Purushottam Gangsar

1.2k total citations · 1 hit paper
17 papers, 883 citations indexed

About

Purushottam Gangsar is a scholar working on Control and Systems Engineering, Mechanics of Materials and Mechanical Engineering. According to data from OpenAlex, Purushottam Gangsar has authored 17 papers receiving a total of 883 indexed citations (citations by other indexed papers that have themselves been cited), including 17 papers in Control and Systems Engineering, 14 papers in Mechanics of Materials and 7 papers in Mechanical Engineering. Recurrent topics in Purushottam Gangsar's work include Machine Fault Diagnosis Techniques (17 papers), Engineering Diagnostics and Reliability (14 papers) and Fault Detection and Control Systems (10 papers). Purushottam Gangsar is often cited by papers focused on Machine Fault Diagnosis Techniques (17 papers), Engineering Diagnostics and Reliability (14 papers) and Fault Detection and Control Systems (10 papers). Purushottam Gangsar collaborates with scholars based in India and Canada. Purushottam Gangsar's co-authors include Rajiv Tiwari, Vikas Singh, Manoj Chouksey and Anand Parey and has published in prestigious journals such as SHILAP Revista de lepidopterología, Mechanical Systems and Signal Processing and Measurement.

In The Last Decade

Purushottam Gangsar

17 papers receiving 832 citations

Hit Papers

align trajectories sort by overperformance

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.

Signal based condition monitoring techniques for fault detection and diagnosis of induction motors: A state-of-the-art review

2020 407 citations Purushottam Gangsar, Rajiv Tiwari Mechanical Systems and Signal Processing profile →

Peers — A (Enhanced Table)

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

Name	h						Papers	Cites
Purushottam Gangsar India	13	734	399	293	124	66	17	883
Wei-Li Qin China	6	590 0.8×	322 0.8×	188 0.6×	97 0.8×	43 0.7×	8	742
Kun Xu China	17	661 0.9×	441 1.1×	262 0.9×	68 0.5×	41 0.6×	41	899
Wanlu Jiang China	18	614 0.8×	468 1.2×	248 0.8×	89 0.7×	111 1.7×	68	877
Zongzhen Zhang China	17	822 1.1×	502 1.3×	268 0.9×	78 0.6×	36 0.5×	78	994
Maogui Niu China	7	699 1.0×	423 1.1×	250 0.9×	53 0.4×	37 0.6×	8	803
Jianyu Wang China	14	600 0.8×	348 0.9×	169 0.6×	93 0.8×	48 0.7×	40	783
Alexander E. Prosvirin South Korea	15	514 0.7×	338 0.8×	183 0.6×	45 0.4×	53 0.8×	21	673
J. Kozik Poland	7	479 0.7×	340 0.9×	171 0.6×	93 0.8×	40 0.6×	12	610
Aisong Qin China	14	553 0.8×	279 0.7×	200 0.7×	65 0.5×	35 0.5×	30	713
Muhammad Sohaib Pakistan	14	459 0.6×	310 0.8×	191 0.7×	65 0.5×	66 1.0×	37	745

Countries citing papers authored by Purushottam Gangsar

Since Specialization

Citations

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

Fields of papers citing papers by Purushottam Gangsar

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Purushottam Gangsar

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

All Works

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17 of 17 papers shown

Gangsar, Purushottam, et al.. (2022). Diagnostics of Combined Mechanical and Electrical Faults of an Electromechanical System for Steady and Ramp-Up Speeds. Journal of Vibration Engineering & Technologies. 10(4). 1431–1450. 7 indexed citations

Gangsar, Purushottam, et al.. (2022). A review on deep learning based condition monitoring and fault diagnosis of rotating machinery. Noise & Vibration Worldwide. 53(11). 550–578. 13 indexed citations

Gangsar, Purushottam, et al.. (2021). Unbalance detection in rotating machinery based on support vector machine using time and frequency domain vibration features. Noise & Vibration Worldwide. 52(4-5). 75–85. 15 indexed citations

Singh, Vikas, et al.. (2021). Deep learning based optimum fault diagnosis of electrical and mechanical faults in induction motor. IOP Conference Series Materials Science and Engineering. 1136(1). 12059–12059. 2 indexed citations

Gangsar, Purushottam, et al.. (2021). Artificial neural network–based fault diagnosis for induction motors under similar, interpolated and extrapolated operating conditions. Noise & Vibration Worldwide. 52(10). 323–333. 14 indexed citations

Singh, Vikas, et al.. (2021). Artificial intelligence application in fault diagnostics of rotating industrial machines: a state-of-the-art review. Journal of Intelligent Manufacturing. 34(3). 931–960. 99 indexed citations

Gangsar, Purushottam, et al.. (2020). Artificial neural network based fault diagnostics for three phase induction motors under similar operating conditions. Vibroengineering PROCEDIA. 30. 55–60. 13 indexed citations

Gangsar, Purushottam & Rajiv Tiwari. (2020). Signal based condition monitoring techniques for fault detection and diagnosis of induction motors: A state-of-the-art review. Mechanical Systems and Signal Processing. 144. 106908–106908. 407 indexed citations breakdown →

Gangsar, Purushottam & Rajiv Tiwari. (2019). Diagnostics of mechanical and electrical faults in induction motors using wavelet-based features of vibration and current through support vector machine algorithms for various operating conditions. Journal of the Brazilian Society of Mechanical Sciences and Engineering. 41(2). 24 indexed citations

10.

Gangsar, Purushottam & Rajiv Tiwari. (2019). Online Diagnostics of Mechanical and Electrical Faults in Induction Motor Using Multiclass Support Vector Machine Algorithms Based on Frequency Domain Vibration and Current Signals. ASCE-ASME Journal of Risk and Uncertainty in Engineering Systems Part B Mechanical Engineering. 5(3). 14 indexed citations

11.

Gangsar, Purushottam & Rajiv Tiwari. (2018). Effect of noise on support vector machine based fault diagnosis of IM using vibration and current signatures. SHILAP Revista de lepidopterología. 211. 3009–3009. 4 indexed citations

12.

Gangsar, Purushottam & Rajiv Tiwari. (2018). Multifault Diagnosis of Induction Motor at Intermediate Operating Conditions Using Wavelet Packet Transform and Support Vector Machine. Journal of Dynamic Systems Measurement and Control. 140(8). 43 indexed citations

13.

Gangsar, Purushottam & Rajiv Tiwari. (2018). A support vector machine based fault diagnostics of Induction motors for practical situation of multi-sensor limited data case. Measurement. 135. 694–711. 67 indexed citations

14.

Gangsar, Purushottam & Rajiv Tiwari. (2017). Comparative investigation of vibration and current monitoring for prediction of mechanical and electrical faults in induction motor based on multiclass-support vector machine algorithms. Mechanical Systems and Signal Processing. 94. 464–481. 120 indexed citations

15.

Gangsar, Purushottam & Rajiv Tiwari. (2017). Analysis of Time, Frequency and Wavelet Based Features of Vibration and Current Signals for Fault Diagnosis of Induction Motors Using SVM. 5 indexed citations

16.

Gangsar, Purushottam & Rajiv Tiwari. (2016). Taxonomy of Induction-Motor Mechanical-Fault Based on Time-Domain Vibration Signals by Multiclass SVM Classifiers. 2(3). 269–281. 15 indexed citations

17.

Gangsar, Purushottam & Rajiv Tiwari. (2014). Multiclass Fault Taxonomy in Rolling Bearings at Interpolated and Extrapolated Speeds Based on Time Domain Vibration Data by SVM Algorithms. Journal of Failure Analysis and Prevention. 14(6). 826–837. 21 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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