Neural networks technologies application in problems of critical places status monitoring of transport aircraft structure

Aeronautical and Space-Rocket Engineering

Strength and thermal conditions of flying vehicles


Аuthors

Bautin A. A.*, Svirskiy Y. A.**

Central Aerohydrodynamic Institute named after N.E. Zhukovsky, TsAGI, 1, Zhukovsky str., Zhukovsky, Moscow Region, 140180, Russia

*e-mail: andrey.bautin@tsagi.ru
**e-mail: yury.svirsky@tsagi.ru

Abstract

Air fleet developing prospects all over the world are closely associated with creation of highly efficient methods for maintaining the aircraft airworthiness. One of the tasks, being solved while such methods developing, is cost reduction during the aircraft operation. A reliable and rather effective periodic inspections system can be replaced by the structure status monitoring, which consists in continuous data collection and analysis of airframe integrity throughout the aircraft entire life span.

Status monitoring is performed by the onboard system, which basic elements are recording and analyzing unit, and sensors. The sensors are fixing the structure response at its integrity violation during operation. The damages detection effectiveness and possibility of reliable determination of the operation conditions depends in many ways on the algorithms realization, in which accordance the analyzing unit operates.

Currently, a large number of sensors types, based on various physical principles, have been developed. Strain gauges, which change of readings may indicate the presence of the structure damage, were widely employed while the experiment and approbation of the onboard monitoring systems.

The article proposes a method for determining the sensors installation scheme while fatigue damage detecting in the fuselage joints with account for the local nature of changes in the stress-strain state near the cracks and the allowable size of cracks that can be considered safe under certain conditions. The multi-site damage parameters, at which the residual strength of the joints does not decrease below the permissible level, were selected by studying the fractures of the joint samples by fractography. The optimal sensors installation scheme determining was performed based on the analysis of relation between of the measurement system readings and damages. This relation is presented herewith in the form of the neural network approximation.

The neural network training to obtain the necessary relation was performed based on the results of local deformations determining by the finite element method for various options of the of cracks location in the critical section of the joint. Various factors affecting strain measurements were accounted for while determining the places of sensors installation.

The article presents the result of the developed methodology application for the optimal sensors installation scheme determining in one of the types of longitudinal fuselage joints when detecting multi-point fatigue cracks during fatigue tests.

Keywords:

status monitoring, lap joint, multi-site fatigue damages, neural network approximation, finite element model of bolted joint

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