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Neural networks and simple models for the fault diagnosis of naval turbochargers

Author

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  • Pantelelis, Nikos G.
  • Kanarachos, Andreas E.
  • Gotzias, Nikos

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.

Suggested Citation

  • Pantelelis, Nikos G. & Kanarachos, Andreas E. & Gotzias, Nikos, 2000. "Neural networks and simple models for the fault diagnosis of naval turbochargers," Mathematics and Computers in Simulation (MATCOM), Elsevier, vol. 51(3), pages 387-397.
    • Handle: RePEc:eee:matcom:v:51:y:2000:i:3:p:387-397
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    Cited by:

    1. Morando, S. & Jemei, S. & Hissel, D. & Gouriveau, R. & Zerhouni, N., 2017. "ANOVA method applied to proton exchange membrane fuel cell ageing forecasting using an echo state network," Mathematics and Computers in Simulation (MATCOM), Elsevier, vol. 131(C), pages 283-294.

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