Capt. Sunil Tyagi, PhD

Noida, India, +91-9922242303, suniltyagi@tyagination.com

LinkedIn : https://www.linkedin.com/in/sunil-tyagi-01/

Google Scholar : https://scholar.google.com/citations?user=aKkPxGcAAAAJ&hl=en

Marine Engineer: Extensive experience in facility management and maintenance; adept at leading complex operations.

Acoustic Stealth Design: Key role in delivering India's first-ever nuclear-powered submarine, showcasing advanced technical acumen.

HR Management: Astute in HR management, excelling in team leadership and strategic personnel development.

Technical Challenges: Successfully navigated significant technical and industrial challenges in high-stakes projects.

Advanced Academics: Holds an interdisciplinary PhD in Machine Learning and Mechanical Engineering.

AI in Maintenance: Specializes in AI applications for predictive maintenance of plant machinery.

Scholarly Contributions: Authored one patent and seven journal papers, underscoring a commitment to innovation and research.

Owns the Patent Titled: Bearing Fault Detection

This invention relates to a sophisticated method and system for detecting faults in ball bearings, crucial components in rotating machinery. The system employs advanced techniques including Discreet Wavelet Transform (DWT) and artificial neural networks to analyze vibration data collected from ball bearings. This data is first normalized and pre-processed, then segmented into bins to extract characteristic features. These features are used to train and utilize an artificial neural network classifier that can accurately identify early-stage faults, significantly enhancing predictive maintenance capabilities and reducing machinery downtime. This innovation is particularly adept at detecting subtle anomalies that traditional methods might miss, making it invaluable for industries reliant on heavy machinery.

Video : https://figshare.com/account/projects/34436/articles/6363911

Details :https://figshare.com/s/e5f58a0ddf8fae7e2815

Journal Papers

• § 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://www.tandfonline.com/doi/full/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 CS. A Comparative Study of SVM Classifiers and Artificial Neural Networks Application for Rolling Element Bearing Fault Diagnosis using Wavelet Transform Preprocessing. International Journal of Mechanical and Mechatronics Engineering. 2008 Jul 20;2(7):904-12. Available from: https://publications.waset.org/9372/a-comparative-study-of-svm-classifiers-and-artificial-neural-networks-application-for-rolling-element-bearing-fault-diagnosis-using-wavelet-transform-preprocessing.
• Tyagi S, Panigrahi SK. Bearing fault diagnosis using acoustic signal processing technique. MILIT Journal; 2014; 1: 11-17. (MILIT)

Conference Papers

• Tyagi S, Panigrahi SK. Simulation of Ball Bearing Defect using Finite Element Transient Analysis. Proceedings of the National Conference on Recent Advancement in Mechanical Engineering (NCRAME 2013); 2013 November 8-9; NERIST, Arunachal Pradesh, India. p. 245-253. Excel India Publishers; 2013
• Tyagi S, Panigrahi SK. An Artificial Neural Networks and Wavelet Based Method for Rolling Element Bearing Fault Diagnosis. National Conference on Condition Monitoring (NCCM); 2013 October 4 – 5; Bangalore, India. Paper ID- NCCM-2013-54
• Tyagi S, Panigrahi SK. A Pattern Recognition Method for Rolling Element Bearing Fault Diagnosis. Proceedings of the International Conference on Advances in Mechanical Engineering; 2013 May 29 - 31; COEP, Pune, Maharashtra, India, Paper ID - ICAME2013 Sl6/P6
• Solwat SN, Tyagi S, Panigrahi SK. Simulation of Gearbox Vibration Using Finite Element Analysis. Proceedings of the International Conference on Advances in Mechanical Engineering; 2013 May 29 - 31; COEP, Pune, India, Paper ID - ICAME2013 S2/O8