Dr. Piyush Shakya received his Ph.D. from the Department of Mechanical Engineering, IIT Delhi in 2015. He finished his Dual Degree (B. Tech in Mechanical and M. Tech in Product Design) from IIT Madras in 2006. He worked as “Member R&D” in TVS motor company, Hosur from 2006 to 2010. Prior to joining IIT Madras, he was a Research Fellow in the School of Aerospace, Transport, and Manufacturing at Cranfield University.
His research interests include condition monitoring, fault diagnosis, and prognosis. He is interested in developing condition monitoring techniques capable of accounting for different types of variations present in practical scenarios (dataset to dataset variations, defect topography variations, etc.).
Condition Monitoring (Fault Diagnosis, Prognosis, and Remaining Useful Life Prediction)
Sensor integration and Multi sensor data fusion.
S. Hashim and P. Shakya, A Spectral Kurtosis based Blind Deconvolution Approach for Spur Gear Fault Diagnosis, ISA Transactions, Accepted (2023), [Impact Factor: 7.3].
S. Buchaiah and P. Shakya, Automatic incipient fault detection and health state assessment of rolling element bearings using pruned exact linear time method, Journal of Vibration and Control, 2022, doi:10.1177/10775463221131843 [Impact Factor: 2.8].
S. Buchaiah, P. Shakya, Bearing fault diagnosis and prognosis using data fusion based feature extraction and feature selection, Measurement, 188 (2022), 110506, [Impact Factor: 5.6].
A. Patel, P. Shakya, Spur gear crack modelling and analysis under variable speed conditions using variational mode decomposition, Mechanism and Machine Theory, 164 (2021), 104357, [Impact Factor: 5.2].
S. K. Mishra, P. Shakya, V. Babureddy, and A. Vignesh, An approach to improve high-frequency resonance technique for bearing fault diagnosis, Measurement, 178 (2021), 109318, 1-24 [Impact Factor: 5.6].
