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Dr. Parth Sarathi Panigrahy

Assistant Professor

Educational Qualification

Ph.D (IIEST Shibpur), M.E (IIEST Shibpur)

Experience

Teaching: 8.8 years

Area of Interest

Condition Monitoring of Electrical Machines, Signal Processing, Artificial Intelligence

Date of Joining at RVCE

05/09/2022

Email ID

parthsarathip@rvce.edu.in

Publications:

International Journals        : 04

International Conferences: 06

Book Chapters       : 02

List of Publications:

International Journals:

  1. P. Panigrahy and P. Chattopadhyay (2021) "Tri-axial vibration based collective feature analysis for decent fault classification of VFD fed induction motor", Measurement (Elsevier), vol. 168, 2021, Article- 108460.

  2. P. Panigrahy, D. Santra and P. Chattopadhyay (2020) “Decent fault classification of VFD fed induction motor using random forest algorithm”, Artificial Intelligence for Engineering Design,Analysis and Manufacturing (Cambridge University Press), 34(4), 492-504.

  3. P. Panigrahy and P. Chattopadhyay (2018) "Cascaded signal processing approach for motor fault diagnosis", COMPEL - The international journal for computation and mathematics in electrical and electronic engineering (Emerald Publishing Limited), vol. 37, no. 6, 2018, pp. 2122-2137.

  4. P. Panigrahy and P. Chattopadhyay (2018) "Improved Classification by Non Iterative and Ensemble Classifiers in Motor Fault Diagnosis", Advances in Electrical and Computer Engineering, vol. 18, no. 1, 2018, pp. 95-104.

International Conference:

  1. R. Mukhopadhyay, P. S. Panigrahy, G. Misra and P. Chattopadhyay, "Quasi 1D CNN-based Fault Diagnosis of Induction Motor Drives," 2018 5th International Conference on Electric Power and Energy Conversion Systems (EPECS), Kitakyushu, Japan, 2018, pp. 1-5.

  2. R. Bhadra, S. Dutta, A. Kedia, S. Gupta, P. Panigrahy, P. Chattopadhyay, “Applied Machine Learning for Bearing Fault Prognostics” IEEE Conference on Applied Signal Processing, Dec 7-9, 2018.

  3. P. Panigrahy, D. Santra and P. Chattopadhyay, “Feature engineering in fault diagnosis of induction motor”, 3rd International Conference on Condition Assessment Techniques in Electrical Systems (CATCON), 2017, pp. 306-310.

  4. P. S. Panigrahy, S. Mitra, P. Konar, P. Chattopadhyay, “FPGA Friendly Fault Detection Technique for Inverter Fed Induction Motor” 2nd International Conference on Control, Instrumentation, Energy & Communication (CIEC-2016), January 28 - 30, 2016, pp. 120-124.

  5. P. S. Panigrahy P. Konar, P. Chattopadhyay, “Application of Data Mining in Fault Diagnosis of Induction Motor” IEEE First International Conference on Control, Measurement and Instrumentation (CMI-2016), January 8-10, 2016, pp. 274 – 278.

  6. P. S. Panigrahy, P.Konar, P.Chattopadhyay, “Broken Bar Fault Detection using Fused DWT- FFT in FPGA Platform”, International Conference on Power, Control and Embedded Systems (ICPCES2014), December 28-29, 2014.

Book Chapters:

  1. P. S. Panigrahy, B. Sai Reddy and M. R. Harika (2022) “Development of fuel cell based energy systems for 3-ph power development & Internet of Things devices”, Chapter-10, in “ Internet of Things and Data Mining for Modern Engineering and Healthcare Applications (1st ed.)”. Chapman and Hall/CRC. https://doi.org/10.1201/9781003217398

  2. P. Konar, P. S. Panigrahy, P. Chattopadhyay (2015) “Tri-Axial Vibration Analysis using Data Mining for Multi class Fault Diagnosis in Induction Motor”, In: Prasath R., Vuppala A., Kathirvalavakumar T. (eds) Mining Intelligence and Knowledge Exploration. Lecture Notes in Computer Science, vol 9468. Springer, Cham.

 

Interaction with outside world:

Awards:

  1. Awarded Institute Fellowship (Senior Research Fellowship) from Indian Institute of Engineering Science and Technology, Shibpur.

  2. Received Best Paper award in IEEE International Conference on Control, Instrumentation, Energy & Communication (CIEC)-2016, from “Fault Analysis” section.

Notifications / CIrculars