| Neural networks for gas turbine fault identification: Multilayer perceptron or radial basis network? I Loboda, Y Feldshteyn, V Ponomaryov De Gruyter 29 (1), 37-48, 2012 | 47 | 2012 |
| Gas turbine fault diagnosis using probabilistic neural networks I Loboda, MA Olivares Robles International Journal of Turbo & Jet-Engines 32 (2), 175-191, 2015 | 39 | 2015 |
| Gas turbine condition monitoring and diagnostics I Loboda Gas turbines, 119-144, 2010 | 28 | 2010 |
| Deviation problem in gas turbine health monitoring I Loboda, S Yepifanov, Y Feldshteyn Proceedings of IASTED International Conference on Power and Energy Systems, 2004 | 28 | 2004 |
| A generalized fault classification for gas turbine diagnostics at steady states and transients I Loboda, S Yepifanov, Y Feldshteyn | 27 | 2007 |
| A mixed data-driven and model based fault classification for gas turbine diagnosis I Loboda, S Yepifanov Turbo Expo: Power for Land, Sea, and Air 43987, 257-265, 2010 | 25 | 2010 |
| Adaptive vector directional filters to process multichannel images V Ponomaryov, A Rosales, F Gallegos, I Loboda IEICE transactions on communications 90 (2), 429-430, 2007 | 25 | 2007 |
| Diagnostic analysis of maintenance data of a gas turbine for driving an electric generator I Loboda, S Yepifanov, Y Feldshteyn Turbo Expo: Power for Land, Sea, and Air 48821, 745-756, 2009 | 24 | 2009 |
| Neural networks for gas turbine diagnosis I Loboda Artificial Neural Networks—Models and Applications, 2016 | 23 | 2016 |
| Evaluation of gas turbine diagnostic techniques under variable fault conditions JL Pérez-Ruiz, I Loboda, LA Miró-Zárate, M Toledo-Velázquez, ... Advances in Mechanical Engineering 9 (10), 1687814017727471, 2017 | 20 | 2017 |
| Neural networks for gas turbine fault identification: multilayer perceptron or radial basis network? I Loboda, Y Feldshteyn, V Ponomaryov Turbo Expo: Power for Land, Sea, and Air 54631, 465-475, 2011 | 18 | 2011 |
| Aircraft engine gas-path monitoring and diagnostics framework based on a hybrid fault recognition approach JL Pérez-Ruiz, Y Tang, I Loboda Aerospace 8 (8), 232, 2021 | 17 | 2021 |
| Polynomials and neural networks for gas turbine monitoring: a comparative study I Loboda, Y Feldshteyn Walter de Gruyter GmbH & Co. KG 28 (3), 227-236, 2011 | 16 | 2011 |
| Polynomials and neural networks for gas turbine monitoring: A comparative study I Loboda, Y Feldshteyn Turbo Expo: Power for Land, Sea, and Air 43987, 417-427, 2010 | 15 | 2010 |
| A benchmarking analysis of a data-driven gas turbine diagnostic approach I Loboda, JL Pérez-Ruiz, S Yepifanov Turbo Expo: Power for Land, Sea, and Air 51128, V006T05A027, 2018 | 14 | 2018 |
| An integrated approach to gas turbine monitoring and diagnostics I Loboda, S Yepifanov, Y Feldshteyn Turbo Expo: Power for Land, Sea, and Air 43123, 359-367, 2008 | 13 | 2008 |
| Gas turbine diagnostics under variable operating conditions I Loboda, Y Feldshteyn, S Yepifanov Turbo Expo: Power for Land, Sea, and Air 4790, 829-837, 2007 | 13 | 2007 |
| Gas turbine fault recognition trustworthiness I Loboda, S Yepifanov Científica 10 (2), 65-74, 2006 | 13 | 2006 |
| Gas path model identification as an instrument of gas turbine diagnosing SV Yepifanov, II Loboda Turbo Expo: Power for Land, Sea, and Air 36843, 371-376, 2003 | 13 | 2003 |
| A more realistic scheme of deviation error representation for gas turbine diagnostics I Loboda, S Yepifanov, Y Feldshteyn Int. J. Turbo Jet-Engines 30 (2), 179-189, 2013 | 12 | 2013 |
