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
• ISSN: 0941-0643; 1433-3058
• DOI: 10.1007/s00521-015-1990-0

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.