 91 0.5× 96 1.0× 88 884
Yongzhi Qu United States 21 797 1.6× 630 1.7× 314 1.2× 186 1.1× 142 1.4× 64 1.2k
Shengyao Gao China 7 454 0.9× 227 0.6× 186 0.7× 90 0.5× 50 0.5× 10 682
Djamel Benazzouz Algeria 17 495 1.0× 374 1.0× 191 0.8× 50 0.3× 65 0.7× 78 756
Miao He United States 13 576 1.1× 423 1.1× 196 0.8× 58 0.3× 146 1.5× 22 964
Mileta Tomovic United States 18 482 1.0× 528 1.4× 179 0.7× 43 0.3× 113 1.1× 83 1.1k
Shuangwen Sheng United States 19 797 1.6× 624 1.7× 283 1.1× 201 1.2× 240 2.4× 73 1.3k
Gang Yu China 13 619 1.2× 422 1.1× 196 0.8× 93 0.5× 178 1.8× 38 945

Countries citing papers authored by Len Gelman

Since Specialization
Citations

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

Fields of papers citing papers by Len Gelman

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Len Gelman

This figure shows the co-authorship network connecting the top 25 collaborators of Len Gelman. A scholar is included among the top collaborators of Len Gelman 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 Len Gelman. Len Gelman 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.
Gelman, Len, et al.. (2025). Novel Higher Order Technologies, Based on Spectral Moduli, for Condition Monitoring of Rotating Machinery. Sensors. 25(20). 6290–6290. 1 indexed citations
2.
Yu, Yue, et al.. (2025). A novel digital twin-enabled three-stage feature imputation framework for non-contact intelligent fault diagnosis. Advanced Engineering Informatics. 66. 103434–103434. 11 indexed citations
3.
Yu, Yue, Hamid Reza Karimi, Len Gelman, & Ahmet Enis Çetin. (2025). MSIFT: A novel end-to-end mechanical fault diagnosis framework under limited & imbalanced data using multi-source information fusion. Expert Systems with Applications. 274. 126947–126947. 20 indexed citations breakdown →
4.
5.
Gelman, Len, et al.. (2024). A Novel Diagnostic Feature for a Wind Turbine Imbalance Under Variable Speed Conditions. Sensors. 24(21). 7073–7073. 1 indexed citations
6.
Yu, Yue, et al.. (2024). A two-stage importance-aware subgraph convolutional network based on multi-source sensors for cross-domain fault diagnosis. Neural Networks. 179. 106518–106518. 35 indexed citations
7.
Gelman, Len, et al.. (2023). Novel Fault Diagnosis of a Conveyor Belt Mis-Tracking via Motor Current Signature Analysis. Sensors. 23(7). 3652–3652. 11 indexed citations
8.
Gelman, Len, et al.. (2023). Innovative Conveyor Belt Monitoring via Current Signals. Electronics. 12(8). 1804–1804. 3 indexed citations
9.
Wilk, Andrzej, et al.. (2021). Novel method of estimation of inertial and dissipative parameters of a railway pantograph model. Vehicle System Dynamics. 60(7). 2413–2435. 7 indexed citations
10.
Gelman, Len, et al.. (2021). Novel Instantaneous Wavelet Bicoherence for Vibration Fault Detection in Gear Systems. Energies. 14(20). 6811–6811. 12 indexed citations
11.
Ball, Andrew D. & Len Gelman. (2021). Advances in Asset Management and Condition Monitoring: COMADEM 2019. 6 indexed citations
12.
13.
Zhao, Dezun, Len Gelman, Fulei Chu, & Andrew D. Ball. (2020). Novel Method for Vibration Sensor-Based Instantaneous Defect Frequency Estimation for Rolling Bearings Under Non-Stationary Conditions. Sensors. 20(18). 5201–5201. 14 indexed citations
14.
Gelman, Len, et al.. (2017). Novel condition monitoring of induction motor bearings via motor current signature analysis. 1–12. 1 indexed citations
15.
Gelman, Len, et al.. (2017). Vibration diagnosis of a gearbox by wavelet bicoherence technology. Insight - Non-Destructive Testing and Condition Monitoring. 59(8). 440–444. 12 indexed citations
16.
Ao, Sio-Iong, et al.. (2015). Transactions on Engineering Technologies: World Congress on Engineering 2014. Springer eBooks. 8 indexed citations
17.
Gelman, Len, et al.. (2015). The World Congress on Engineering 2015, WCE 2015. Research Explorer (The University of Manchester). 20 indexed citations
18.
Ao, Sio-Iong, et al.. (2015). Transactions on Engineering Technologies. CERN Document Server (European Organization for Nuclear Research). 6 indexed citations
19.
Gelman, Len & Ivan Petrunin. (2008). Time–frequency higher‐order spectra with adjustment to the instantaneous frequency variation. International Journal of Adaptive Control and Signal Processing. 24(3). 178–187. 5 indexed citations
20.
Gelman, Len, et al.. (2000). NON-LINEAR VIBROACOUSTICAL FREE OSCILLATION METHOD FOR CRACK DETECTION AND EVALUATION. Mechanical Systems and Signal Processing. 14(3). 343–351. 13 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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