Arvind Keprate

About

I am a Professor at Oslo Metropolitan University where I teach Design-related courses such as Machine Design, Process & Piping Design, and Sustainable Design to Mechanical Engineering students. I am also the Leader of the Mechanics and Material Technology Research Group at OsloMet. Besides this, I teach various courses related to Machine Learning, Probability, Statistics, Data Analytics and Python at Kristiania University College in Oslo.

I hold a Master's in Subsea Technology and a Ph.D. in Offshore Technology from the University of Stavanger, Norway. I have been a visiting researcher at the Prognostics Center of Excellence, NASA Ames Research Center, USA.

I am 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 such as Wind turbines, Process Piping, and Power Transmission Lines.

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

Employment

• Høyskolen Kristiania External Lecturer
  2020 - Present
• OsloMet – Oslo Metropolitan University Associate Professor
  2020 - Present
• NASA Ames Research Center Research Scientist
  2016 - 2016
• University of Stavanger Research Fellow
  2014 - 2017
• EMAS AMC Senior Engineer
  2013 - 2015
• Reliance Industries Limited Pipeline Engineer
  2007 - 2012

Education

• University of Stavanger PhD
  2014 - 2017
• Universitetet i Stavanger Masters in Offshore Technology
  2012 - 2014
• Himachal Pradesh University Bachelors in Mechanical Engineering
  2003 - 2007
• Sainik School Sujanpur Tihra High School
  1996 - 2003

Projects & Funding

Projects & funding information is unavailable.

Publications (68)

• Education 4.0 For Cyber-Physical Systems: A Case of Wind Turbines
  2026 0 citations DOI: 10.1007/978-3-032-30833-7_12
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• Fatigue Damage Assessment of FOWT Mooring Lines Using Sequence-to-Sequence Based Indirect Sensing
  2026 DOI: 10.1007/978-3-031-95963-9_4
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• MoC-PiRNN: A motion-conditioned physics-integrated recurrent framework for moving-boundary flow prediction
  2026 0 citations DOI: 10.2139/ssrn.6435550
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• Temporal Feature Extraction Based Real Time Damage Detection of Floating Offshore Wind Turbine Mooring Lines
  2026 DOI: 10.1007/978-3-031-95963-9_5
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• A motion-conditioned physics-integrated recurrent framework for moving-boundary flow prediction
  2026 0 citations DOI: 10.2139/ssrn.6946084
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• Addressing material uncertainty in reliability analysis of floating offshore mooring through probabilistic meta-model developed with Stochastic Kriging technique
  Ocean Engineering 2025 4 citations DOI: 10.1016/j.oceaneng.2025.121697
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• A Conceptual Framework of An Integrative Leadership for Cybersecurity Management and Designing Digital Road Map for Organizations
  2025 2 citations DOI: 10.4324/9781003626640-5
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• Real-time fatigue assessment of Floating Offshore Wind Turbine Mooring employing sequence-to-sequence-based deep learning on indirect fatigue response
  Ocean Engineering 2025 21 citations DOI: 10.1016/j.oceaneng.2024.119741
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• Characterizing Damage in Wind Turbine Mooring Using a Data-Driven Predictor Model within a Particle Filtering Estimation Framework
  PHM Society European Conference 2024 3 citations DOI: 10.36001/phme.2024.v8i1.4051
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• Data-driven approaches for deriving a soft sensor in a district heating network
  Energy 2024 8 citations DOI: 10.1016/j.energy.2024.130426
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