A Fault Diagnosis Method for Aeroengine Based on 5G Edge Computing and Deep Learning

Abstract

The invention relates to an aero-engine fault diagnosis method based on 5G edge computing and deep learning, including steps: data collection, preprocessing and storage based on the 5G new cloud edge-end network architecture; constructing a machine learning module in the edge cloud, and aero-engine The historical data stored in the fault database management system provides training samples for the machine learning module; the aero-engine gear fault simulation platform and the aero-engine fault database management system intelligently manage themselves. The beneficial effects of the present invention are: using limited aero-engine fault data resources under the 5G emerging network architecture, combined with computing and storage resources, can increase the storage and transmission speed of massive aero-engine operation data, and provide a reliable basis for aero-engine fault identification; The invention directly performs the convolutional neural network operation on the original one-dimensional vibration signal, the process is relatively simple, does not require rich experience of signal processing experts, and the recognition effect is relatively ideal.

Description

technical field [0001] The invention relates to the field of fault diagnosis of complex equipment, in particular to the field of fault diagnosis of aero-engines, and in particular to an aero-engine fault diagnosis method based on 5G edge computing and deep learning. Background technique [0002] Since the end of the 20th century, with the continuous development of 5G information technology, artificial neural networks have been increasingly used in the aerospace field due to their powerful parallel processing capabilities, nonlinear function approximation capabilities, self-organization, self-learning, and self-adaptation Widely used, it has become one of the key means of aircraft fault diagnosis at the present stage. [0003] As the most important power component of an aero-engine, due to its complex mechanical structure and harsh working environment, its internal parts are prone to mechanical damage after a long period of use, which will greatly reduce its working performan...

AI Technical Summary

Problems solved by technology

[0003] As the most important power component of an aero-engine, due to its complex mechanical structure and harsh working environment, its internal parts are prone to mechanical damage after a long period of use, which will greatly reduce its working performance. For example, its rotating mechanism Shafting parts (such as gears, bearings, etc.), under the failure of surface wear and other problems, it is easy to cause huge vibration and noise of engine components, reduce operating efficiency, and seriously cause damage to the entire unit, resulting in huge economic losses

Failure to accurately detect the occurrence of faults in real time will have a huge hidden danger to the safety and efficiency of aerial operations

Images