Dr. Prashant Kumar

Biographical Information

• Ph.D. (2017-2022), Indian Institute of Technology (Indian School of Mines), Dhanbad, Jharkhand, India

• Master of Technology in Electrical Engineering (2015-2017), Indian Institute of Technology (Indian School of Mines), Dhanbad, Jharkhand, India 

• Bachelor of Technology in Electrical Engineering (2010-2014), Biju Patnaik University of Technology, Rourkela, Odisha, India

Academic Experience

• May, 2022-currently, Research professor in Dongguk University, Seoul, Korea

Research Interests  

• Prognostics and Health Management (PHM)

• Application of Machine Learning in fault diagnosis of rotating machinery 

• Application of Deep Learning in fault diagnosis of rotating machinery

• Mine Winder Drives

• Mine Electrical Safety

• Fault detection in Electrical Drives

• Electrical Machines & Drives

AWARDS & HONORS 

• Awarded MOE (erstwhile M.H.R.D.) Fellowship during Master of Technology from July 2015 to May 2017.

PUBLICATIONS-INTERNATIONAL JOURNAL/ COMMUNICATED INTERNATIONAL JOURNALS

1. Prince, Hati, A.S., and Kumar, P., 2023. An Adaptive Neural Fuzzy Interface Structure Optimization for Prediction of Energy Consumption and Airflow of a Ventilation System. Applied Energy, 337, pp. 1-9.  DOI: https://doi.org/10.1016/j.apenergy.2023.120879 (SCIE | Q1 | IF: 11.44)

2. Raou, I., Kumar, P., Hyewon, L., and Kim, H.S., 2023. Transfer Learning-Based Intelligent Fault Detection Approach for the Industrial Robotic System. Mathematics, 11(4), pp. 1-14.  DOI: https://doi.org/10.3390/math11040945 (SCIE | Q1 | IF: 2.59)

3. Kumar, P., Kumar, P., Hati, A.S., and Kim, H.S., 2022. Deep transfer learning framework for bearing fault detection in motors. Mathematics, 10(24), pp. 1-14.  DOI: https://doi.org/10.3390/math10244683 (SCIE | Q1 | IF: 2.59)

4. Kumar, P. and Hati, A.S., 2022. Dilated Convolutional Neural Network Based Model for Bearing Faults and Broken Rotor Bar Detection in Squirrel Cage Induction Motors. Expert Systems with Applications, 191, pp. 1-12. DOI: https://doi.org/10.1016/j.eswa.2021.116290 (SCIE | Q1 | IF: 6.95)

5. Kumar, P. and Hati, A.S., 2021. Transfer learning-based deep CNN model for multiple faults detection in SCIM. Neural Computing and Applications, 33(22), pp. 15851–15862. DOI: https://doi.org/10.1007/s00521-021-06205-1 (SCIE | Q1 | IF: 5.60)

6. Kumar, P. and Hati, A.S., 2021. Convolutional neural network with batch normalisation for fault detection in squirrel cage induction motor. IET Electric Power Applications, 15(1), pp.39-50. DOI: https://doi.org/10.1049/elp2.12005 (SCIE | Q1 | IF: 2.56) 

7. Kumar, P. and Hati, A.S., 2021. Deep convolutional neural network based on adaptive gradient optimizer for fault detection in SCIM. ISA Transactions, 111, pp. 350-359. DOI: https://doi.org/10.1016/j.isatra.2020.10.052 (SCIE | Q1 | IF: 5.46) 

8. Kumar, P. and Hati, A.S., 2021. Review on machine learning algorithm based fault detection in induction motors. Archives of Computational Methods in Engineering, 28(3), pp.1929-1940. DOI: https://doi.org/10.1007/s11831-020-09446-w (SCIE | Q1 | IF: 7.30) 

9. Kumar, P., Sinha, A.K., and Chatterjee, T.K., 2016. An assessment of vibration monitoring as an effective tool for induction motor condition monitoring and fault diagnosis: A brief review. International Journal of Control Theory and Applications, 9(41), pp. 407 - 416. (SCOPUS) 

RESEARCH PAPER PRESENTED IN NATIONAL/ INTERNATIONAL CONFERENCES

1. Kumar, P., Hati, A.S., Padmanaban, S., Leonowicz, Z. and Chakrabarti, P., 2020, June. Amalgamation of Transfer Learning and Deep Convolutional Neural Network for Multiple Fault Detection in S.C.I.M. In 2020 IEEE International Conference on Environment and Electrical Engineering and 2020 IEEE Industrial and Commercial Power Systems Europe (EEEIC/I&CPS Europe) (pp. 1-6). IEEE. 

2. Sinha, A.K., Prince, Kumar, P. and Hati, A.S., 2020, December. ANN Based Fault Detection Scheme for Bearing Condition Monitoring in S.R.I.M.s using FFT, DWT and Band-pass Filters. In 2020 International Conference on Power, Instrumentation, Control and Computing (PICC) (pp. 1-6). IEEE. 

BOOK CHAPTER

1. Kumar, P. and Hati, A.S., 2022. Support Vector Classifier-Based Broken Rotor Bar Detection in Squirrel Cage Induction Motor. In Machines, Mechanism and Robotics (pp. 429-438). Springer, Singapore. . IEEE.