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Aero-engine remaining service life prediction model based on hybrid machine learning

A technology of machine learning model and life prediction model, applied in prediction, neural learning method, biological neural network model, etc., can solve problems such as obstacles and insufficient data, and achieve the effect of accurate prediction

Active Publication Date: 2021-04-06
DALIAN UNIV OF TECH
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  • Abstract
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AI Technical Summary

Problems solved by technology

Deep learning-based approaches are hampered by insufficient data on aero-engines

Method used

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  • Aero-engine remaining service life prediction model based on hybrid machine learning
  • Aero-engine remaining service life prediction model based on hybrid machine learning
  • Aero-engine remaining service life prediction model based on hybrid machine learning

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[0043] In order to make the object, technical solution and advantages of the present invention more clear, the present invention will be further described in detail below in conjunction with the examples. It should be understood that the specific embodiments described here are only used to explain the present invention, not to limit the present invention.

[0044] The application principle of the present invention will be described in detail below in conjunction with the accompanying drawings.

[0045] SOM automatically finds the internal laws and essential attributes in the sample, and self-organizes and self-adaptively changes the network parameters and structure. This paper proposes that the hybrid model is regarded as an improved version of the self-organizing map SOM, and after a standard training process, the GBRT regression model is combined with the SOM at a deeper level. The difference from the traditional SOM method is that the data mapped to neurons are retained to...

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Abstract

The invention belongs to the technical field of aero-engine fault prediction and health management, discloses an aero-engine residual service life prediction model based on hybrid machine learning, and particularly relates to a hybrid machine learning model-based SGBRT for predicting the residual service life of an aero-engine in time. The model combines a self-organizing mapping network and a gradient boosting regression tree algorithm, and can predict the residual service life of the aero-engine through the following steps: firstly, enabling the model to use the self-organizing mapping network to cluster an original sample set into a cluster; and then constructing a gradient boosting regression tree for each cluster so as to predict the residual service life of the aero-engine. According to the method, the remaining service life of the aero-engine can be better predicted, and the intrinsic characteristics of the degradation data of the aero-engine are also disclosed.

Description

technical field [0001] The invention belongs to the technical field of aero-engine failure prediction and health management, and in particular relates to an aero-engine remaining service life prediction model based on hybrid machine learning. Background technique [0002] Aeroengines are one of the most critical parts of an aircraft, and they are highly complex systems. Aeroengines usually work for a long time under severe conditions such as high temperature, high pressure, high speed and high load, so they are prone to failure. The failure of an aircraft engine can lead to catastrophic consequences, so very high reliability and safety are required. In addition, the maintenance cost of aero-engines is very high. For the management of aero engines, airlines are facing various pressures, including ensuring the safety and reliability of the engines, avoiding engine failures during operation, and reducing engine maintenance costs. Engine failure prediction and health manageme...

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Application Information

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IPC IPC(8): G06F30/27G06N3/04G06N3/08G06K9/62G06F17/18G06Q10/04G06F119/04
CPCG06F30/27G06N3/08G06F17/18G06Q10/04G06F2119/04G06N3/045G06F18/23
Inventor 徐甜甜韩光洁林川田晨史国华
Owner DALIAN UNIV OF TECH
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