文章基本信息

标题：Deep Learning based Crack Growth Analysis for Structural Health Monitoring
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作者：A. CHAMBON ; A. BELLAOUCHOU ; V. ATAMURADOV 等
期刊名称：IFAC PapersOnLine
印刷版ISSN：2405-8963
出版年度：2022
卷号：55
期号：10
页码：3268-3273
DOI：10.1016/j.ifacol.2022.10.133
语种：English
出版社：Elsevier
摘要：This paper presents an ensemble deep learning (DL) based structural health monitoring approach for complex systems. The proposed methodology consists of crack detection and crack growth prediction. An ensemble DL-based image segmentation technique, which is ResNet -UNet, has been developed to detect the existence of a crack pattern. The ensemble technique has very good performance in image classification, object detection and image segmentation problems. Once a crack has been detected from the image, the same image is put forward into crack length extraction phase. The pixel-wise crack length extraction technique tries to extract crack length via counting the binary pixel values corresponding to the crack region. The ARMA time series forecasting model is then trained on crack length feature to estimate remaining-useful-life (RUL) of crack surface. The proposed approach has been validated on airplane fuselage data set. The proposed approach is very promising in structural health monitoring of complex systems.
关键词：Structural health monitoring;ResNet;U-Net;crack detection;growth analysis;ARMA;RUL prediction;airplane fuselage crack detection;predictive maintenance