| A broadband internally resonant vibratory energy harvester LQ Chen, WA Jiang, M Panyam, MF Daqaq Journal of Vibration and Acoustics 138 (6), 061007, 2016 | 157 | 2016 |
| Characterizing the effective bandwidth of tri-stable energy harvesters M Panyam, MF Daqaq Journal of Sound and Vibration 386, 336-358, 2017 | 133 | 2017 |
| On approximating the effective bandwidth of bi-stable energy harvesters M Panyam, R Masana, MF Daqaq International Journal of Non-Linear Mechanics 67, 153-163, 2014 | 62 | 2014 |
| Micropower generation using cross-flow instabilities: a review of the literature and its implications MF Daqaq, A Bibo, I Akhtar, AH Alhadidi, M Panyam, B Caldwell, J Noel Journal of Vibration and Acoustics 141 (3), 030801, 2019 | 40 | 2019 |
| Exploiting the subharmonic parametric resonances of a buckled beam for vibratory energy harvesting M Panyam, MF Daqaq, SA Emam Meccanica 53 (14), 3545-3564, 2018 | 31 | 2018 |
| A comparative performance analysis of electrically optimized nonlinear energy harvesters M Panyam, MF Daqaq Journal of Intelligent Material Systems and Structures 27 (4), 537-548, 2016 | 26 | 2016 |
| Least-squares fitting of analytic primitives on a GPU MPM Ram, TR Kurfess, TM Tucker Journal of manufacturing systems 27 (3), 130-135, 2008 | 22* | 2008 |
| Vibration based fault diagnostics in a wind turbine planetary gearbox using machine learning A Amin, A Bibo, M Panyam, P Tallapragada Wind Engineering 47 (1), 175-189, 2023 | 13 | 2023 |
| GPU for CAD TR Kurfess, TM Tucker, K Aravalli, M P. M. Computer-Aided Design and Applications 4 (6), 853-862, 2007 | 8 | 2007 |
| Vibration-based condition monitoring in wind turbine gearbox using convolutional neural network A Amin, A Bibo, M Panyam, P Tallapragada 2022 American control conference (ACC), 3777-3782, 2022 | 7 | 2022 |
| Wind turbine gearbox fault diagnosis using cyclostationary analysis and interpretable CNN A Amin, A Bibo, M Panyam, P Tallapragada Journal of Vibration Engineering & Technologies 12 (2), 1695-1705, 2024 | 5 | 2024 |
| Condition Monitoring in a Wind Turbine Planetary Gearbox Using Sensor Fusion and Convolutional Neural Network A Amin, A Bibo, M Panyam, P Tallapragada IFAC-PapersOnLine 55 (37), 776-781, 2022 | 5 | 2022 |
| On the multi-body modeling and validation of a full scale wind turbine nacelle test bench M Panyam, A Bibo, S Roach Dynamic Systems and Control Conference 51913, V003T29A005, 2018 | 4 | 2018 |
| Considerations for testing full-scale wind turbine nacelles with hardware-in-the-loop KC Heinold Clemson University, 2021 | 3 | 2021 |
| Modeling considerations for testing full-scale offshore wind turbine nacelles with hardware-in-the-loop K Heinold, M Panyam, A Bibo International Design Engineering Technical Conferences and Computers and …, 2020 | 3 | 2020 |
| Application of a test bench to wind turbine drivetrains subject to dynamic loads: Learnings and recommendations P Giguère, A Bibo, M Panyam, JR Wagner Conference for Wind Power Drives, 213, 2019 | 3 | 2019 |
| Nonlinear modal interactions to improve the broadband transduction of vibratory energy harvesters LQ Chen, WA Jiang, M Panyam, MF Daqaq Smart Materials, Adaptive Structures and Intelligent Systems 57304, V002T07A003, 2015 | 3 | 2015 |
| Evaluation of dynamic testing of full-scale wind turbine drivetrains with hardware-in-the-loop A Bibo, M Panyam Wind Engineering 46 (5), 1550-1569, 2022 | 2 | 2022 |
| Experimental Measurement of In-Plane Rolling Nonpneumatic Tire Vibrations Using High-Speed Imaging M Panyam, B Ayalew, T Rhyne, S Cron, J Adcox Tire Science and Technology 47 (3), 196-210, 2019 | 1 | 2019 |
| A Bayesian deep learning framework for reliable fault diagnosis in wind turbine gearboxes under various operating conditions A Amin, A Bibo, M Panyam, P Tallapragada Wind Engineering 48 (2), 297-309, 2024 | | 2024 |
