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基于机器视觉的疲劳裂纹扩展系统。

Machine Vision-Based Fatigue Crack Propagation System.

机构信息

Department of Control Systems and Instrumentation, VŠB-Technical University of Ostrava, 708 00 Ostrava, Czech Republic.

出版信息

Sensors (Basel). 2022 Sep 10;22(18):6852. doi: 10.3390/s22186852.

DOI:10.3390/s22186852
PMID:36146201
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC9504353/
Abstract

This paper introduces a machine vision-based system promising low-cost solution for detecting a fatigue crack propagation caused by alternating mechanical stresses. The fatigue crack in technical components usually starts on surfaces at stress concentration points. The presented system was designed to substitute a strain gauge sensor-based measurement using an industrial camera in cooperation with branding software. This paper presents implementation of a machine vision system and algorithm outputs taking on fatigue crack propagation samples.

摘要

本文介绍了一种基于机器视觉的系统，可为检测交变机械应力引起的疲劳裂纹扩展提供低成本的解决方案。技术部件中的疲劳裂纹通常始于表面的应力集中点。所提出的系统旨在替代基于应变计传感器的测量，使用工业相机与打标软件配合使用。本文介绍了机器视觉系统的实现以及针对疲劳裂纹扩展样本的算法输出。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/77ed/9504353/0841540b71e3/sensors-22-06852-g013.jpg
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https://cdn.ncbi.nlm.nih.gov/pmc/blobs/77ed/9504353/0abfe09c6fea/sensors-22-06852-g004.jpg
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https://cdn.ncbi.nlm.nih.gov/pmc/blobs/77ed/9504353/77ee4106c1b4/sensors-22-06852-g006.jpg
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https://cdn.ncbi.nlm.nih.gov/pmc/blobs/77ed/9504353/0841540b71e3/sensors-22-06852-g013.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/77ed/9504353/218147d0d3a8/sensors-22-06852-g001.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/77ed/9504353/200aaaec8b70/sensors-22-06852-g002.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/77ed/9504353/2f2e7f8588d0/sensors-22-06852-g003.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/77ed/9504353/0abfe09c6fea/sensors-22-06852-g004.jpg
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https://cdn.ncbi.nlm.nih.gov/pmc/blobs/77ed/9504353/77ee4106c1b4/sensors-22-06852-g006.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/77ed/9504353/4ffe5c06b60a/sensors-22-06852-g007.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/77ed/9504353/3066b0e4d0f4/sensors-22-06852-g008.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/77ed/9504353/bde3414b9a7f/sensors-22-06852-g009.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/77ed/9504353/a266cb51610c/sensors-22-06852-g010.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/77ed/9504353/e5a1e121cd1b/sensors-22-06852-g011.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/77ed/9504353/0841540b71e3/sensors-22-06852-g013.jpg

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Coherent Fiber-Optic Sensor for Ultra-Acoustic Crack Emissions.用于超声裂纹发射的相干光纤传感器。
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Pixel-Level Fatigue Crack Segmentation in Large-Scale Images of Steel Structures Using an Encoder-Decoder Network.基于编解码器网络的钢结构大尺寸图像像素级疲劳裂纹分割。
Sensors (Basel). 2021 Jun 16;21(12):4135. doi: 10.3390/s21124135.
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Crack-Length Estimation for Structural Health Monitoring Using the High-Frequency Resonances Excited by the Energy Release during Fatigue-Crack Growth.利用疲劳裂纹扩展过程中能量释放激励的高频共振进行结构健康监测的裂纹长度估计。
Sensors (Basel). 2021 Jun 20;21(12):4221. doi: 10.3390/s21124221.
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SSVM: An Ultra-Low-Power Strain Sensing and Visualization Module for Long-Term Structural Health Monitoring.SSVM：用于长期结构健康监测的超低功耗应变传感与可视化模块。
Sensors (Basel). 2021 Mar 22;21(6):2211. doi: 10.3390/s21062211.
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A Machine Learning Approach to Model Interdependencies between Dynamic Response and Crack Propagation.一种用于模拟动态响应与裂纹扩展之间相互关系的机器学习方法。
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Vision and Deep Learning-Based Algorithms to Detect and Quantify Cracks on Concrete Surfaces from UAV Videos.基于视觉和深度学习的算法，用于从无人机视频中检测和量化混凝土表面的裂缝。
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