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Dr. Amar Kumar Verma
Post-Doctoral Fellow
Indian Institute of Technology Delhi

Post-Doctoral Status: November 2022 to till date

Department/Centre: Centre for Automotive Research & Tribology

Advisor: Prof. S. Fatima (CART, IIT Delhi)

Co-advisor: Prof. B. K. Panigrahi (Electrical Engineering, IIT Delhi)

Research Area: Machinery Health Monitoring, Deep Learning Techniques and Autonomous Ground Vehicles

Address: Block V-142/143, Automotive Health Monitoring Lab, CART, IIT Delhi


Other Links: 

  • Google Scholar
  • Research Gate
  • LinkedIn


  • Post-Doctoral Fellow (TiHAN, IIT Hyderabad, Telangana), December 2021 to October 2022

Educational Background

  • Ph.D. in Machinery Condition Monitoring (BITS Pilani, Hyderabad, Telangana), 2021

  • Master of Technology in Power and Energy (Amrita University, Kerala), 2017

  • Bachelor of Technology in Electronics and Engineering (JNTU Kakinada, Andhra Pradesh), 2015


Journal Publications

  1. Verma, A. K., Raval, P. D., Rajagopalan, N., Khariya, V., & Sudha, R. (2022). Development of an AI-based fsa for real‐time condition monitoring for industrial machine. Neural Computing and Applications, 1–19. 

  2. Rai, S., Saurabh, P., & Verma, A. K. (2022). GTN Damage Modelling of the AA6063 Using Image Processing. Journal of Brilliant Engineering, 1, 4400.

  3. Saurabh, P., Verma, A. K., Rai, S., & Singla, S. (2021). Study of gene‐ gene interaction networks. Journal of Nature, 4, 16–18. 

  4. Verma, A. K., Nagpal, S., Desai, A., & Sudha, R. (2021). An efficient neural‐network model for real‐time fault detection in industrial machine. Neural Computing and Applications, 33(4), 1297–1310. 

  5. Verma, A. K., & Radhika, S. (2021). Multi‐level stator winding failure analysis on the insulation material for industrial induction motor. Experimental techniques, 1–15. 

  6. Verma, A. K., Vamsi, I., Saurabh, P., Sudha, R., Sabareesh, G., & Rajkumar, S. (2021). Wavelet and deep learning‐based detection of sars‐ncov from thoracic x‐ray images for rapid and efficient testing. Expert Systems with Applications, 185, 115650. 

  7. Verma, A. K., Akkulu, P., Padmanabhan, S. V., & Radhika, S. (2020). Automatic condition monitoring of industrial machines using FSA‐based hall‐effect transducer. IEEE Sensors Journal, 21(2), 1072–1081. 

Conference Proceedings

  1. Verma, A. K., Fatima, S., Panigrahi, B. K., 2023, A reliable framework for predicting wind turbine failures utilising SCADA and Alarm data, 5th International Conference on System Reliability and Safety Engineering (SRSE), Beijing, China, 20-23 October 2023.

  2. Verma, A. K., Vaibhav, R., Perabhattula, V., & Rajalakshmi, P. (2022). Development of Lane Departure Warning System and SiL Testing for AV Systems in India (No. 2022-28-0117). SAE Technical Paper.

  3. Verma, A. K.., Radhika, S., & Surampudi, N. (2020). Web based application for quick and handy health condition monitoring system for a reliable wind power generation in Asme international mechanical engineering congress and exposition (Vol. 84669, V014T14A009). American Society of Mechanical Engineers. 

  4. Ranjan, G., Verma, A. K., & Radhika, S. (2019). K‐nearest neighbors and grid search cv based real time fault monitoring system for industries in 2019 ieee 5th international conference for con‐ vergence in technology (i2ct) (pp. 1–5). IEEE. 

  5. Vamsi, I. V., Abhinav, N., Verma, A. K., & Radhika, S. (2018). Random forest based real time fault monitoring system for indus‐ tries in 2018 4th international conference on computing communi‐ cation and automation (iccca) (pp. 1–6). IEEE. 

  6. Verma, A. K., Radhika, S., & Padmanabhan, S. (2018). Wavelet based fault detection and diagnosis using online mcsa of stator winding faults due to insulation failure in industrial induction machine in 2018 ieee recent advances in intelligent computational systems (raics) (pp. 204–208). IEEE.

Book Chapter

  1. Verma, A. K., Vinod, J. V., & Sudha, R. (2021). A modular zigbee-based IoT platform for reliable health monitoring of industrial machines using refsa. In Microelectronics and Signal Processing (pp. 179-188). CRC Press.

  2. Verma, A. K., Jain, A., & Sudha, R. (2020). Neuro‐fuzzy classifier for identification of stator winding inter‐turn fault for industrial machine. In International conference on modelling, simulation and intelligent computing (pp. 101–110). Springer, Singapore.

  3. Verma, A. K., Spandana, P., Padmanabhan, S., & Radhika, S. (2019). Quantitative modeling and simulation for stator inter‐turn fault detection in industrial machine. In International conference on intelligent computing and communication (pp. 87–97). Springer, Singapore. 

Patent Filed

  1. A System and Method for Real‐Time Monitoring of a Health Condition of a Machine” Radhika Sudha, Amar Kumar Verma, Naren Surampudi: Application No. 202111022555.

  2. Automatic Health Condition Detection System, Radhika Sudha, Amar Kumar Verma, Naren Surampudi, Prerna Saurabh, Inturi Vamsi, Sabareesh G R, Rajkumar S: Application No. 202211054480.

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