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

• Published in: Neural computing & applications Vol. 27; no. 8; pp. 2157 - 2192
• Main Authors: Kiakojoori, S., Khorasani, K.
• Format: Journal Article
• Language: English
• Published: London Springer London 01.11.2016; Springer Nature B.V
• Subjects: Aerospace engines; Aging aircraft; Artificial Intelligence; Bayesian analysis; Computational Biology/Bioinformatics; Computational Science and Engineering; Computer Science; Computer simulation; Data Mining and Knowledge Discovery; Diagnostic systems; Fouling; Gas turbine engines; Health; Image Processing and Computer Vision; Neural networks; Performance prediction; Predictive Analytics Using Machine Learning; Probability and Statistics in Computer Science; Prognosis; Dynamic neural networks; Health monitoring prediction; Prognosis; Degradation prediction; Gas turbine engines

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 network...