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Method for obtaining gas path parameter deviation value under small sample condition

A gas path parameter and deviation value technology, which is applied in the field of obtaining gas path parameter deviation value and realizing the acquisition of gas path parameter deviation value under the condition of small samples, can solve the lack of universality of the gas path parameter deviation value model, and new models can Problems such as lack of information and complex working conditions of civil aviation engines

Active Publication Date: 2020-08-28
HARBIN INST OF TECH AT WEIHAI
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  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

[0003] However, due to the complex working conditions and various models of civil aviation engines, the gas path parameter deviation value model lacks universality and there is a shortage of usable information for new models.
That is, the existing technology lacks the establishment of a civil aviation engine gas path parameter deviation value regression model under cross-working conditions and cross-models, and the corresponding engine gas path parameter deviation value mining method to realize knowledge transfer and reuse

Method used

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  • Method for obtaining gas path parameter deviation value under small sample condition
  • Method for obtaining gas path parameter deviation value under small sample condition
  • Method for obtaining gas path parameter deviation value under small sample condition

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Experimental program
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Embodiment 1

[0038] The flow chart of a method for obtaining the deviation value of gas path parameters provided by this embodiment is as follows: figure 1 as shown,

[0039] The method includes:

[0040] Step 1 collects source and target domain aero-engine ACARS data, constructs an engine sample data set, and divides the engine sample data set into a training set and a test set; usually 80% of the training set and 20% of the test set are divided;

[0041] Step 2 performs normalization preprocessing on the data of the training set and test set of the engine in the source domain and the target domain; in this embodiment, the ACARS data obtained from the civil aviation company can be used to directly perform sample normalization;

[0042] Step 3 Construct the depth field adaptive gas path parameter deviation value regression model. The depth field adaptive gas path parameter deviation value regression model consists of three parts: feature extraction module, domain adaptive model and regres...

Embodiment 2

[0122] In the second embodiment, the technical solution of the first embodiment is experimentally verified by using the historical cruising data of civil aviation engines. The following is a detailed description of data sampling and preprocessing, hyperparameter settings, and model performance comparison.

[0123] In order to fully verify the application of the Res-BPNN-based deep domain adaptive regression model proposed in the first embodiment in the field of civil aviation engine gas path parameter deviation value mining and the universal applicability of the model, this embodiment uses the CFM56-5B2 produced by GE / 3 and CFM56-7B26 two different types of civil aviation engines, respectively, two sets of data sets were obtained and used in two gas path parameter deviation value mining experiments. The two groups of transfer learning tasks are transfer task A: CFM56-5B2 / 3→CFM56-7B26 and transfer task B: CFM56-7B26→CFM56-5B2 / 3.

[0124] Migration task A (CFM56-5B2 / 3→CFM56-7B...

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Abstract

The invention discloses a method for obtaining a gas path parameter deviation value under a small sample condition, and belongs to the technical field of aero-engine health management and monitoring.The method comprises the following steps: constructing an engine sample data set; carrying out normalization preprocessing on the data; constructing a depth domain adaptive gas path parameter deviation value regression model; training a depth domain adaptive gas path parameter deviation value regression model by using the target domain engine and source domain engine training set; testing an engine sample extracted from the target domain engine test set by using the trained depth domain adaptive gas path parameter deviation value regression model, analyzing a regression effect, and obtaining small sample new model aircraft engine ACARS data; and obtaining a gas path parameter deviation value by utilizing the stored gas path parameter deviation value regression model. According to the method, the gas circuit parameter deviation value model is established under the cross-working-condition and cross-machine-type conditions, and then the monitoring autonomy of the engine is obtained.

Description

technical field [0001] The application belongs to the technical field of aero-engine monitoring and health management, and relates to a method for obtaining deviation values ​​of air path parameters, in particular to a method for realizing the acquisition of deviation values ​​of air path parameters under the condition of small samples. Background technique [0002] Gas path parameter monitoring is an important technical means for the health management of aero-engines, especially civil aero-engines. Aeroengine is a kind of heat engine, and its core components are gas circuit system components, such as compressors, combustors, turbines, etc. The thermal parameters of the gas circuit components reflect the performance status of the engine. The commonly used gas circuit parameters are: EGT, FF, N1, N2, etc. These parameters are collected by airborne equipment and transmitted to the aircraft monitoring base in the form of ACARS (Aircraft Communication Addressing and Reporting S...

Claims

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G06F30/27G06F17/18G06N3/04G06N3/08
CPCG06F30/27G06F17/18G06N3/084G06N3/045
Inventor 付旭云周星杰钟诗胜张永健
Owner HARBIN INST OF TECH AT WEIHAI