Blar i TKD - Institutt for maskin, elektronikk og kjemi på forfatter "Keprate, Arvind"

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    • Artificial Intelligence Based Approach for Predicting Fatigue Strength Using Composition and Process Parameters 

      Keprate, Arvind; Ratnayake Mudiyanselage, Chandima (International Conference on Offshore Mechanics and Arctic Engineering;Volume 3: Materials Technology, Conference object, 2020-12-18)
      Accurate prediction of the fatigue strength of steels is vital, due to the extremely high cost (and time) of fatigue testing and the often fatal consequences of fatigue failures. The work presented in this paper is an ...

    • CFD Simulations of Flow Jetting Impact and High Erosion Region in a Production Choke and its Downstream Spool 

      Keprate, Arvind; Bagalkot, Nikhil; perinpasivam, Agastian (Linköping Electronic Conference Proceedings;Proceedings of the 63rd International Conference of Scandinavian Simulation Society, SIMS 2022, Chapter; Peer reviewed; Conference object; Journal article, 2022)
      Erosion wear, a rather well-known problem in the petroleum and transport industry. Over the years there have been many different models suggested to estimate the erosion. Each model uses unique equations and is suited for ...

    • Combining Computational Fluid Dynamics and Gradient Boosting Regressor for Predicting Force Distribution on Horizontal Axis Wind Turbine 

      Bagalkot, Nikhil; Keprate, Arvind; Orderløkken, Rune (Vibration;Volume 4 / Issue 1, Peer reviewed; Journal article, 2021-03-14)
      The blades of the horizontal axis wind turbine (HAWT) are generally subjected to significant forces resulting from the flow field around the blade. These forces are the main contributor of the flow-induced vibrations that ...

    • Comparing Deep Learning Based Image Processing Techniques for Unsupervised Anomaly Detection in Offshore Wind Turbines 

      Keprate, Arvind; Sheikhi, Saeid; Siddiqui, Muhammad Salman (IEEE International Conference on Industrial Engineering and Engineering Management;, Chapter; Peer reviewed; Conference object, 2023)
      Offshore wind turbines (OWTs) play a crucial role in renewable energy generation, but their remote and harsh environments make them prone to various anomalies that can significantly affect their performance and reliability. ...

    • Effect of leading-edge erosion on the performance of offshore horizontal axis wind turbine using BEM method 

      Mian, Haris Hameed; Siddiqui, Muhammad Salman; Yang, Liang; Keprate, Arvind; Badar, Abdul Waheed (Peer reviewed; Journal article, 2023)
      This research focuses on the effect of leading-edge erosion on the performance of wind turbines, specifically the GE1.5XLE horizontal axis wind turbine. The blade element momentum (BEM) method is used to predict the ...

    • Estimation of effluent nutrients in municipal MBBR process 

      Komulainen, Tiina M.; Baqeri, A. Malik; Nermo, Einar; Keprate, Arvind; Saltnes, Torgeir; Jansen, Katrine M.; Korostynska, Olga (Peer reviewed; Journal article, 2023)
      The recently updated European Union’s Urban Waste Water Treatment Directive proposal, European Green Deal, Biodiversity Strategy for 2030, and EU’s Energy System Integration highlight a pressing need for innovative ...

    • Exploratory Data Analysis of the N-CMAPSS Dataset for Prognostics 

      Keprate, Arvind; Chatterjee, Supratik (IEEE International Conference on Industrial Engineering and Engineering Management;2021 IEEE International Conference on Industrial Engineering and Engineering Management (IEEM), Peer reviewed; Journal article, 2021-01-20)
      In the recent years, industries such as aeronautical, railway, and petroleum has transitioned from corrective/preventive maintenance to condition based maintenance (CBM). One of the enablers of CBM is Prognostics which ...

    • From Theory to Practice Leveraging Project Based Learning to Cultivate Student Engagement in Mechanical Engineering Education 

      Keprate, Arvind; Woodford, Sam; Borrajo, Rafael (IEEE International Conference on Industrial Engineering and Engineering Management;, Chapter; Peer reviewed; Conference object, 2024)
      This paper explores the transformative impact of Education 4.0 on learning experiences in the context of mechanical engineering education. Education 4.0 is an evolving paradigm that is student-centered, scalable, ...

    • Kvasir-Instruments and Polyp Segmentation Using UNet 

      Keprate, Arvind (Nordic Machine Intelligence (NMI);Vol. 1 No. 1 (2021): MedAI: Transparency in Medical Image Segmentation, Peer reviewed; Journal article, 2021-11-01)
      This paper aims to describe the methodology used to develop, fine-tune and analyze a UNet model for creating masks for two datasets: Polyp Segmentation and Instrument Segmentation, which are part of MedAI challenge. For ...

    • Limitations and Opportunities in PHM for Offshore Wind Farms: A Socio-Technical-Ecological System Perspective 

      Keprate, Arvind (Chapter, 2023)
      The burgeoning importance of offshore wind farms (OWFs) in the transition to sustainable energy systems underscores the need for effective Prognostics and Health Management (PHM) strategies. While the current PHM framework ...

    • Machine learning-based abnormality detection approach for vacuum pump assembly line 

      Garg, Paras; Patil, Amitkumar; Soni, Gunjan; Keprate, Arvind; Arora, Seemant (Reliability: Theory & Applications;Special Issue No 2(64), Volume 16, November 2021, Peer reviewed; Journal article, 2021-11-04)
      The fundamental basis of Industry 4.0 is to make the manufacturing sector more productive and autonomous. In the manufacturing sector, practitioners always long for product quality improvement, reducing reworking costs, ...

    • Multiscale Damage Modelling of Composite Materials Using Bayesian Network 

      Keprate, Arvind; Moslemian, Ramin (Lecture Notes in Civil Engineering;Volume 110, Conference object, 2020-12-13)
      Fibre reinforced polymers or as they widely are known composite materials, have made it possible to develop and manufacture large wind turbine and tidal blades central to competitiveness of wind and tidal turbines against ...

    • Predicting performance of in-situ microbial enhanced oil recovery process and screening of suitable microbe-nutrient combination from limited experimental data using physics informed machine learning approach 

      P.S., Pavan; Keprate, Arvind; Bagalkot, Nikhil; P, Sivasankar (Bioresource Technology;, Peer reviewed; Journal article, 2022)
      Screening of suitable microbe-nutrient combination and prediction of oil recovery at the initial stage is essential for the success of Microbial Enhanced Oil Recovery (MEOR) technique. However, experimental and physics-based ...

    • Predicting Remaining Fatigue Life of Topside Piping Using Deep Learning 

      Keprate, Arvind; Chatterjee, Supratik (International Conference on Applied Artificial Intelligence (ICAPAI);2021 International Conference on Applied Artificial Intelligence (ICAPAI), Conference object, 2021-06-29)
      Topside piping is the most commonly failed equipment in the Petroleum and Maritime industry. The prominent degradation mechanism causing piping failure is fatigue which results in unnecessary hydrocarbon release from these ...

    • Prediction of Stress Correction Factor for Welded Joints Using Response Surface Models 

      Keprate, Arvind; Donthi, Nikhil (ASME 2021 40th International Conference on Ocean, Offshore and Arctic Engineering;Volume 2: Structures, Safety, and Reliability, Conference object, 2021-10-11)
      While performing fatigue reliability analysis of the butt-welded joints it is vital to estimate the Stress Concentration Factor at these joints. A common approach adopted by industry to estimate the SCF at weld toes is to ...

    • Prognostics for Small Bore Piping Undergoing Fatigue Degradation 

      Keprate, Arvind; Bagalkot, Nikhil (IEEE International Conference on Industrial Engineering and Engineering Management;2022 IEEE International Conference on Industrial Engineering and Engineering Management (IEEM), Conference object, 2022)
      Prognostics of Small Bore Piping (SBP) degrading due to fatigue deals with estimating its remnant useful life (RUL). This manuscript elaborates the RUL prediction procedure for SBP. Physics-based model is utilized to ...

    • Reliability analysis of 15MW horizontal axis wind turbine rotor blades using fluid-structure interaction simulation and adaptive kriging model 

      Keprate, Arvind; Bagalkot, Nikhil; Siddiqui, Muhammad Salman; Sen, Subhamoy (Peer reviewed; Journal article, 2023)
      Over the course of the last four decades, the rotor diameter of Horizontal Axis Wind Turbines (HAWTs) has undergone a substantial increase, expanding from 15 m (30 kW) to an impressive 240 m (15MW), primarily aimed at ...

    • Residual Stress Prediction Of Welded Joints Using Gradient Boosting Regression 

      Keprate, Arvind; Bhardwaj, Sachin; RATNAYAKE MUDIYANSELAGE, Chandima; Ratnayake Mudiyanselage, Chandima (Communications in Computer and Information Science;Volume 1616, Conference object, 2022-07-23)
      Welding residual stress (WRS) estimation is highly nonlinear process due to its association with high thermal gradients generated during welding. Accurate and fast estimation of welding induced residual stresses in critical ...

    • Review of Weld Quality Classification Standard and Post Weld Fatigue Life Improvement Methods for Welded Joints 

      Bhardwaj, Sachin; Keprate, Arvind; Chandima Ratnayake, Mudiyanselage (Lecture Notes in Civil Engineering;Volume 110, Conference object, 2020-12-13)
      In typical fabricated structures, there are hundreds of joints, which acts as a potential location for fatigue cracking. Ageing and life extension (ALE) of such structures could be developed further by creating an optimal ...

    • Standards Ethics Legal Implications Challenges of Artificial Intelligence 

      Keprate, Arvind; Chauhan, Sanjana (Chapter; Peer reviewed; Conference object, 2022)
      We are moving towards an era of automation and technological revolution with Artificial Intelligence (AI) at its core. There is no doubt that AI has created commercial value across various industries such as e-commerce, ...