 Neural networks and simple models for the fault diagnosis of naval turbochargersNikos G. Pantelelis, Andreas E. Kanarachos and Nikos Gotzias Mathematics and Computers in Simulation (MATCOM), 2000, vol. 51, issue 3, 387-397 Abstract: The present work deals with the development of simple finite element (FE) models of a turbocharger (rotor, foundation and hydrodynamic bearings) combined with neural networks and identification methods and vibration data obtained from real machines towards the automatic fault diagnosis. The development of this system is based on four sequential steps: the first is the development of simple but realistic FE models based on dynamic simulations of the complete system. The second step is the monitoring of the real turbocharger. The third step is the accurate modelling of the foundations and the excitation from the main engine, which will be done using a robust optimisation method. In the fourth step all the possible faults of the machine are identified using the artificial neural networks (ANN). In this way we can take advantage of the ANN learning capability for the real time diagnosis of potential faults. The application of the proposed system to a real naval turbocharger with vibration data obtained on working conditions show some promising results. Keywords: Neural networks; Finite element models; Turbocharger; Condition monitoring; Vibrations (search for similar items in EconPapers) Downloads: (external link) Related works: Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text Persistent link: https://EconPapers.repec.org/RePEc:eee:matcom:v:51:y:2000:i:3:p:387-397 Access Statistics for this article Mathematics and Computers in Simulation (MATCOM) is currently edited by Robert Beauwens More articles in Mathematics and Computers in Simulation (MATCOM) from Elsevier |
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