GENERATIVE ADVERSARIAL MULTI-HEAD ATTENTION NEURAL NETWORK SELF-LEARNING METHOD FOR AERO-ENGINE DATA RECONSTRUCTION

Title:

GENERATIVE ADVERSARIAL MULTI-HEAD ATTENTION NEURAL NETWORK SELF-LEARNING METHOD FOR AERO-ENGINE DATA RECONSTRUCTION

Document Type and Number:

WIPO Patent Application WO/2024/087129

Kind Code:

A1

Abstract:

The present invention relates to the field of end-to-end self-learning of missing aero-engine data, and provides a generative adversarial multi-head attention neural network self-learning method for aero-engine data reconstruction. The method comprises: first, preprocessing samples, prefilling standardized data by using a machine learning algorithm, and using information obtained after prefilling as part of training information to participate in network training; second, constructing a generative adversarial multi-head attention network model, and training the generative adversarial multi-head attention network model by using a training sample set; and finally, generating a sample by using a trained sample generator G. According to the present invention, distribution information of data can be better learned by using a generative adversarial network, spatial information and time sequence information between aero-engine data are fully mined by using a parallel convolution and multi-head attention mechanism, and compared with existing filling algorithms, the algorithm can effectively improve the self-learning precision of missing data, and has great significance for subsequent prediction and maintenance of an aero-engine.

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Inventors:

MA SONG (CN)
SUN TAO (CN)
XU ZENGSONG (CN)
SUN XIMING (CN)
LI ZHI (CN)

Application Number:

PCT/CN2022/128101

Publication Date:

May 02, 2024

Filing Date:

October 28, 2022

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Assignee:

UNIV DALIAN TECH (CN)

International Classes:

G06F30/27; G06N3/04; G06N3/08

Foreign References:

CN113869386A	2021-12-31
CN113298131A	2021-08-24
CN112185104A	2021-01-05
CN114445252A	2022-05-06
CN114022311A	2022-02-08
US20200394508A1	2020-12-17

Attorney, Agent or Firm:

LIAONING HONGWEN INTELLECTUAL PROPERTY AGENCY CO., LTD. (CN)

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