Affiliation of Author(s):民航学院
Journal:Hangkong Dongli Xuebao
Abstract:Targeting the form and law of the aero-engine's performance degradation, a degradation assessment method based on denoising autoencoder was proposed. On account of the collected six aero-engine condition monitoring parameters, denoising autoencoder and greedy layer-wise training algorithm were used to assess performance degradation in order to explore the deep influences of those parameters on engine's performances and extract the data characteristics more conducive to the assessment. The comparison between the proposed algorithm and back propagation(BP) neural networks as well as support vector machine showed that the proposed method had high accuracy and robustness. The accuracy of the proposed method was 93.5% and the accuracy just reduced to 84.5% when signal-to-noise ratio was 10dB. The proposed method also can prevent the over-fitting of small samples in aero-engine condition monitoring. © 2018, Editorial Department of Journal of Aerospace Power. All right reserved.
ISSN No.:1000-8055
Translation or Not:no
Date of Publication:2018-08-01
Co-author:Hong, Jiyu,Ni, Xiaomei
Correspondence Author:wanghuawei