Ranjan Sasti Charan Pal

Ranjan Sasti Charan Pal is a dedicated academician and a researcher specializing in mechanical engineering. He obtained his BE degree in Mechanical Engineering from the Maharashtra Institute of Technology, Pune in 2015. Moving further in his academic career, he completed his MTech in Machine Design at the National Institute of Technology (NIT), Durgapur, where he graduated as a Gold Medallist in 2018.

Driven by his commitment towards academics, Ranjan enrolled in the prestigious Ph.D. program at the Indian Institute of Technology (IIT), Kharagpur. His doctoral research focuses on fault diagnosis and prognosis of electrical systems. He has published research papers in peer-reviewed journals and presented his findings at national and international conferences. Following the submission of his Ph.D. thesis, Ranjan joined NAMTECH as an Assistant Professor in the field of Smart Manufacturing.

Before embarking on his doctoral studies, Ranjan gained valuable industrial experience as a Productivity Improvement Engineer at Sandvik Asia Private Limited in Pune, India.

During his leisure time, he takes pleasure in dancing, drawing, making portraits, and singing.

2018 – 2024 PhD

Acoustics and Condition Monitoring Lab, Department of Mechanical Engineering, Indian Institute of Technology Kharagpur

2016 – 2018 MTech Machine Design

National Institute of Technology, Durgapur

2011 – 2015 B.E Mechanical Engineering

Maharashtra Institute of Technology, Pune

July 2024 to present

Assistant Professor at NAMTECH, an education initiative of ArcelorMittal Nippon Steel India, Research Park, IIT Gandhinagar Campus, Gujarat

July 2019 to June 2024

Teaching Assistant at Indian Institute of Technology Kharagpur.
Teaching Assistant at NPTEL (National Programme on Technology Enhanced Learning).

July 2015 to June 2016

Productivity Improvement Engineer at Sandvik Asia Private Limited.

R. S. C. Pal and A. R. Mohanty, “Identification between broken rotor bars and low-frequency load torque fluctuations in a three-phase induction motor”, International Journal of Automation and Control (IJAAC), vol. 18, no. 2, pp. 133–160, 2024.
R. S. C. Pal, N. Dewangan, and A. R. Mohanty, “Fault diagnosis of induction motors: An architecture for real time assessment as cyber-physical system”, Engineering Transactions, vol. 71, no. 1, pp. 23–42, 2023.
R. S. C. Pal and A. R. Mohanty, “Remaining Useful Life Prediction of Bearings based on Cox Hazard Model”, 29th International Congress on Sound and Vibration (ICSV), 9-13 July 2023, Prague.
R. S. C. Pal and A. R. Mohanty, “Bearing Fault Detection In Permanent Magnet Synchronous Motors Using Vibration and Motor Current Signature Analysis”, 28th International Congress on Sound and Vibration (ICSV), 24-28 July 2022, Sands Expo & Convention Centre, Singapore.
R. S. C. Pal and A. R. Mohanty, “A Simplified Dynamical Model of Mixed Eccentricity Fault in a Three-Phase Induction Motor”, IEEE Transactions on Industrial Electronics, vol. 68, no. 5, pp. 4341-4350, May 2021.
R. S. C. Pal, N. B. Hui, J. P. Davim, “Analysis and optimization of truss structures, constrained handling using genetic algorithm”, Journal of Applied and Computational Mechanics, vol. 7, pp. 1324-1333, 2021.
A. R. Mohanty and R. S. C. Pal, “A cyber-physical system based real-time fault diagnosis of induction motors”, 13th International Workshop on Structural Health Monitoring (IWSHM), at Stanford University, CA, 2021.
R. S. C. Pal and A. R. Mohanty, “Dynamical Modelling of Three-Phase Induction Motor with Broken Rotor Bars”, National Conference on Condition Monitoring, 20-21 September 2019, KL University, Vaddeswaram, India.
C. Mahapatra, R. S. C. Pal, A. R. Mohanty, “Localization of sound source by using delay estimators on signals from microphone arrays”, 26th International Congress on Sound and Vibration (ICSV), 7-11 July 2019, Montreal, Canada.

Innovated a Python-based Windows application for evaluating the real-time fault diagnosis and power quality of electrical assets, enhancing the reliability of motors, generators, and transformers in industrial facilities.
Innovated algorithms for estimating the remaining useful life of electrical assets, utilizing statistical and machine-learning methods.
Developed a simplified dynamical model for air-gap eccentricity fault detection in electric motors through motor current signature analysis.
Distinguished between broken rotor bars and low-frequency load torque fluctuations in motors through motor current signature analysis.
Identified bearing faults in permanent magnet synchronous motors using a combination of motor current and vibration analysis.