Contact Dr Ravi Pandit
- Tel: +44 (0) 1234 758471
- Email: Ravi.Pandit@cranfield.ac.uk
- Twitter: @RaviPan86371323
- ORCID
- Google Scholar
- ResearchGate
Background
Dr Ravi Pandit is currently a Lecturer in Instrumentation and AI at the Centre for Life-cycle Engineering and Management within the School of Aerospace, Transport and Manufacturing, Cranfield University. Prior to that, he held several academic posts such as Research Fellow (the University of Exeter, 2020-2021), Research Associate (the University of Strathclyde, 2016-2020), Visiting Researcher (UCLM Spain, 2018), Visiting Researcher (Wood plc Scotland, 2016-2017), Assistant Professor(Electronics and Instrumentation Engineering, Jadavpur University. 2014-2016), Assistant Professor(School of Electrical Engineering, VIT Vellore, 2011-2014) respectively. During his professional career, he received a number of prestigious and highly competitive research awards/fellowships, for example, Travel Grant (US$3.5k), Marie Curie Fellowship (of value £224,325.00), Erasmus Mundas (of value ~ 129,000). As of now, has published numerous papers in highly ranked journals (mostly in Q1 & Q2) and have presented my research at several international conferences and workshops in these areas. Overall, he has more than 10 years of academic and research experience; with various reputed academics and industries through a number of interdisciplinary projects.
Research opportunities
Dr Ravi Pandit is interested in supervising PhD, and MSc students with a research interest in:
Data analytics and Machine Learning for clean energy technologies (e.g., wind and solar)
Data analytics and Machine Learning for railways
Descriptive, predictive and prescriptive analytics
Online condition-based monitoring
Big data statistical analysis: feature analysis, computation, modelling (time-series)
Digital twins and the Internet of Things (IoT) for Offshore wind
Please get in touch if you would like to collaborate or work on these areas.
Opportunities
Dr Ravi Pandit is currently recruiting for PhD (self-funded) and Post-Doc (through fellowship scheme e.g., RAEng, UKRI, etc) in these above areas. A highly motivated top-quality candidate (both home and international) requested to send a detailed CV and your top 1-2 papers (for post-doc) for discussion.
Current activities
Ravi's main research interests include offshore wind energy covering predictive maintenance, digital twins, Internet of Things (IoT), online condition monitoring, SCADA and vibration data analysis and remaining useful life (RUL) estimation. With a background in data analytics and machine learning and clean energy technologies, he is interested in understanding the digitalisation aspect of clean energy technologies and how state-of-art digital technologies can improve decision management, automate the process and reduce overall costs.
Publications
Articles In Journals
- Pandit R & Wang J. (2024). A comprehensive review on enhancing wind turbine applications with advanced SCADA data analytics and practical insights. IET Renewable Power Generation, 18(4)
- Pandit R, Infield D & Santos M. (2023). Accounting for Environmental Conditions in Data-Driven Wind Turbine Power Models. IEEE Transactions on Sustainable Energy, 14(1)
- Astolfi D, Pandit R, Lombardi A & Terzi L. (2023). Multivariate Data-Driven Models for Wind Turbine Power Curves including Sub-Component Temperatures. Energies, 16(1)
- Castellani F, Pandit R, Natili F, Belcastro F & Astolfi D. (2023). Advanced Methods for Wind Turbine Performance Analysis Based on SCADA Data and CFD Simulations. Energies, 16(3)
- Pandit RK, Astolfi D & Durazo Cardenas I. (2023). A Review of Predictive Techniques Used to Support Decision Making for Maintenance Operations of Wind Turbines. Energies, 16(4)
- Hadjoudj Y & Pandit RK. (2023). Improving O&M decision tools for offshore wind farm vessel routing by incorporating weather uncertainty. IET Renewable Power Generation, 17(6)
- Astolfi D, Pandit R, Lombardi A & Terzi L. (2023). Diagnosis of wind turbine systematic yaw error through nacelle anemometer measurement analysis. Sustainable Energy, Grids and Networks, 34(June)
- Minguell MG & Pandit R. (2023). TrackSafe: A comparative study of data-driven techniques for automated railway track fault detection using image datasets. Engineering Applications of Artificial Intelligence, 125(October)
- Pandit R & Xie W. (2023). Data‐driven models for predicting remaining useful life of high‐speed shaft bearings in wind turbines using vibration signal analysis and sparrow search algorithm. Energy Science & Engineering, 11(12)
- Hadjoudj Y & Pandit R. (2023). A review on data‐centric decision tools for offshore wind operation and maintenance activities: Challenges and opportunities. Energy Science & Engineering, 11(4)
- Pandit R, Astolfi D, Hong J, Infield D & Santos M. (2023). SCADA data for wind turbine data-driven condition/performance monitoring: A review on state-of-art, challenges and future trends. Wind Engineering, 47(2)
- Astolfi D & Pandit R. (2022). Multivariate Wind Turbine Power Curve Model Based on Data Clustering and Polynomial LASSO Regression. Applied Sciences, 12(1)
- Sierra-Garcia JE, Santos M & Pandit R. (2022). Wind turbine pitch reinforcement learning control improved by PID regulator and learning observer. Engineering Applications of Artificial Intelligence, 111(May)
- Astolfi D, Pandit R, Celesti L, Vedovelli M, Lombardi A, .... (2022). Data-Driven Assessment of Wind Turbine Performance Decline with Age and Interpretation Based on Comparative Test Case Analysis. Sensors, 22(9)
- Astolfi D, Pandit R, Celesti L, Lombardi A & Terzi L. (2022). SCADA data analysis for long-term wind turbine performance assessment: A case study. Sustainable Energy Technologies and Assessments, 52(D)
- Sacie M, Santos M, López R & Pandit R. (2022). Use of State-of-Art Machine Learning Technologies for Forecasting Offshore Wind Speed, Wave and Misalignment to Improve Wind Turbine Performance. Journal of Marine Science and Engineering, 10(7)
- Astolfi D & Pandit R. (2022). Wind Turbine Performance Decline with Age. Energies, 15(14)
- Astolfi D, Pandit R, Terzi L & Lombardi A. (2022). Discussion of Wind Turbine Performance Based on SCADA Data and Multiple Test Case Analysis. Energies, 15(15)
- Pandit R, Astolfi D, Tang AM & Infield D. (2022). Sequential Data-Driven Long-Term Weather Forecasting Models’ Performance Comparison for Improving Offshore Operation and Maintenance Operations. Energies, 15(19)
- Astolfi D, Pandit R, Gao L & Hong J. (2022). Individuation of Wind Turbine Systematic Yaw Error through SCADA Data. Energies, 15(21)
- Pandit R, Infield D & Dodwell T. (2021). Operational Variables for Improving Industrial Wind Turbine Yaw Misalignment Early Fault Detection Capabilities Using Data-Driven Techniques. IEEE Transactions on Instrumentation and Measurement, 70
- Pandit R & Kolios A. (2020). SCADA Data-Based Support Vector Machine Wind Turbine Power Curve Uncertainty Estimation and Its Comparative Studies. Applied Sciences, 10(23)
- Richmond M, Sobey A, Pandit R & Kolios A. (2020). Stochastic assessment of aerodynamics within offshore wind farms based on machine-learning. Renewable Energy, 161
- Pandit RK, Infield D & Kolios A. (2020). Gaussian process power curve models incorporating wind turbine operational variables. Energy Reports, 6
- Pandit RK, Kolios A & Infield D. (2020). Data‐driven weather forecasting models performance comparison for improving offshore wind turbine availability and maintenance. IET Renewable Power Generation, 14(13)
- Pandit RK & Infield D. (2019). SCADA based nonparametric models for condition monitoring of a wind turbine. The Journal of Engineering, 2019(18)
- Pandit RK, Infield D & Kolios A. (2019). Comparison of advanced non‐parametric models for wind turbine power curves. IET Renewable Power Generation, 13(9)
- Pandit RK & Infield D. (2019). Comparative analysis of Gaussian Process power curve models based on different stationary covariance functions for the purpose of improving model accuracy. Renewable Energy, 140
- Pandit RK, Infield D & Carroll J. (2019). Incorporating air density into a Gaussian process wind turbine power curve model for improving fitting accuracy. Wind Energy, 22(2)
- Pandit RK & Infield D. (2019). Comparative assessments of binned and support vector regression-based blade pitch curve of a wind turbine for the purpose of condition monitoring. International Journal of Energy and Environmental Engineering, 10(2)
- Pandit RK & Infield D. (2018). Comparative analysis of binning and Gaussian Process based blade pitch angle curve of a wind turbine for the purpose of condition monitoring. Journal of Physics: Conference Series, 1102(1)
- Pandit R & Infield D. (2018). Gaussian Process Operational Curves for Wind Turbine Condition Monitoring. Energies, 11(7)
- Pandit RK & Infield D. (2018). SCADA‐based wind turbine anomaly detection using Gaussian process models for wind turbine condition monitoring purposes. IET Renewable Power Generation, 12(11)
- Kumar Pandit R & Infield D. (2018). Performance Assessment of a Wind Turbine Using SCADA based Gaussian Process Model. International Journal of Prognostics and Health Management, 9(1)
Conference Papers
- Garcia-Vaca MA, Sierra-García JE, Santos M & Pandit R. (2023). Modelling Wind Turbine Power Curves based on Frank’s copula
- Astolfi D, Gao L, Pandit R & Hong J. (2023). Experimental Analysis Of The Effect Of Static Yaw Error On Wind Turbine Nacelle Anemometer Measurements
- Rengel J, Santos M & Pandit R. (2022). EfficientNet Architecture Family Analysis on Railway Track Defects
- Kolios A, Walgern J, Koukoura S, Pandit R & Chiachio-Ruano J. (2020). openO&M: Robust O&M open access tool for improving operation and maintenance of offshore wind turbines
- Kolios A, Walgern J, Koukoura S, Pandit R & Chiachio-Ruano J. (2019). Open O&M: Robust O&M Open Access Tool for Improving Operation and Maintenance of Offshore wind Turbines
- Pandit R & Infield D. (2018). Comparative analysis of binning and support vector regression for wind turbine rotor speed based power curve use in condition monitoring
- Pandit RK & Infield D. (2018). Power curve modeling using support vector machine and its accuracy dependence on kernel scale
- Pandit RK & Infield D. (2018). QQ plot for assessment of Gaussian process wind turbine power curve error distribution function
- Pandit RK & Infield D. (2017). Using Gaussian process theory for wind turbine power curve analysis with emphasis on the confidence intervals
- Pandit R & Boland M. (2013). The impact on staff efficiency of implementing a DICOM-compatible workflow in an academic ophthalmology practice
- PANDIT R, FORGACS G & RUJAN P. (1981). FINITE-SIZE CALCULATIONS FOR THE KINETIC ISING-MODEL