The invention belongs to the technical field of aero-engine monitoring, and discloses an aero-engine service life prediction method based on a knowledge embedded graph neural network, which comprises the following steps: firstly, constructing an aero-engine sensor knowledge graph, then converting the knowledge graph into an embedded vector, and then constructing a time-space degradation data set of knowledge embedding; and finally predicting the service life of the aero-engine through the space-time diagram convolutional neural network model. According to the method, time correlation, spatial correlation and sensor global information integration are considered, and the service life of the aero-engine can be effectively and accurately predicted.
本发明航空发动机监测技术领域,公开了一种基于知识嵌入图神经网络的航空发动机寿命预测方法,首先构建航空发动机传感器知识图谱,再将知识图谱转换为嵌入向量,然后构建知识嵌入的时空退化数据集,最后通过时空图卷积神经网络模型预测航空发动机寿命。本发明兼顾了时间相关性、空间相关性和传感器全局信息整合,能够有效且准确的预测航空发动机寿命。
Aero-engine life prediction method based on knowledge embedded graph neural network
一种基于知识嵌入图神经网络的航空发动机寿命预测方法
01.04.2025
Patent
Elektronische Ressource
Chinesisch
IPC: | G06N COMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS , Rechnersysteme, basierend auf spezifischen Rechenmodellen / F02C Gasturbinenanlagen , GAS-TURBINE PLANTS / G07C TIME OR ATTENDANCE REGISTERS , Zeit- oder Anwesenheitskontrollgeräte / B64F GROUND OR AIRCRAFT-CARRIER-DECK INSTALLATIONS SPECIALLY ADAPTED FOR USE IN CONNECTION WITH AIRCRAFT , Boden- oder Flugzeugträgerdeckeinrichtungen besonders ausgebildet für die Verwendung in Verbindung mit Luftfahrzeugen / G06F ELECTRIC DIGITAL DATA PROCESSING , Elektrische digitale Datenverarbeitung |
Aero-Engine Control Based on Improved Neural Network
IEEE | 2016
|Aero-engine service life prediction and maintenance method with risk avoidance
Europäisches Patentamt | 2024
|Probabilistic fatigue life prediction of an aero-engine turbine shaft
Emerald Group Publishing | 2022
|Europäisches Patentamt | 2023
|