Dr. Suvendu Mohanty | Health Monitoring | Best Sensor for Health Monitoring Award

Dr. Suvendu Mohanty, Indian Institute of Technology Madras, India

Dr. Suvendu Mohanty is a Postdoctoral Researcher in Mechanical Engineering at the Indian Institute of Technology Madras, specializing in machine health monitoring, predictive maintenance, and remaining useful life (RUL) estimation of mechanical systems. He holds a Ph.D. in Production Engineering from NIT Agartala, with a research focus on failure prediction of CNG-driven engines. With over a decade of academic and research experience, including prior roles as Assistant Professor, Dr. Mohanty has led and contributed to high-impact projects in collaboration with industry giants such as Walmart Inc. and Honeywell International Inc. His interdisciplinary expertise spans wear analysis, tribology, AI-driven diagnostics, and multi-sensor data fusion. A prolific researcher and active contributor to conferences and workshops, he is passionate about translating research into real-world engineering solutions that enhance reliability and sustainability in industrial systems.

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Nominee: Dr. Suvendu Mohanty
Designation: Postdoctoral Researcher, Mechanical Engineering
Institution: Indian Institute of Technology Madras, India

Dr. Suvendu Mohanty is an exceptional candidate for the Best Sensor for Health Monitoring Award, recognized for his impactful research in multi-sensor data fusion, predictive diagnostics, and machine health monitoring systems. His work lies at the critical intersection of mechanical engineering, artificial intelligence, and sensor-based prognostics, directly advancing the field of health monitoring technologies for both machines and potential extensions to biomedical systems

🎓 Education

Ph.D. in Production Engineering
🏫 National Institute of Technology (NIT) Agartala, India | 📅 2024
📚 Thesis: Failure Prediction of Engine Driven by CNG Through Prognostic Approach
📊 CGPA: 8.93/10
M.Tech. in Thermal Engineering
🏫 NIT Patna, India | 📅 2013
📚 Thesis: Analysis of Exhaust Emission of Internal Combustion Engine Using Biodiesel Blend
📊 CGPA: 7.73/10
B.Tech. in Mechanical Engineering
🏫 Bhadrak Institute of Engineering & Technology (BIET), Odisha, India | 📅 2011
📚 Thesis: Turbulent Fluid Flow & Heat Transfer in Mixing Junction Using Gambit and Fluent
📊 CGPA: 7.32/10

🛠️ Work Experience

🔬 Postdoctoral Researcher
🏢 Engineering Asset Management Group, Mechanical Engineering, IIT Madras
📅 Aug 2024 – Present
✅ Focus: Predictive maintenance, multi-sensor data integration, AI-based diagnostics
🤝 Industrial Collaborations: Walmart Inc., Honeywell International Inc.
👨‍🏫 Assistant Professor
🏫 Hi-Tech Institute of Technology, Bhubaneswar
📅 June 2013 – July 2015
🧪 Courses: IC Engine, Thermodynamics, Heat Transfer
👨‍🏫 Assistant Professor
🏫 Gandhi Institute for Education and Technology, Bhubaneswar
📅 Aug 2015 – Dec 2016
🧪 Courses: Mechanical Measurements, Heat Transfer, Labs

🏆 Achievements

🔧 Successfully executed multiple research collaborations with Honeywell and Walmart Inc. on predictive maintenance and diagnostics.
📊 Developed AI-integrated health monitoring systems for rotating machinery and induction motors.
📝 Published and presented several research papers in national seminars and workshops.
🧪 Led experimental diagnostics on bearing systems using the Honeywell Versatile Transmitter (HVT) system.

🥇 Awards & Honors

🧭 Postdoctoral Research Fellowship, IIT Madras (2024–Present)
🏅 Organising Committee Member – International Conference on Next Generation Technologies: Design and Manufacturing (ICNGT), IIT Madras, Nov 2024
🏅 Organising Member, FFMA-2012, NIT Patna
🧠 National Cyber Olympiad Participant – Science Olympiad Foundation
🎤 Seminar Presenter – RTMERAF at the Institution of Engineers, Tripura
🎓 Workshop Participation – CTSR and ECED programs at NIT Agartala

Publication Top Notes:

Maintenance analytics for achieving sustainability using CNG as alternative fuel

A frame work for comparative wear based failure analysis of CNG and diesel operated engines

Application of Artificial Intelligence for Failure Prediction of Engine Through Condition Monitoring Technique

Fractal mathematics applications for wear image analysis of engines using biofuels

Artificial Neural Network coupled Condition Monitoring for advanced Fault Diagnosis of Engine

Experimental Investigation of Tribo-Corrosive Nature of Biodiesel and its Effect on Lubricating System

Intelligent prediction of engine failure through computational image analysis of wear particle

Importance of Tribological study for Internal Combustion Engines using Biofuel

Prof Dr. Isak Shabani, Faculty of Electrical and Computer Engineering, University of Prishtina, Albania

Isak Shabani, born on November 3, 1976, in Albania, is a distinguished Full Professor of Computer Engineering at the University of Prishtina’s Faculty of Electrical and Computer Engineering. He obtained his Bachelor’s degree in Computing and Telecommunication in 2002, followed by a Master’s degree in Computer Engineering in 2007, and a Ph.D. in Computer Sciences in 2012 from the same institution. Prof. Shabani’s research interests encompass a wide range of topics, including big data analytics, artificial intelligence, and cloud computing, with a particular focus on applications in government and healthcare. He has published extensively, with 58 scientific papers, 28 textbook chapters, and 128 abstracts to his name. His notable contributions include studies on IoT devices for health monitoring, student dropout rates, and electronic voting systems utilizing blockchain technology.

Education

PhD in Computer Sciences
University of Prishtina, Faculty of Electrical and Computer Engineering
Date: June 2, 2012

Magister of Computer Engineering
University of Prishtina, Faculty of Electrical and Computer Engineering
Date: July 7, 2007

Computing and Telecommunication
University of Prishtina, Faculty of Electrical and Computer Engineering
Date: April 17, 2002

Work Experience

Full Professor
University of Prishtina, Faculty of Electrical and Computer Engineering “Hasan Prishtina”
Date: September 10, 2022 – Present

[Previous Academic Positions]
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