红外技术2026，Vol.48Issue(2)：226-233,8.
基于Bi-LSTM的红外热波成像对蜂窝结构缺陷分类研究
Research on Classification of Honeycomb Structural Defects by Infrared Thermography Based on Bi-LSTM
章林强 1安奎远 2郑金华 3盛涛 3江海军4
作者信息
- 1. 上海航天精密机械研究所,上海 201600
- 2. 石家庄海山实业发展总公司,河北 石家庄 050299
- 3. 上海复合材料科技有限公司,上海 201112
- 4. 南京诺威尔光电系统有限公司,江苏 南京 210014
- 折叠
摘要
Abstract
Honeycomb structures are widely valued for their light weight and resistance to high-temperature,corrosion,and impact,making them important weight reduction materials in aerospace and other fields.However,various types of defects can arise during the manufacturing and service stages of honeycomb structures,particularly internal corrosion caused by moisture ingress and defects such as adhesive layer aging and debonding.Therefore,a honeycomb-structure defect classification and recognition method based on a bidirectional long short-term memory network(Bi-LSTM)is proposed by combining infrared thermal wave imaging technology and recurrent neural networks.The Bi-LSTM model was used to identify and judge the logarithmic temperature-time curve of infrared sequence images.The focal loss and 5-point connection method improved the effective recognition accuracy of the system by effectively solving the problem of imbalanced positive and negative samples which can lead to inaccurate background recognition and low recognition accuracy.Experimental analysis showed that the Bi-LSTM model had an accuracy of 99%in distinguishing water and glue in honeycomb structures and can thus be effectively applied for their detection and recognition.关键词
红外热波成像/双向长短期记忆网络/蜂窝结构/缺陷分类Key words
infrared thermography/bi-directional long short-term memory/honeycomb structural/defect classification分类
矿业与冶金引用本文复制引用
章林强,安奎远,郑金华,盛涛,江海军..基于Bi-LSTM的红外热波成像对蜂窝结构缺陷分类研究[J].红外技术,2026,48(2):226-233,8.
