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A method for obtaining the deviation value of gas path parameters under the condition of small sample

A gas path parameter and deviation value technology, applied in the field of obtaining the gas path parameter deviation value under the condition of realizing small samples, and obtaining the gas path parameter deviation value, which can solve the lack of universality of the gas path parameter deviation value model, and new models can be used. Lack of use information, complex civil aviation engine operating conditions and other problems

Active Publication Date: 2022-07-22
HARBIN INST OF TECH AT WEIHAI
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  • Abstract
  • Description
  • Claims
  • Application Information

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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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  • A method for obtaining the deviation value of gas path parameters under the condition of small sample
  • A method for obtaining the deviation value of gas path parameters under the condition of small sample
  • A method for obtaining the deviation value of gas path parameters under the condition of small sample

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Embodiment 1

[0038] The flowchart of a method for obtaining a deviation value of a gas path parameter provided in this embodiment is as follows: figure 1 shown,

[0039] The method includes:

[0040] Step 1: Collect the source domain and target domain aero-engine ACARS data, construct an engine sample data set, and divide 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] In step 2, normalization preprocessing is performed on the data of the training set and the 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 constructs a regression model of the deviation value of the air path parameter of the depth field adaptation, and the regression model of the deviation value of the air path parameter of the depth field adaptation is composed of thre...

Embodiment 2

[0122] In the second embodiment, the technical solution of the first embodiment is experimentally verified by using the historical cruise data of the civil aviation engine. 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 deep domain adaptive regression model based on Res-BPNN proposed in the first embodiment in the field of civil aviation engine gas path parameter deviation value mining and the general applicability of the model, this embodiment is from 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 applied to two gas path parameter deviation value mining experiments. The two sets 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...

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Abstract

The application discloses a method for obtaining the deviation value of gas path parameters under the condition of small samples, which belongs to the technical field of aero-engine health management and monitoring. The invention includes constructing an engine sample data set, performing normalization preprocessing on the data, constructing a regression model of the self-adaptive gas path parameter deviation value in the depth field, and using the target field engine and the source field engine training set to train the self-adaptive gas path parameter deviation in the depth field. value regression model, use the trained deep domain adaptive gas path parameter deviation value regression model to test the engine samples extracted from the target domain engine test set, analyze the regression effect, and obtain a small sample of new aircraft engine ACARS Data, and use the saved gas path parameter deviation value regression model to obtain the gas path parameter deviation value. The invention realizes the establishment of a gas path parameter deviation value model under cross-working conditions and cross-machine models, thereby obtaining the autonomy of engine monitoring.

Description

technical field [0001] The present application belongs to the technical field of aero-engine monitoring and health management, and relates to a method for obtaining the deviation value of gas path parameters, in particular to a method for obtaining the deviation value of gas path parameters under the condition of small samples. Background technique [0002] Air circuit parameter monitoring is an important technical means for the health management of aero-engines, especially civil aviation engines. As a kind of heat engine, the core components of aero-engines are air system components, such as compressors, combustion chambers, turbines, etc. The thermal parameters of the air path components reflect the performance state of the engine. The commonly used air path 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 CommunicationAddressing and Reporting System) m...

Claims

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Application Information

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Patent Type & Authority Patents(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