N.R. Sakthivel

1.7k total citations · 1 hit paper
39 papers, 1.3k citations indexed

About

N.R. Sakthivel is a scholar working on Control and Systems Engineering, Mechanical Engineering and Ocean Engineering. According to data from OpenAlex, N.R. Sakthivel has authored 39 papers receiving a total of 1.3k indexed citations (citations by other indexed papers that have themselves been cited), including 16 papers in Control and Systems Engineering, 13 papers in Mechanical Engineering and 8 papers in Ocean Engineering. Recurrent topics in N.R. Sakthivel's work include Machine Fault Diagnosis Techniques (12 papers), Fault Detection and Control Systems (11 papers) and Oil and Gas Production Techniques (8 papers). N.R. Sakthivel is often cited by papers focused on Machine Fault Diagnosis Techniques (12 papers), Fault Detection and Control Systems (11 papers) and Oil and Gas Production Techniques (8 papers). N.R. Sakthivel collaborates with scholars based in India, Sweden and Ethiopia. N.R. Sakthivel's co-authors include V. Sugumaran, Binoy B. Nair, K.I. Ramachandran, S. Shankar, Alokesh Pramanik, T. Mohanraj, Rajasekar Rathanasamy, M. Elangovan, M. Saimurugan and Pankaj Kumar and has published in prestigious journals such as SHILAP Revista de lepidopterología, Scientific Reports and Expert Systems with Applications.

In The Last Decade

N.R. Sakthivel

35 papers receiving 1.2k citations

Hit Papers

align trajectories

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.

Tool condition monitoring techniques in milling process — a review

2019 277 citations N.R. Sakthivel et al. profile →

Peers

N.R. Sakthivel

ME

CSE

EEE

MM

IME

Hongfu Zuo China

Miguel Delgado-Prieto Spain

Yiwei Cheng China

Govind Vashishtha India

Minh‐Quang Tran Taiwan

Lei Xiao China

Meng Ma China

Pritesh Shah India

Van Tung Tran South Korea

Petter Krus Sweden

Hongfu Zuo China

N.R. Sakthivel

495 ×0.7

ME

747 ×1.5

CSE

321 ×1.0

EEE

297 ×1.2

MM

173 ×0.7

IME

Citations per year, relative to N.R. Sakthivel N.R. Sakthivel (= 1×) peers Hongfu Zuo

Countries citing papers authored by N.R. Sakthivel

Since Specialization

Citations

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

Fields of papers citing papers by N.R. Sakthivel

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of N.R. Sakthivel

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

All Works

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20 of 20 papers shown

1.

Sakthivel, N.R., et al.. (2025). Machine Learning Approaches for Fault Detection in Internal Combustion Engines: A Review and Experimental Investigation. Informatics. 12(1). 25–25. 4 indexed citations

2.

Sakthivel, N.R., et al.. (2025). Cross domain fault diagnosis in internal combustion engines using multisensor data with transfer federated and transformer based federated transfer learning. Scientific Reports. 15(1). 36775–36775.

3.

Sakthivel, N.R., et al.. (2024). An acoustic dataset for surface roughness estimation in milling process. Data in Brief. 57. 111108–111108. 1 indexed citations

4.

Sridharan, Naveen Venkatesh, et al.. (2024). Deep learning for fault diagnosis of monoblock centrifugal pumps: a Hilbert–Huang transform approach. International Journal of Systems Assurance Engineering and Management. 1 indexed citations

5.

Sridharan, Naveen Venkatesh, et al.. (2023). Can pretrained networks be used in fault diagnosis of monoblock centrifugal pump?. Proceedings of the Institution of Mechanical Engineers Part E Journal of Process Mechanical Engineering. 239(3). 1306–1317. 1 indexed citations

6.

Sridharan, Naveen Venkatesh, Seshathiri Dhanasekaran, V. Sugumaran, et al.. (2023). Deep Learning for Enhanced Fault Diagnosis of Monoblock Centrifugal Pumps: Spectrogram-Based Analysis. Machines. 11(9). 874–874. 24 indexed citations

7.

Nair, Binoy B. & N.R. Sakthivel. (2021). An Upper Limb Rehabilitation Exercise Status Identification System Based on Machine Learning and IoT. Arabian Journal for Science and Engineering. 47(2). 2095–2121. 5 indexed citations

8.

Sakthivel, N.R., et al.. (2016). EFFECT OF KERNEL FUNCTION IN SUPPORT VECTOR MACHINE FOR THE FAULT DIAGNOSIS OF PUMP. SHILAP Revista de lepidopterología. 5 indexed citations

9.

Elangovan, M., et al.. (2015). Machine Learning Approach to the Prediction of Surface Roughness Using Statistical Features of Vibration Signal Acquired in Turning. Procedia Computer Science. 50. 282–288. 60 indexed citations

10.

Warrier, M., et al.. (2014). Voxel based parallel post processor for void nucleation and growth analysis of atomistic simulations of material fracture. Journal of Molecular Graphics and Modelling. 50. 134–141. 14 indexed citations

11.

Chaurasia, S., et al.. (2014). An improved time-dependent nonlocal electron heat-flux model and its verification by laser-driven Al foil acceleration experiment. High Energy Density Physics. 11. 36–44. 16 indexed citations

12.

Muralidharan, V.S., V. Sugumaran, & N.R. Sakthivel. (2011). Wavelet decomposition and support vector machine for fault diagnosis of monoblock centrifugal pump. International Journal of Data Analysis Techniques and Strategies. 3(2). 159–159. 18 indexed citations

13.

Nair, Binoy B., et al.. (2010). Application of hybrid adaptive filters for stock market prediction. 443–447. 9 indexed citations

14.

Nair, Binoy B., et al.. (2010). A Decision tree- Rough set Hybrid System for Stock Market Trend Prediction. International Journal of Computer Applications. 6(9). 1–6. 63 indexed citations

15.

Elangovan, M., et al.. (2010). Evaluation of expert system for condition monitoring of a single point cutting tool using principle component analysis and decision tree algorithm. Expert Systems with Applications. 38(4). 4450–4459. 83 indexed citations

16.

Saimurugan, M., K.I. Ramachandran, V. Sugumaran, & N.R. Sakthivel. (2010). Multi component fault diagnosis of rotational mechanical system based on decision tree and support vector machine. Expert Systems with Applications. 38(4). 3819–3826. 153 indexed citations

17.

Sakthivel, N.R., V. Sugumaran, & Binoy B. Nair. (2010). Comparison of decision tree-fuzzy and rough set-fuzzy methods for fault categorization of mono-block centrifugal pump. Mechanical Systems and Signal Processing. 24(6). 1887–1906. 70 indexed citations

18.

Sakthivel, N.R., et al.. (2009). Vibration based fault diagnosis of monoblock centrifugal pump using decision tree. Expert Systems with Applications. 37(6). 4040–4049. 188 indexed citations

19.

Sakthivel, N.R., et al.. (2002). Biomodelling through Reverse Engineering.

20.

Sakthivel, N.R. & Shashank Chaturvedi. (2001). Opacity Calculations for Dense Plasmas. Contributions to Plasma Physics. 41(4). 335–338. 3 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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