Tauheed Mian

706 total citations · 1 hit paper
16 papers, 534 citations indexed

About

Tauheed Mian is a scholar working on Control and Systems Engineering, Mechanical Engineering and Mechanics of Materials. According to data from OpenAlex, Tauheed Mian has authored 16 papers receiving a total of 534 indexed citations (citations by other indexed papers that have themselves been cited), including 14 papers in Control and Systems Engineering, 10 papers in Mechanical Engineering and 5 papers in Mechanics of Materials. Recurrent topics in Tauheed Mian's work include Machine Fault Diagnosis Techniques (14 papers), Gear and Bearing Dynamics Analysis (7 papers) and Advanced machining processes and optimization (5 papers). Tauheed Mian is often cited by papers focused on Machine Fault Diagnosis Techniques (14 papers), Gear and Bearing Dynamics Analysis (7 papers) and Advanced machining processes and optimization (5 papers). Tauheed Mian collaborates with scholars based in India and Belgium. Tauheed Mian's co-authors include Anurag Choudhary, Shahab Fatima, Bijaya Ketan Panigrahi, Mohd Bilal Naim Shaikh, Rismaya Kumar Mishra and A.R. Mohanty and has published in prestigious journals such as IEEE Transactions on Industry Applications, IEEE Sensors Journal and Measurement.

In The Last Decade

Tauheed Mian

14 papers receiving 510 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.

Convolutional neural network based bearing fault diagnosis of rotating machine using thermal images

2021 217 citations Anurag Choudhary, Tauheed Mian et al. Measurement profile →

Peers — A (Enhanced Table)

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

Name	h						Papers	Cites
Tauheed Mian India	9	387	263	194	66	52	16	534
Myeongsu Kang United States	10	527 1.4×	302 1.1×	156 0.8×	74 1.1×	58 1.1×	16	673
Juan José Saucedo-Dorantes Mexico	16	450 1.2×	274 1.0×	169 0.9×	134 2.0×	62 1.2×	56	639
J. Kozik Poland	7	479 1.2×	340 1.3×	171 0.9×	93 1.4×	42 0.8×	12	610
Chemseddine Rahmoune Algeria	14	427 1.1×	332 1.3×	180 0.9×	45 0.7×	23 0.4×	41	553
M. Saimurugan India	10	347 0.9×	237 0.9×	103 0.5×	51 0.8×	41 0.8×	32	465
Zhenhui Zhan China	11	449 1.2×	272 1.0×	126 0.6×	40 0.6×	39 0.8×	14	566
Lizhi Liu China	7	496 1.3×	295 1.1×	184 0.9×	59 0.9×	56 1.1×	13	656
Moussa Hamadache South Korea	11	398 1.0×	274 1.0×	106 0.5×	60 0.9×	56 1.1×	24	556
Zhenling Mo China	13	507 1.3×	327 1.2×	166 0.9×	66 1.0×	43 0.8×	26	637

Countries citing papers authored by Tauheed Mian

Since Specialization

Citations

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

Fields of papers citing papers by Tauheed Mian

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Tauheed Mian

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

All Works

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16 of 16 papers shown

Mian, Tauheed, et al.. (2025). A comprehensive review on generative artificial intelligence models for fault diagnostics and prognostics of rotating machineries. Engineering Applications of Artificial Intelligence. 166. 113594–113594.

Mian, Tauheed, Anurag Choudhary, Shahab Fatima, & Bijaya Ketan Panigrahi. (2023). Mel-spectrogram based Approach for Fault Detection in Ball Bearing using Convolutional Neural Network. 283–289.

Mian, Tauheed, Anurag Choudhary, & Shahab Fatima. (2023). Multi-Sensor Fault Diagnosis for Misalignment and Unbalance Detection Using Machine Learning. IEEE Transactions on Industry Applications. 59(5). 5749–5759. 40 indexed citations

Choudhary, Anurag, Tauheed Mian, Shahab Fatima, & Bijaya Ketan Panigrahi. (2023). Nature‐inspired artificial bee colony‐based hyperparameter optimization of CNN for anomaly detection in induction motor. Expert Systems. 41(7). 5 indexed citations

Choudhary, Anurag, Tauheed Mian, Shahab Fatima, & Bijaya Ketan Panigrahi. (2023). Fault Diagnosis of Electric Two-Wheeler Under Pragmatic Operating Conditions Using Wavelet Synchrosqueezing Transform and CNN. IEEE Sensors Journal. 23(6). 6254–6263. 16 indexed citations

Choudhary, Anurag, Tauheed Mian, Shahab Fatima, & Bijaya Ketan Panigrahi. (2023). Multi Sensor based Bearing Fault Diagnosis of Switched Reluctance Motor for Electric Vehicle. 257–263. 2 indexed citations

Mian, Tauheed, Anurag Choudhary, Shahab Fatima, & Bijaya Ketan Panigrahi. (2023). Artificial intelligence of things based approach for anomaly detection in rotating machines. Computers & Electrical Engineering. 109. 108760–108760. 25 indexed citations

Mishra, Rismaya Kumar, Tauheed Mian, Shahab Fatima, A.R. Mohanty, & Bijaya Ketan Panigrahi. (2023). Motor and bearing multi-fault diagnosis with sound quality metrics. NOISE-CON proceedings. 268(8). 57–67. 1 indexed citations

Mian, Tauheed, et al.. (2022). Near dry turning of EN8 and EN31 steel: multi-objective optimization using grey relational analysis. Engineering Research Express. 4(3). 35053–35053. 6 indexed citations

10.

Mian, Tauheed, Anurag Choudhary, & Shahab Fatima. (2022). Vibration and infrared thermography based multiple fault diagnosis of bearing using deep learning. Nondestructive Testing And Evaluation. 38(2). 275–296. 70 indexed citations

11.

Mian, Tauheed, Anurag Choudhary, & Shahab Fatima. (2022). Multi-Sensor Fault Diagnosis for Misalignment and Unbalance Detection Using Machine Learning. 1–6. 16 indexed citations

12.

Mian, Tauheed, Anurag Choudhary, & Shahab Fatima. (2022). An efficient diagnosis approach for bearing faults using sound quality metrics. Applied Acoustics. 195. 108839–108839. 50 indexed citations

13.

Choudhary, Anurag, Tauheed Mian, Shahab Fatima, & Bijaya Ketan Panigrahi. (2022). Passive Thermography Based Bearing Fault Diagnosis Using Transfer Learning With Varying Working Conditions. IEEE Sensors Journal. 23(5). 4628–4637. 51 indexed citations

14.

Choudhary, Anurag, Tauheed Mian, Shahab Fatima, & Bijaya Ketan Panigrahi. (2022). Deep Transfer Learning Based Fault Diagnosis of Electric Vehicle Motor. 1–6. 3 indexed citations

15.

Mian, Tauheed, et al.. (2021). A sensor fusion based approach for bearing fault diagnosis of rotating machine. Proceedings of the Institution of Mechanical Engineers Part O Journal of Risk and Reliability. 236(5). 661–675. 32 indexed citations

16.

Choudhary, Anurag, Tauheed Mian, & Shahab Fatima. (2021). Convolutional neural network based bearing fault diagnosis of rotating machine using thermal images. Measurement. 176. 109196–109196. 217 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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