Surya Kandukuri works as a Senior Researcher at NORCE, in the DARWIN research group. He obtained his master's degree in control engineering from TU Delft in 2006 and PhD in condition monitoring from the University of Agder in 2018. Since 2007, he has been working in condition monitoring across energy, aerospace, marine, and digitalization sectors with GE Global Research, Airbus Defense and Space, Machine Prognostics AS, and Cognite AS, respectively. Through his stint in the industry, he developed remote monitoring and diagnostics solutions for several high-value equipments such as gas turbines, steam turbine components, heat exchangers, gasification units, aircraft electrical systems, and marine vessel propulsion systems. His current research includes the development of condition monitoring for machinery and structures with applications in wind, hydropower, fish farming, and rail infrastructure.
Surya Teja Kandukuri
Senior Researcher
suka@norceresearch.no
+47 486 44 655
Tullins gate 2, 0166 Oslo, Norway
Projects
Publications
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Academic articleSensitivity analysis for multi-measurement points based SHM in the mooring lines of floating offshore wind turbines– Journal of Physics: Conference Series (JPCS) 2024
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Academic articleA Systems Perspective on the Public Perception of Wind Power in Norway– INCOSE International Symposium 2024
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Academic articleSoft Ordering 1-D CNN to Estimate the Capacity Factor of Windfarms for Identifying the Age-Related Performance Degradation– Proceedings of the European Conference of the Prognostics and Health Management Society (PHME) 2024
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Academic articleFully Automated Diagnostics of Induction Motor Drives in Offshore Wind Turbine Pitch Systems Using Extended Park Vector Transform and Convolutional Neural Network– Proceedings of the European Conference of the Prognostics and Health Management Society (PHME) 2024
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Academic articleData driven approach for the management of wind and solar energy integrated electrical distribution network with high penetration of electric vehicles– Journal of Cleaner Production 2023
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Academic articleEstimation of Wind Turbine Performance Degradation with Deep Neural Networks– Proceedings of the European Conference of the Prognostics and Health Management Society (PHME) 2022
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Academic articleData Driven Seal Wear Classifications using Acoustic Emissions and Artificial Neural Networks– Proceedings of the European Conference of the Prognostics and Health Management Society (PHME) 2022
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Academic articleAutomated and Rapid Seal Wear Classification Based on Acoustic Emission and Support Vector Machine– Proceedings of the European Conference of the Prognostics and Health Management Society (PHME) 2021
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Academic articleA Novel Fault Indicator for Local Demagnetization in Fractional-Slot Permanent Magnet Synchronous Motor using Winding Function Theory– Proceedings of the European Conference of the Prognostics and Health Management Society (PHME) 2020
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Academic articleRapid Diagnosis of Induction Motor Electrical Faults using Convolutional Autoencoder Feature Extraction– Proceedings of the European Conference of the Prognostics and Health Management Society (PHME) 2020
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Academic articleSensitivity Analysis Of Online Oil Quality Monitoring For Early Detection Of Water Ingress In Marine Propulsion Systems– Proceedings of the European Conference of the Prognostics and Health Management Society (PHME) 2020
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Academic chapterDiagnosis of inverter-fed induction motors in short time windows using physics-assisted deep learning framework– 2019
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Academic chapterDiagnostics of stator winding failures in wind turbine pitch motors using Vold-Kalman filter– 2019
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Academic articleA Two-Stage Fault Detection and Classification Scheme for Electrical Pitch Drives in Offshore Wind Farms Using Support Vector Machine– IEEE transactions on industry applications 2019
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Academic chapterToward farm-level health management of wind turbine systems: status and scope for improvements– 2018
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Academic chapterFault diagnostics for electrically operated pitch systems in offshore wind turbines– 2018
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Academic chapterParameter Identification of a Winding Function Based Model for Fault Detection of Induction Machines– 2018
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Academic chapterMulti-Component Fault Detection in Wind Turbine Pitch Systems Using Extended Park's Vector and Deep Autoencoder Feature Learning– 2018
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Academic articleFault diagnostics of wind turbine electric pitch systems using sensor fusion approach– Journal of Physics: Conference Series (JPCS) 2018
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Academic chapterA two-stage fault detection and classification for electric pitch drives in offshore wind farms using support vector machine– 2017
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Academic chapterSensorless control of induction motors using an extended Kalman filter and linear quadratic tracking– 2017
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Academic articleCurrent signature based fault diagnosis of field-oriented and direct torque–controlled induction motor drives– Proceedings of the Institution of mechanical engineers. Part I, journal of systems and control engineering 2017
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Academic chapterEarly detection and classification of bearing faults using support vector machine algorithm– 2017
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Academic chapterEKF-based estimation and control of electric drivetrain in offshore pipe racking machine– 2016
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Academic articleFault Diagnostics for Electrically Operated Pitch Systems in Offshore Wind Turbines– Journal of Physics: Conference Series (JPCS) 2016
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Academic articleAssessment of synthetic winds through spectral modeling and validation using FAST– Journal of Physics: Conference Series (JPCS) 2016
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Academic literature reviewA review of diagnostics and prognostics of low-speed machinery towards wind turbine farm-level health management– Renewable and Sustainable Energy Reviews 2016
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Academic articleTowards farm-level health management of offshore wind farms for maintenance improvements– The International Journal of Advanced Manufacturing Technology 2015
