Dynamic neural networks for gas turbine engine degradation prediction, health monitoring and prognosis. Issue 8 (November 2016)

• Record Type: Journal Article
• Authors: Kiakojoori, S.; Khorasani, K.
• Is Part Of: Neural computing & applications. Volume 27:Issue 8(2016)
• Page Start: 2157
• Page End: 2192
• Publication Date: 2016-11
• Subjects: Dynamic neural networks -- Health monitoring prediction -- Gas turbine engines -- Degradation prediction -- Prognosis; Neural networks (Computer science) -- Periodicals; Neural circuitry -- Periodicals; Artificial intelligence -- Periodicals; Neural Networks (Computer) -- Periodicals; Réseaux neuronaux (Informatique) -- Périodiques; Réseaux nerveux -- Périodiques; Intelligence artificielle -- Périodiques; 006.32
• DOI: 10.1007/s00521-015-1990-0
• Languages: English
• ISSNs: 0941-0643

Abstract

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.