Mohd Atif Jamil

About

Dr. Mohd Atif Jamil obtained his B. Tech. and M. Tech. Degrees in Mechanical Engineering from Aligarh Muslim University in 2010 and 2013. He has recently completed his PhD and currently working in the Department of Mechanical Engineering, AMU as a Guest Faculty. Prior to this, he served as a Guest Faculty and subsequently as an Assistant Professor (Contractual) at the University Polytechnic, AMU, for about two years. Additionally, he worked as an Assistant Professor at a reputable AKTU affiliated engineering college in Aligarh for over five years.

His accomplishments include the publication of a few SCIE and Scopus indexed journal articles, a book chapter (Springer) and participation in 12 international conference proceedings/presentations. His research focuses on Condition Monitoring & Intelligent Fault Diagnosis (IFD) of rolling element bearings, and vibration modeling of bearing defects.

Dr. Atif holds a Life Membership with the Condition Monitoring Society of India (CMSI) and is an Associate Member of The Institution of Engineers (India).

Employment

• Aligarh Muslim University Guest Faculty
  2023 - Present
• Aligarh Muslim University Assistant Professor (Contractual)
  2021 - 2021
• Aligarh Muslim University Guest Faculty
  2019 - 2021
• Vivekananda College of Technology & Management Assistant Professor
  2013 - 2018

Education

• Aligarh Muslim University Ph. D.
  2018 - 2023
• Aligarh Muslim University M. Tech.
  2010 - 2013
• Aligarh Muslim University B. Tech.
  2006 - 2010

Projects & Funding

Projects & funding information is unavailable.

Publications (9)

• Intelligent Fault Detection in Wind Turbine Using Spectral Estimation Methods
  2026 DOI: 10.1007/978-981-96-5791-9_11
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• Minimal Redundancy Maximal Relevance (MRMR)-Based Feature Ranking for Efficient Automated Fault Diagnosis of Rolling Element Bearing
  2026 DOI: 10.1007/978-981-96-5791-9_15
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• Influence of One-Way ANOVA and Kruskal–Wallis Based Feature Ranking on the Performance of ML Classifiers for Bearing Fault Diagnosis
  Journal of Vibration Engineering and Technologies 2024 DOI: 10.1007/s42417-023-01036-x
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• Identifying Condition Indicators for Artificially Intelligent Fault Classification in Rolling Element Bearings
  Mechanisms and Machine Science 2023 DOI: 10.1007/978-981-99-4721-8_22
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• Fault Classification of Rolling Element Bearing in Machine Learning Domain
  The International Journal of Acoustics and Vibration 2022 DOI: 10.20855/ijav.2022.27.21829
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• Erratum: Feature-based performance of SVM and KNN classifiers for diagnosis of rolling element bearing faults (Vibroengineering Procedia (2021) 39 (36-42) DOI: 10.21595/vp.2021.22307)
  Vibroengineering Procedia 2022 DOI: 10.21595/vp.2021.22353
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• Intelligent Prediction of Multiple Defects in Rolling Element Bearing using ANN Algorithm
  2022 5th International Conference on Multimedia, Signal Processing and Communication Technologies, IMPACT 2022 2022 DOI: 10.1109/IMPACT55510.2022.10029154
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• Feature-based performance of SVM and KNN classifiers for diagnosis of rolling element bearing faults
  Vibroengineering Procedia 2021 DOI: 10.21595/vp.2021.22307
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• Unsupervised Content based Image Retrieval at Different Precision Level by Combining Multiple Features
  Journal of Physics: Conference Series 2021 DOI: 10.1088/1742-6596/1950/1/012059
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