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 a 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. 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.
Fagområder
Vitenskapsdisipliner
Forskningsgrupper
Publikasjoner og forskningsresultater
Vitenskapelige publikasjoner
Mian, Haris Hameed; Siddiqui, Muhammad Salman;
Keprate, Arvind
; Mathew, Sathyajith
(2024).
Predictive Modeling of Semi-Submersible Floater Motion Using Bi-LSTM Model.
12 s.
Journal of Physics: Conference Series (JPCS).
Vol. 2875.
https://doi.org/10.1088/1742-6596/2875/1/012029
Siddiqui, Muhammad Salman; Waheed, Abdul Waheed; Yang, Liang; Saeed, Muhammed;
Keprate, Arvind
(2024).
Qualitative Investigation of Wake Composition in Offshore Wind Turbines: A Combined Computational and Statistical Analysis of Inner and Outer Blade Sections.
7 s.
E3S Web of Conferences.
Vol. 487.
https://doi.org/10.1051/e3sconf/202448701001
Sharma, Nikhil; Saraswat, Chirag; Sharma, Jeetesh; Mittal, Murari Lal;
Keprate, Arvind
(2024).
Multi-Objective Optimization for Economic and Environmental Sustainability in Apparel E-commerce Reverse Logistics.
17 s.
International Journal of Mathematical, Engineering and Management Sciences.
Vol. 9.
https://doi.org/10.33889/IJMEMS.2024.9.1.006
Sharma, Jeetesh; Mittal, Murari Lal; Soni, Gunjan;
Keprate, Arvind
(2024).
Explainable Artificial Intelligence (XAI) Approaches in Predictive Maintenance: A Review.
8 s.
Recent Patents on Engineering.
Vol. 18.
https://doi.org/10.2174/1872212118666230417084231
Frafjord, Aksel Johan; Radicke, Jan-Philip;
Keprate, Arvind
;
Komulainen, Tiina
(2024).
Data-driven approaches for deriving a soft sensor in a district heating network.
Energy.
Vol. 292.
https://doi.org/10.1016/j.energy.2024.130426
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
;
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
; 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
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
