Captain Sunil Tyagi, PhD, ME+
Capt Sunil Tyagi, PhD was commissioned in the Engineering Branch of the Indian Navy in 1992 after completing B.E. (Mechanical) from GB Pant University, Pant Nagar, India. Capt Tyagi has over fourteen years of experience in operations and maintenance of onboard marine machinery, about five years of teaching and about six years of submarine acoustic stealth design experience. After joining the Navy; he continued his academic progression along with his naval career, he received an M.E. degree in Mechanical – Marine engineering from Pune University, India in 2002.
In the year 2017, he completed his interdisciplinary PhD from the Defence Institute of Advanced Technology, Pune. His doctoral research united two significant strands of technology, i.e., artificial intelligence and mechanical engineering. In doctoral work, the officer applied various global optimization techniques and emerging pattern recognition algorithms for the predictive maintenance of Gears and Ball-bearings. He has obtained one patent and has published eight papers in reputed international journals. His main areas of research interest are the practical application of machine learning algorithms.
The officer is currently employed as a Senior Fellow at the New Delhi-based think tank, the Centre for Air Power Studies and is working on a book project titled ‘Winning the Future War at Sea with AI’. He is a member of the Institute of Marine Engineers (India), Condition Monitoring Society of India and Soft Computing Research Society.
- Tyagi S, Panigrahi SK. Bearing Fault Detector. 201621003344 A. The Patent Office Journal, India. 2016 May 27: 20909. http://www.ipindia.nic.in/writereaddata/Portal/IPOJournal/1_346_1/part1.pdf
- Tyagi S, Panigrahi SK. An improved envelope detection method using particle swarm optimization for rolling element bearing fault diagnosis. Journal of Computational Design and Engineering. 2017;4(4):305–17. Available from: https://doi.org/10.1016/j.jcde.2017.05.002 (Elsevier)
- Tyagi S, Panigrahi SK. An SVM—ANN Hybrid Classifier for Diagnosis of Gear Fault. Applied Artificial Intelligence. 2017; 31(3):209-31. Available from: http://dx.doi.org/10.1080/08839514.2017.1315502. (Taylor & Francis)
- Tyagi S, Panigrahi S. Transient analysis of ball bearing fault simulation using finite element method. Journal of The Institution of Engineers (India): Series C. 2014; 95(4): 309–318. Available from: https://link.springer.com/article/10.1007/s40032-014-0129-x. (Springer)
- Tyagi S, Panigrahi SK. A Hybrid Genetic Algorithm and Back-Propagation classifier for gearbox fault diagnosis. Applied Artificial Intelligence. 2017; 31(7-8): 593-612.
Available from: https://doi.org/10.1080/08839514.2017.1413066. (Taylor & Francis).
- Tyagi S, Panigrahi SK. A DWT and SVM based method for rolling element bearing fault diagnosis and its comparison with Artificial Neural Networks. Journal of Applied and Computational Mechanics. 2017;3(1):80-91. Available from: http://dx.doi.org/10.22055/jacm.2017.21576.1108. (Chamran University)
- Tyagi S, Panigrahi SK. A simple continuous wavelet transform method for detection of rolling element bearing faults and its comparison with envelope detection. International Journal of Science and Research (IJSR). 2017; 6(3):1033. Available from: https://www.ijsr.net/archive/v6i3/ART20171614.pdf. (IJSR)
- Tyagi S, Panigrahi SK. Bearing fault diagnosis using acoustic signal processing technique. MILIT Journal; 2014; 1: 11-17. (MILIT)