|本期目录/Table of Contents|

[1]钱鹏,陆金桂.基于PSO-BP神经网络的红外无损检测缺陷定量识别[J].南京工业大学学报(自然科学版),2019,41(04):501-507.[doi:10.3969/j.issn.1671-7627.2019.04.016]
 QIAN Peng,LU Jingui.Quantitative identification of defects in infrared NDT based on PSO-BP neural network[J].Journal of NANJING TECH UNIVERSITY(NATURAL SCIENCE EDITION),2019,41(04):501-507.[doi:10.3969/j.issn.1671-7627.2019.04.016]
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基于PSO-BP神经网络的红外无损检测缺陷定量识别()
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《南京工业大学学报(自然科学版)》[ISSN:1671-7627/CN:32-1670/N]

卷:
41
期数:
2019年04期
页码:
501-507
栏目:
出版日期:
2019-07-10

文章信息/Info

Title:
Quantitative identification of defects in infrared NDT based on PSO-BP neural network
文章编号:
1671-7627(2019)04-0501-07
作者:
钱鹏陆金桂
南京工业大学 机械与动力工程学院,江苏 南京 211800
Author(s):
QIAN PengLU Jingui
School of Mechanical and Power Engineering, Nanjing Tech University, Nanjing 211800,China
关键词:
红外无损检测 定量识别 BP神经网络 粒子群算法
Keywords:
infrared nondestructive testing quantitative identification BP neural network particle swarm optimization algorithm
分类号:
TP183
DOI:
10.3969/j.issn.1671-7627.2019.04.016
文献标志码:
A
摘要:
为解决红外无损检测缺陷定量识别困难的问题,提出了一种粒子群算法(PSO)优化反向传播(BP)神经网络的缺陷定量识别方法。以最佳检测时间与最大温差为模型的输入,孔洞缺陷的深度与直径大小为模型的输出,建立粒子群优化的BP神经网络缺陷定量识别模型。使用ANSYS软件对带有平底孔洞缺陷的金属平板进行脉冲热分析,提取金属平板检测表面的最大温差与最佳检测时间,作为神经网络模型训练与检验的数据样本,使用神经网络进行预测。计算结果表明:预测值的最大误差为5.5%,最小误差为1%,证明了粒子群优化BP神经网络方法进行红外无损检测定量识别的可行性。
Abstract:
In order to solve the problem of quantitative recognition of defects in infrared nondestructive testing, a particle swarm optimization(PSO)algorithm for quantitative defect recognition with back propagation(BP)neural network was proposed. With the input of the optimal detection time and maximum temperature difference, the depth and diameter of hole defects were the output of the model, and the BP neural network defect identification model of particle swarm optimization was established. ANSYS was used to analyze the metal plate with flat bottom hole defects by pulse thermal analysis. The maximum temperature difference and the best detection time were extracted from the surface of the metal plate. The neural network model was used to train, test and predict the data. Results showed that the maximum error of neural network prediction was 5.5%, and the minimum error was 1%. The feasibility of PSO-BP neural network for quantitative identification of infrared nondestructive testing was proved.

参考文献/References:

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备注/Memo

备注/Memo:
收稿日期:2018-05-09
作者简介:钱鹏(1992—),男,E-mail:2510537199@qq.com; 陆金桂(联系人),教授,E-mail:lujg@njtech.edu.cn.
引用本文:钱鹏,陆金桂.基于PSO-BP神经网络的红外无损检测缺陷定量识别[J].南京工业大学学报(自然科学版),2019,41(4):501-507..
更新日期/Last Update: 2019-07-20