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
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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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