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Arvind Keprate

Arvind Keprate

Kort om

Dr. Arvind Keprate received his B.Tech in Mechanical Engineering (2007) from Himachal Pradesh University, M.Sc. in Marine & Subsea Technology (2014), and Ph.D. (2017), in Offshore Engineering from the University of Stavanger, Norway. During his Ph.D. he was a visiting researcher at the Prognostics Center of Excellence, NASA Ames Research Center, USA, where he developed a surrogate model (using machine learning and deep learning) to predict the stress intensity factor (SIF) for various crack sizes and loading conditions.

He is currently an Associate Professor at Oslo Metropolitan University where he teaches various Design-related courses such as Machine Design, Process & Piping Design, and Sustainable Design to Mechanical Engineering students. He also teaches various courses related to Machine Learning, Probability & Statistics, Data Analytics, and Python at Kristiania University College in Oslo. He is also the Leader of the Mechanics, Mechatronics, and Materials Technology (M3T) Research Group at OsloMet and also the Project Manager for the GrønnMet lab project. Besides this, he is also a member of the Doctoral Committee at the faculty.

Dr. Keprate has over 10 years of industrial experience (Reliance Industries Limited, DNV) as a pipeline engineer, with expertise in fatigue analysis, fitness-for-service (FFS) assessment, asset integrity, and risk/reliability engineering. He is also an accomplished researcher specializing in the application of machine learning and probabilistic techniques to Condition Monitoring, Prognostics, Reliability Modelling, and Integrity Assessment of engineering assets. Currently, his research is focused on PHM of complex Socio-Ecological Technical Systems such as Wind Farms.

Dr. Keprate has been awarded research grants from funding agencies, including the Research Council of Norway (RCN), Norwegian Directorate for Higher Education and Skills (HK-dir), Norwegian AI Research Consortium (NORA), and RegionaleForskningFond (RFF).

Dr. Keprate also serves as an Associate Editor for the International Journal of System Assurance Engineering and Management, Springer-Nature, and has been a topic editor for a Special Issue on “Online Monitoring of Wind Power Plants Using Digital TwinModels”, Frontiers in Energy Research.

Publikasjoner og forskningsresultater

Vitenskapelige publikasjoner

Frafjord, Aksel Johan; Radicke, Jan-Philip; Komulainen, Tiina ; Keprate, Arvind (2024). Data-driven approaches for deriving a soft sensor in a district heating network. Energy.
https://doi.org/10.1016/j.energy.2024.130426

Mian, Haris Hameed; Siddiqui, Muhammad Salman; Yang, Liang; Keprate, Arvind ; Badar, Abdul Waheed (2023). Effect of leading-edge erosion on the performance of offshore horizontal axis wind turbine using BEM method. 13 s. Journal of Physics: Conference Series (JPCS). Vol. 2626.
https://doi.org/10.1088/1742-6596/2626/1/012028

Keprate, Arvind ; Sheikhi, Saeid; Siddiqui, Muhammad Salman; Tanwar, Monika (2023). Comparing Deep Learning Based Image Processing Techniques for Unsupervised Anomaly Detection in Offshore Wind Turbines. ., . (Red.). IEEM 2023 IEEE International Conference on Industrial Engineering and Engineering Management Singapore 18 - 21 December 2023. s. 274-278. IEEE conference proceedings.
https://doi.org/10.1109/IEEM58616.2023.10406361

Komulainen, Tiina M. ; Baqeri, A. Malik; Nermo, Einar; Keprate, Arvind ; Saltnes, Torgeir; Jansen, Katrine M.; Korostynska, Olga (2023). Estimation of effluent nutrients in municipal MBBR process. 8 s. Linköping Electronic Conference Proceedings.
https://doi.org/10.3384/ecp200037

Keprate, Arvind ; Bagalkot, Nikhil; Siddiqui, Muhammad Salman; Sen, Subhamoy (2023). Reliability analysis of 15MW horizontal axis wind turbine rotor blades using fluid-structure interaction simulation and adaptive kriging model. 14 s. Ocean Engineering. Vol. 288.
https://doi.org/10.1016/j.oceaneng.2023.116138

Bindingsbø, Oliver Trygve; Singh, Maneesh; Øvsthus, Knut; Keprate, Arvind (2023). Fault detection of a wind turbine generator bearing using interpretable machine learning. 19 s. Frontiers in Energy Research. Vol. 11.
https://doi.org/10.3389/fenrg.2023.1284676

Keprate, Arvind ; Woodford, Sam ; Borrajo, Rafael (2023). From Theory to Practice Leveraging Project Based Learning to Cultivate Student Engagement in Mechanical Engineering Education. ., . (Red.). IEEM 2023 IEEE International Conference on Industrial Engineering and Engineering Management Singapore 18 - 21 December 2023. IEEE conference proceedings.
https://doi.org/10.1109/IEEM58616.2023.10406362

Siddiqui, Muhammad Salman; Keprate, Arvind ; Yang, Liang; Malmedal, Tiril (2023). Towards an Integrative Framework for Digital Twins in Wind Power. ., . (Red.). IEEM 2023 IEEE International Conference on Industrial Engineering and Engineering Management Singapore 18 - 21 December 2023. IEEE conference proceedings.
https://doi.org/10.1109/IEEM58616.2023.10406340

Keprate, Arvind (2023). LIMITATIONS AND OPPORTUNITIES IN PHM FOR OFFSHORE WIND FARMS: A SOCIO-TECHNICAL-ECOLOGICAL SYSTEM PERSPECTIVE. Kulkarni, Chetan; Roychoudhury, Indranil (Red.). PROCEEDINGS OF THE ANNUAL CONFERENCE OF THE PHM SOCIETY 2023. PHM society.
https://doi.org/10.36001/phmconf.2023.v15i1.3697

Keprate, Arvind ; Bagalkot, Nikhil (2022). Prognostics for Small Bore Piping Undergoing Fatigue Degradation. ., . (Red.). 2022 IEEE International Conference on Industrial Engineering and Engineering Management (IEEM) 7-10 Dec 2022. IEEE conference proceedings.
https://doi.org/10.1109/IEEM55944.2022.9989892





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