Akhand Rai

1.2k total citations · 1 hit paper
15 papers, 948 citations indexed

About

Akhand Rai is a scholar working on Mechanical Engineering, Control and Systems Engineering and Mechanics of Materials. According to data from OpenAlex, Akhand Rai has authored 15 papers receiving a total of 948 indexed citations (citations by other indexed papers that have themselves been cited), including 11 papers in Mechanical Engineering, 10 papers in Control and Systems Engineering and 6 papers in Mechanics of Materials. Recurrent topics in Akhand Rai's work include Machine Fault Diagnosis Techniques (10 papers), Gear and Bearing Dynamics Analysis (6 papers) and Engineering Diagnostics and Reliability (5 papers). Akhand Rai is often cited by papers focused on Machine Fault Diagnosis Techniques (10 papers), Gear and Bearing Dynamics Analysis (6 papers) and Engineering Diagnostics and Reliability (5 papers). Akhand Rai collaborates with scholars based in India, China and South Korea. Akhand Rai's co-authors include Sanjay Upadhyay, Anil Kumar, P. S. Balaji, Bimal Das, Akhilendra Singh, Hesheng Tang, Jiawei Xiang, Anil K. Mishra, Prashant Tiwari and Ricardo Branco and has published in prestigious journals such as Mechanical Systems and Signal Processing, Applied Soft Computing and Tribology International.

In The Last Decade

Akhand Rai

13 papers receiving 911 citations

Hit Papers

A review on signal processing techniques utilized in the ... 2016 2026 2019 2022 2016 100 200 300 400 500
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 review on signal processing techniques utilized in the fault diagnosis of rolling element bearings
    2016 583 citations Akhand Rai, Sanjay Upadhyay Tribology International profile →

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
Akhand Rai India 7 813 624 305 102 89 15 948
Zhongjie Shen China 11 620 0.8× 445 0.7× 263 0.9× 82 0.8× 83 0.9× 17 766
Bingchang Hou China 17 621 0.8× 391 0.6× 172 0.6× 76 0.7× 146 1.6× 36 748
Purushottam Gangsar India 13 734 0.9× 399 0.6× 293 1.0× 38 0.4× 60 0.7× 17 883
Zhenling Mo China 13 507 0.6× 327 0.5× 166 0.5× 58 0.6× 47 0.5× 26 637
Alexandre Mauricio Belgium 11 765 0.9× 529 0.8× 253 0.8× 20 0.2× 101 1.1× 28 855
Fangzhou Cheng United States 15 643 0.8× 333 0.5× 133 0.4× 113 1.1× 91 1.0× 21 802
Qinghua Zhang China 12 567 0.7× 301 0.5× 198 0.6× 40 0.4× 41 0.5× 44 728
Wei-Li Qin China 6 590 0.7× 322 0.5× 188 0.6× 48 0.5× 53 0.6× 8 742
Seokgoo Kim South Korea 15 412 0.5× 284 0.5× 124 0.4× 105 1.0× 55 0.6× 29 626

Countries citing papers authored by Akhand Rai

Since Specialization
Citations

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

Fields of papers citing papers by Akhand Rai

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Akhand Rai

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

All Works

15 of 15 papers shown
1.
Mishra, Anil K., et al.. (2025). Leak detection in pipelines based on a multilayer network of time–frequency domain cross-visibility graphs and graph convolutional networks. Mechanical Systems and Signal Processing. 238. 113250–113250.
2.
Mishra, Anil K., et al.. (2025). Leak detection in pipelines based on acoustic emission and growing neural gas network utilizing unlabeled healthy condition data. Flow Measurement and Instrumentation. 102. 102816–102816. 5 indexed citations
3.
Rai, Akhand, Jong-Myon Kim, Anil Kumar, & P. S. Balaji. (2024). Gear fault diagnosis based on bidimensional time-frequency information theoretic features and error-correcting output codes: A multi-class support vector machine. Proceedings of the Institution of Mechanical Engineers Part O Journal of Risk and Reliability. 239(3). 552–567.
4.
Rai, Akhand, et al.. (2024). Gear fault diagnosis based on complex network theory and error-correcting output codes: Multi class support vector machine. Proceedings of the Institution of Mechanical Engineers Part I Journal of Systems and Control Engineering. 238(6). 1135–1152. 2 indexed citations
5.
Rai, Akhand, et al.. (2024). Research prospects of friction stir additive manufacturing (FSAM). AIP conference proceedings. 3023. 30006–30006. 2 indexed citations
6.
Singh, Akhilendra, et al.. (2024). Synergistic effect of fatigue and hydrogen-induced damage under asymmetric loading of TRIP steel. Engineering Failure Analysis. 161. 108352–108352. 3 indexed citations
7.
8.
Rai, Akhand, et al.. (2022). An intelligent fault diagnosis framework based on piecewise aggregate approximation, statistical moments, and sparse autoencoder. Proceedings of the Institution of Mechanical Engineers Part O Journal of Risk and Reliability. 237(4). 686–702. 2 indexed citations
9.
Kumar, Anil, et al.. (2022). Maximal overlap discrete wavelet packet transforms and variants of neutrosophic cubic cross-entropy-based identification of rotor defects. Measurement Science and Technology. 33(8). 85107–85107. 7 indexed citations
10.
Rai, Akhand, et al.. (2019). The application of semi-nonnegative matrix factorization for detection of incipient faults in bearings. Proceedings of the Institution of Mechanical Engineers Part C Journal of Mechanical Engineering Science. 233(13). 4543–4555. 6 indexed citations
11.
Rai, Akhand & Sanjay Upadhyay. (2018). An integrated approach to bearing prognostics based on EEMD-multi feature extraction, Gaussian mixture models and Jensen-Rényi divergence. Applied Soft Computing. 71. 36–50. 71 indexed citations
12.
Rai, Akhand & Sanjay Upadhyay. (2017). Intelligent bearing performance degradation assessment and remaining useful life prediction based on self-organising map and support vector regression. Proceedings of the Institution of Mechanical Engineers Part C Journal of Mechanical Engineering Science. 232(6). 1118–1132. 66 indexed citations
13.
Rai, Akhand & Sanjay Upadhyay. (2017). The use of MD-CUMSUM and NARX neural network for anticipating the remaining useful life of bearings. Measurement. 111. 397–410. 68 indexed citations
14.
Rai, Akhand & Sanjay Upadhyay. (2017). Bearing performance degradation assessment based on a combination of empirical mode decomposition and k-medoids clustering. Mechanical Systems and Signal Processing. 93. 16–29. 127 indexed citations
15.
Rai, Akhand & Sanjay Upadhyay. (2016). A review on signal processing techniques utilized in the fault diagnosis of rolling element bearings. Tribology International. 96. 289–306. 583 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.

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