Rate My Professor Akhand Rai

Ahmedabad University

Engineering Mechanical Engineering Signal Processing Artificial Intelligence Machine Learning Instrumentation Engineering Acoustic Engineering Mechanics

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5.05/4/2026

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About Akhand

Akhand Rai serves as Assistant Professor at the School of Engineering and Applied Science, Ahmedabad University. He holds a PhD from the Indian Institute of Technology Roorkee and an M.Tech in Mechanical Engineering from IIT (BHU) Varanasi, where he secured the top rank and was awarded the IIT BHU-Varanasi Gold Medal in 2012. Rai commenced his career at General Electric Aviation in Bangalore, conducting high and low cycle fatigue analyses for jet engine externals and fuel-supply systems. He later held academic positions including Assistant Professor at Jaypee University of Engineering and Technology, Guna for 2.5 years, Visiting Assistant Professor at Thapar Institute of Engineering and Technology post-PhD, and Postdoctoral Professional Researcher at the University of Ulsan, South Korea, where he advanced fault detection technologies for pipelines and boiler tubes.

His research specializes in condition monitoring, rotating machinery dynamics, vibration analysis, fault diagnosis and prognosis of rolling element bearings and gears, pipeline leak diagnosis, battery management, and mental stress detection. Employing signal processing and artificial intelligence, Rai's work aims to minimize maintenance costs and avert failures in mechanical systems. He has published 16 papers in prestigious SCI journals like Mechanical Systems and Signal Processing, Measurement, Tribology International, Applied Soft Computing, and IEEE Transactions on Instrumentation and Measurement, plus 5 conference papers, garnering 864 citations, an h-index of 10, and i10-index of 10. Notable publications encompass "A novel health indicator for pipeline leak detection independent of prior failure information" (Measurement, 2021), "A Novel Pipeline Leak Detection Technique Based on Acoustic Emission Features and Two-Sample Kolmogorov-Smirnov Test" (Sensors, 2021), "A novel health indicator based on information theory features for assessing rotating machinery performance degradation" (IEEE Transactions on Instrumentation and Measurement, 2020), "The application of semi-nonnegative matrix factorization for detection of incipient faults in bearings" (Proceedings of the Institution of Mechanical Engineers, Part C, 2019), and a review on signal processing for rolling element bearing fault diagnosis (Tribology International, 2016). Rai's impactful contributions have positioned him in Stanford University's World's Top 2% Global Scientists list for 2021 in Mechanical Engineering and Transports, earned him third prize in DRDO's Dare to Dream 2.0 innovation contest for data-driven health monitoring of aero gas turbine engines, secured SERB funding for intelligent pipeline leak detection research, and a Best Paper Award for "Remaining Useful Life Prediction of Lithium-Ion Batteries using Artificial Intelligence" at the International Conference on Sustainable Energy and Clean Technologies (2022).