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Neural Computing and Applications
Erschienen in: Neural Computing and Applications 8/2016

01.11.2016 | Predictive Analytics Using Machine Learning

Dynamic neural networks for gas turbine engine degradation prediction, health monitoring and prognosis

verfasst von: S. Kiakojoori, K. Khorasani

Erschienen in: Neural Computing and Applications | Ausgabe 8/2016

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Abstract

In this paper, the problem of health monitoring and prognosis of aircraft gas turbine engines is considered by using computationally intelligent methodologies. Two different dynamic neural networks, namely the nonlinear autoregressive with exogenous input neural networks and the Elman neural networks, are developed and designed for this purpose. The proposed dynamic neural networks are designed to capture the dynamics of two main degradations in the gas turbine engine, namely the compressor fouling and the turbine erosion. The health status and condition of the engine in terms of the turbine output temperature (TT) are then predicted subject to occurrence of these deteriorations. Various scenarios consisting of fouling and erosion separately as well as combined are considered. For each scenario, several neural networks are trained and their performance in predicting multiple flights ahead TTs is evaluated. Finally, the most suitable neural networks for achieving the best prediction are selected by using the normalized Bayesian information criterion model selection. Simulation results presented demonstrate and illustrate the effective performance of our proposed neural network-based prediction and prognosis strategies.
Vorheriger Artikel Guest editorial: special issue on predictive analytics using machine learning
Nächster Artikel A hybrid grid-GA-based LSSVR learning paradigm for crude oil price forecasting

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Metadaten
Titel
Dynamic neural networks for gas turbine engine degradation prediction, health monitoring and prognosis
verfasst von
S. Kiakojoori
K. Khorasani
Publikationsdatum
01.11.2016
Verlag
Springer London
Erschienen in
Neural Computing and Applications / Ausgabe 8/2016
Print ISSN: 0941-0643
Elektronische ISSN: 1433-3058
DOI
https://doi.org/10.1007/s00521-015-1990-0

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