Aero-engine residual life prediction method based on transfer learning

An aero-engine and life prediction technology, which is applied in neural learning methods, computer-aided design, biological neural network models, etc., can solve problems such as difficult to obtain degradation data, low prediction accuracy, and poor engineering usability, so as to achieve improved accuracy and high Prediction accuracy and the effect of improving prediction accuracy

Active Publication Date: 2021-08-24
BEIHANG UNIV
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Problems solved by technology

[0005] The purpose of the present invention is to disclose a method for predicting the remaining life of an aero-engine based on transfer learning. The technical problem solved by this method is that the current data-driven prediction method is difficult to obtain degradation data, poor engineering usability, and low prediction accuracy.

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  • Aero-engine residual life prediction method based on transfer learning
  • Aero-engine residual life prediction method based on transfer learning
  • Aero-engine residual life prediction method based on transfer learning

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Embodiment Construction

[0066] The flow chart of the method for predicting the remaining life of an aero-engine based on transfer learning proposed by the present invention, such as figure 1 shown. The specific steps of the method for predicting the remaining life of an aero-engine based on transfer learning are as follows:

[0067] Step 1. Sensitive parameter analysis S1: Sensitivity analysis is carried out for all the state monitoring parameters of the aero-engine, so as to obtain several monitoring parameter sets X={x 1 , x 2 ,...,x n}

[0068] Step 2, data preprocessing S2: smoothing and normalizing preprocessing are performed on the selected aero-engine performance degradation characteristic parameter sequence, and c preprocessed parameter data X={x 1 , x 2 ,...x c}, c is the number of engine operating cycles, where X c ={x c1 , x c2 ,...,x cn}

[0069] Step 3. Calculation of the similarity distance based on the dynamic time warping algorithm S3: Carrying out the transferability measu...

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Abstract

An aero-engine residual life prediction method based on transfer learning comprises the following steps: performing sensitivity analysis on multi-dimensional degradation parameters of an aero-engine, screening out degradation sensitive parameters, and preprocessing the degradation sensitive parameters to improve data expression ability; carrying out mobility measurement on a to-be-tested sample based on a dynamic time warping algorithm, and finishing time domain sensitive data matching by utilizing a sliding time window; on the basis of a mobility measurement result, screening a migratable sample and calculating a migration weight; constructing a training data set by cutting migratable sample data; training a plurality of residual life prediction models by using the training data of each transferable sample; and realizing prediction result fusion of the life prediction models based on the transferable weight to obtain a final prediction result.

Description

technical field [0001] The invention relates to the technical field of prediction of the remaining life of an aero-engine, in particular to a method for predicting the remaining life of an aero-engine based on transfer learning. Background technique [0002] The aero-engine is the most important part of the aircraft. Due to its complex structure and harsh working environment, the failure modes of the aero-engine are more diverse and more prone to failure than other parts of the aircraft. Accurately predicting the remaining service life of aero-engines is of great significance for improving the safety and reliability of aero-engines and avoiding the waste of maintenance resources caused by excessive maintenance. [0003] Existing aero-engine remaining service life prediction methods can be divided into model-based methods, statistical analysis-based methods, and data-driven methods. Although the model-based prediction method can more accurately reflect the degradation mechan...

Claims

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G06F30/27G06N3/04G06N3/08G06F119/02G06F119/04
CPCG06F30/27G06N3/049G06N3/08G06F2119/02G06F2119/04G06N3/044
Inventor 程玉杰周安马剑吕琛
Owner BEIHANG UNIV
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