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Regression model for civil aero-engine gas path parameter deviation based on deep domainadaptation with Res-BP neural network

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Chinese Journal of Aeronautics 2021年第1期34卷 79-90页

作者： Xingjie ZHOU Xuyun FU Minghang ZHAO Shisheng ZHONG Department of Mechanical Engineering Harbin Institute of Technology at WeihaiWeihai 264209China

The variations in gas path parameter deviations can fully reflect the healthy state of aeroengine gas path components and units;therefore,airlines usually take them as key parameters for monitoring the aero-engine gas path performance state and conducting fault *** the past,the airlines could not obtain deviations *** present,a data-driven method based on an aero-engine dataset with a large sample size can be utilized to obtain the ***,it is still difficult to utilize aero-engine datasets with small sample sizes to establish regression models for deviations based on deep neural *** obtain monitoring autonomy of each aero-engine model,it is crucial to transfer and reuse the relevant knowledge of deviation modelling learned from different aero-engine *** paper adopts the Residual-Back Propagation Neural Network(Res-BPNN)to deeply extract high-level features and stacks multi-layer Multi-Kernel Maximum Mean Discrepancy(MK-MMD)adaptation layers to map the extracted high-level features to the Reproduce Kernel Hilbert Space(RKHS)for discrepancy *** further reduce the distribution discrepancy of each aero-engine model,the method of maximizing domain-confusion loss based on an adversarial mechanism is introduced to make the features learned from different domains as close as possible,and then the learned features can be *** the above methods,domain-invariant features can be extracted,and the optimal adaptation effect can be ***,the effectiveness of the proposed method is verified by using cruise data from different civil aero-engine models and compared with other transfer learning algorithms.

关键词： Civil aero-engine deep domain adaptation domain confusion Neural networks Transfer learning

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