Specialization

Asset Performance and Reliability, Rotor Dynamics, Condition Monitoring, Vibration Analysis, Artificial Intelligence, Gas Turbine/ Steam Turbine Operation and Maintenance

Email

sudhar.rajagopalan@thapar.edu

Contact No.

+91-9994358014

Biography

Dr. Sudhar Rajagopalan, an Assistant Professor (Research) in the Department of Mechanical Engineering at Thapar Institute of Engineering & Technology (TIET), Patiala, has a distinguished research profile in rotor dynamics and vibration analysis. With over 17 years of experience in the oil and gas industry, he has played Asset Performance and Reliability key roles in organizations such as Petrofac (Singapore), OQ (Oman), PDO (Oman), IOCL (India), and ArcelorMittal (India), driving significant improvements in asset reliability, predictive maintenance, and cost optimization. He holds a Ph.D. from Thapar Institute of Engineering & Technology, specializing in vibration-based rotor fault detection using deep learning. A Certified Machinery Vibration Diagnostic Engineer (Vibration Institute, USA), Dr. Sudhar is a leading authority in vibration analysis, root cause failure analysis, and predictive maintenance for rotating equipment such as turbines, pumps, compressors, and motors. Beyond his industrial achievements, he is an accomplished researcher with several SCI-indexed publications in machine learning-driven fault diagnostics. He is also a dedicated mentor and trainer, sharing his expertise in condition monitoring, reliability engineering, and advanced asset management strategies.

Academic Profile

• Ph.D.  (M.Tech Integrated Course) in Mechanical Engineering, Thapar Institute of Engineering and Technology (Patiala), Punjab. India
  • Jan 2018 – Jan 2025
• B.E. in Mechanical Engineering, Anna University, Chennai, India,
  • 2004-2007

Work Experience

• Reliability & Asset Performance Management (Petrofac, Singapore) July 2024
• Reliability & Asset Performance Management (OQ, Oman) Sep 2022- Jun 2024
• Reliability & Condition Monitoring Specialist (Petroleum Development Oman, Oman) Aug 2014 to Aug 2022
• Reliability & Condition Monitoring (Indian Synthetic Rubber Ltd., Panipat, India) Aug 2013 to Aug 2014
• Reliability Centered Maintenance (Essar Steel, Gujarat) Oct 2007 to Jul 2013

Research Interests

• Asset Management
• Reliability
• Engineering Design and Analysis
• Turbine Technology
• Machine Learning
• Deep Learning
• Fault Prediction
• Vibration Analysis and Control

Research Publications

SCI (5)

1. Rajagopalan, S., Singh, J., & Purohit, A. (2024). VMD-based ensembled SMOTEBoost for imbalanced multi-class rotor mass imbalance fault detection and diagnosis under industrial noise. Journal of Vibration Engineering & Technologies, 12(2), 1457-1478. (SCI Indexed)
2. Rajagopalan, S., Singh, J., & Purohit, A. (2024). Performance analysis of genetically optimized 1D convolutional neural network architecture for rotor system fault detection and diagnosis. Proceedings of the Institution of Mechanical Engineers, Part E: Journal of Process Mechanical Engineering, 09544089241235707. (SCI Indexed)
3. Rajagopalan, S., Purohit, A., & Singh, J. (2024). Genetically optimised SMOTE-based adversarial discriminative domain adaptation for rotor fault diagnosis at variable operating conditions. Measurement Science and Technology, 35(10), 106109. (SCI Indexed) 
4. Arora, J. K., Rajagopalan, S., Singh, J., & Purohit, A. (2024). Low-frequency adaptation-deep neural network-based domain adaptation approach for shaft imbalance fault diagnosis. Journal of Vibration Engineering & Technologies, 12(1), 375-394. (SCI Indexed)
5. Yadav, A., Rajagopalan, S., Purohit, A., & Singh, J. (2023). Variable dropout one-dimensional CNN for vibration-based shaft unbalance detection in industrial machinery. Journal of Vibration Engineering & Technologies, 11(1), 301-318. (SCI Indexed)
6. Rajagopalan, S., Purohit, A., & Singh, J. (2021, December). A Systematic Review of Rotor Unbalance Diagnosis in Rotating Machinery Based on Machine Learning Algorithms. In International Conference on Vibration Engineering and Technology of Machinery (pp. 281-300). Singapore: Springer Nature Singapore. (SCOPUS Indexed)

Course of Interest

• • Vibration Analysis
  • Condition Monitoring
  • Reliability Engineering
  • Maintenance & Operation

Research Profile

https://scholar.google.com/citations?user=-yOyVk0AAAAJ&hl=en&oi=ao