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Indexed by:Journal Papers

Date of Publication:2020-07-01

Journal:APPLIED MATHEMATICAL MODELLING

Included Journals:SCIE

Volume:83

Page Number:487-496

ISSN No.:0307-904X

Key Words:Feature learning; Deep computation; Gas-path fault detection

Abstract:Recently, the safety of aircraft has attracted much attention with some crashes occurring. Gas-path faults, as the most common faults of aircraft, pose a vast challenge for the safety of aircraft because of the complexity of the aero-engine structure. In this article, a hybrid deep computation model is proposed to effectively detect gas-path faults on the basis of the performance data. In detail, to capture the local spatial features of the gas-path performance data, an unfully connected convolutional neural network of one-dimensional kernels is used. Furthermore, to model the temporal patterns hidden in the gas-path faults, a recurrent computation architecture is introduced. Finally, extensive experiments are conducted on real aero-engine data. The results show that the proposed model can outperform the models with which it is compared. (C) 2020 Elsevier Inc. All rights reserved.