Igor Loboda
Igor Loboda
Professor of Mechanics, National Polytechnic Institute, Mexico
Verified email at ipn.mx
Title
Cited by
Cited by
Year
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
322012
Gas turbine fault diagnosis using probabilistic neural networks
I Loboda, MAO Robles
International Journal of Turbo & Jet-Engines 32 (2), 175-191, 2015
272015
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
242007
Deviation problem in gas turbine health monitoring
I Loboda, S Yepifanov, Y Feldshteyn
Proceedings of IASTED International Conference on Power and Energy Systems, 2004
242004
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
202010
Gas turbine condition monitoring and diagnostics
I Loboda, G Injeti
Gas Turbines, 119-144, 2010
202010
A generalized fault classification for gas turbine diagnostics at steady states and transients
I Loboda, S Yepifanov, Y Feldshteyn
202007
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
172009
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
162010
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
152011
Gas turbine fault recognition trustworthiness
I Loboda, S Yepifanov
Científica 10 (2), 65-74, 2006
122006
On the selection of an optimal pattern recognition technique for gas turbine diagnosis
I Loboda, S Yepifanov
102013
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
102011
Gas turbine diagnostics under variable operating conditions
I Loboda, Y Feldshteyn, S Yepifanov
ASME Turbo Expo 2007: Power for Land, Sea, and Air, 829-837, 2007
102007
Trustworthiness problem of gas turbine parametric diagnosing
I Loboda
IFAC Proceedings Volumes 36 (5), 375-380, 2003
102003
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
92017
Neural networks for gas turbine diagnosis
I Loboda
Artificial Neural Networks-Models and Applications, 2016
92016
Gas turbine fault classification using probability density estimation
I Loboda
ASME Turbo Expo 2014: Turbine Technical Conference and Exposition, 2014
92014
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
92013
Gas Turbine Fault Recognition by Artificial Neural Networks
I Loboda, M Nakano Miyatake, A Goryachiy, et al.
The Fourth International Congress of Electromechanical Engineering and Systems, 2005
92005
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