Defects Detection and Recognition in Aviation Riveted Joints by Using Ultrasonic Echo Signals of Non-Destructive Testing

被引：10

作者：

Amosov, Oleg S. ^{[1
]}

Amosova, Svetlana G. ^{[1
]}

Iochkov, Ilya O. ^{[2
]}

机构：

[1] Russian Acad Sci, VA Trapeznikov Inst Control Sci, Moscow, Russia

[2] Komsomolsk On Amur State Univ, Komsomolsk On Amur, Russia

来源：

IFAC PAPERSONLINE | 2021年 / 54卷 / 01期

关键词：

detection; pattern recognition; deep neural network; defect; non-destructive testing; ultrasonic method; echo signal; rivet joint; fractal property;

D O I：

10.1016/j.ifacol.2021.08.056

中图分类号：

TP [自动化技术、计算机技术];

学科分类号：

0812 ;

摘要：

The statement of the problem of defects detection and recognition in aviation riveted joints is given. The solution is proposed to be implemented using a deep neural network with a recurrent LSTM layer. An example of solving the problem of detecting and recognizing a defect in rivets is considered. The accuracy of the result obtained in the simulation was 100% for two -class and 96.25% for four-class recognition by echo signals of an ultrasonic flaw detector. It is shown that it is possible to use the fractal properties of the non-destructive testing echo signal when detecting defects. Copyright (C) 2021 The Authors.

引用

页码：484 / 489

页数：6

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