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论文类型：期刊论文

发表时间：2020-07-01

发表刊物：APPLIED MATHEMATICAL MODELLING

收录刊物：SCIE

卷号：83

页面范围：487-496

ISSN号：0307-904X

关键字：Feature learning; Deep computation; Gas-path fault detection

摘要：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.