Rate My Professor Pratesh Jayaswal

Dr. Pratesh Jayaswal is a Professor in the Department of Mechanical Engineering at Madhav Institute of Technology & Science, Gwalior, having joined the institution as a lecturer in 2003. He holds a B.E. in Mechanical Engineering from MITS Gwalior (2000), an M.Tech. in Mechanical Engineering with specialization in Tribology & Maintenance from SGSITS Indore (2003), and a Ph.D. in Mechanical Engineering from RGPV Bhopal (2011) titled 'An Investigation to Machine Fault Signature Analysis'. In addition to his academic role, he serves as Registrar and is a member of the Executive Council at MITS, contributing to governance, quality assurance, and educational leadership.

His research interests include Tribology and Maintenance, Vibration Analysis & Quality Assurance, with main focus on Vibration and Noise Control and Fault Diagnosis. Professor Jayaswal has published more than 60 papers in SCI/SCI-E, Scopus-indexed journals, conferences, and book chapters. Key works encompass 'Fault Investigation of Rolling Element Bearing Using Vibration Signature Analysis and Artificial Neural Network' (Springer, 2022), 'Envelope Spectrum Analysis of Noisy Signal with Spectral Kurtosis to Diagnose Bearing Defect' (Springer, 2022), 'Wear and fatigue behaviour investigation of hip implant head-stem interface using finite element analysis' (Materials Today: Proceedings, 2022), 'A study on tribological effect and surface treatment methods of Bio-ceramics composites' (Materials Today: Proceedings, 2021), 'A Review of Fault Detection, Diagnosis, and Prognosis of Rolling Element Bearing Using Advanced Approaches and Vibration Signature Analysis' (Springer, 2020), 'Diagnosis and Classifications of Bearing Faults Using Artificial Neural Network and Support Vector Machine' (J. Inst. Eng. India Ser. C, 2020), 'Machine Fault Signature Analysis' (International Journal of Rotating Machinery, 2008), and 'Application of ANN, Fuzzy Logic and Wavelet Transform in Machine Fault Diagnosis Using Vibration Signal Analysis' (Journal of Quality in Maintenance Engineering, 2010). His contributions frequently involve advanced signal processing and machine learning for bearing faults, collaborating with students like Pavan Agrawal and Arvind Singh Tomar. As a registered Ph.D. supervisor with RGPV, he guides research in mechanical engineering.