Dynamic neural networks for gas turbine engine degradation prediction, health monitoring and prognosis
- 29 July 2015
- journal article
- Published by Springer Science and Business Media LLC in Neural Computing & Applications
- Vol. 27 (8), 2157-2192
- https://doi.org/10.1007/s00521-015-1990-0
Abstract
No abstract availableKeywords
This publication has 61 references indexed in Scilit:
- Nonlinear Fault Diagnosis of Jet Engines by Using a Multiple Model-Based ApproachJournal of Engineering for Gas Turbines and Power, 2011
- Multimodel InferenceSociological Methods & Research, 2004
- A Generic Approach for Gas Turbine Adaptive ModelingJournal of Engineering for Gas Turbines and Power, 2004
- Time series forecasting using a hybrid ARIMA and neural network modelNeurocomputing, 2003
- Adaptive time delay neural network structures for nonlinear system identificationNeurocomputing, 2002
- Identification of asymmetric prediction intervals through causal forcesJournal of Forecasting, 2001
- The present status of maintenance strategies and the impact of maintenance on reliabilityIEEE Transactions on Power Systems, 2001
- Non-linear system identification using neural networksInternational Journal of Control, 1990
- Estimating the Dimension of a ModelThe Annals of Statistics, 1978
- Parameter Selection for Multiple Fault Diagnostics of Gas Turbine EnginesJournal of Engineering for Power, 1975