Aircraft fuel system fault prediction method for maintenance outfield based on deep learning, terminal and readable storage medium

Abstract

The invention provides an aircraft fuel system fault prediction method for maintenance outfield based on deep learning, a terminal and a readable storage medium. The method comprises the steps of acquiring a time sequence data set composed of N types of aircraft parameters sensitive to faults in an aircraft fuel system; obtaining a spectrogram according to the time sequence wave of the fuel system flight parameter data within fixed time; and enabling the deep learning algorithm to perform fault prediction on the aircraft fuel system according to the spectrogram. A time sequence data set composed of N types of aircraft parameters sensitive to faults in an aircraft fuel system is acquired. Then, a spectrogram is acquired according to the time sequence waves of the fuel system within fixed time according to the flight parameters. Finally, the deep learning algorithm based on a convolutional neural network framework is adopted to predict the fault of the aircraft fuel system according tothe spectrogram, the remaining life of the aircraft fuel system is accurately predicted, the health condition of the aircraft fuel system can be effectively predicted, and serious consequences causedby uncertain faults in actual flight are avoided.

Description

technical field [0001] The present invention relates to the field of computer technology, and in particular to a deep learning-based method for predicting failures of aircraft fuel systems in the maintenance field of a military flight big data maintenance field autonomous support information support system, a terminal and a readable storage medium. Background technique [0002] From the 1990s to the present, aviation equipment technology has developed rapidly, especially in the context of military strategy adjustments and changes in the combat use of aviation equipment, the requirements for aircraft ground support are getting higher and higher, and the support of aircraft fuel systems is among them. is the most fundamental factor. The rapid development of military technology has put forward higher requirements for the guarantee and failure prediction of flight fuel systems. But in the long-term development, the support technology of aircraft fuel system always lags behind t...

AI Technical Summary

Problems solved by technology

The original aircraft fuel system support system is facing huge challenges under the new equipment conditions, and if the support is not in place, the combat readiness rate of military aircraft will be greatly reduced

[0003] The health management of the aircraft fuel system lacks quantitative analysis, and the experience and data accumulated in the actual use and maintenance process have not been well combined with the design data, resulting in a separation between theory and practice

There is no early warning mechanism when the aircraft fuel system fails, and it is difficult for the maintenance personnel of the aircraft maintenance terminal to know the aircraft fuel system equipped, and the predictability is insufficient, and the phenomenon of excessive maintenance and insufficient maintenance coexists, resulting in a decline in the integrity rate of the aircraft fuel system ;

[0004] When the aircraft fuel system fails, the fault data at this stage is not structured, and it is difficult for the field maintenance personnel to conduct a clear fault diagnosis based on the comprehensive analysis of the fault phenomenon, reliability data, index data, etc., so it is difficult to find Optimal Failure Prediction Method for Aircraft Fuel System Replacement

This increases the maintenance cost of the aircraft fuel system, and at the same time, the faulty fuel system cannot be well maintained, resulting in a waste of resources;

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