使用声波发射技术基于卷积神经网络的疲劳裂长度估计.
Asaad Migot1,2, Ahmed Saaudi3,2, Roshan Joseph4
1Department of Petroleum and Gas Engineering, College of Engineering, University of Thi-Qar, Nasiriyah 64001, Iraq.
Sensors (Basel, Switzerland)
|January 28, 2026
概括
这项研究使用深度学习和声辐射 (AE) 信号来估计金属板的疲劳裂长度. 使用卷积神经网络 (CNN) 的转移学习实现了99%的准确性,增强了结构性健康监测.
科学领域:
- 材料科学 材料科学 材料科学
- 机械工程 机械工程
- 人工智能的人工智能
背景情况:
- 疲劳裂传播是工程结构中关键的故障机制.
- 有效的监控对于及时维护和防止灾难性故障至关重要.
- 声辐射 (AE) 信号提供了一种有希望的非破坏性方法来检测和分析裂的生长.
研究的目的:
- 开发一个深度学习框架,用AE信号来估计金属板中的疲劳骨折长度.
- 调查卷积神经网络 (CNN) 的有效性,并转移学习来分析AE数据.
- 通过数据驱动的方法增强结构性健康监测能力.
主要方法:
- 使用Choi-Williams分布,AE波形被转化为时间频率图像.
- 基于CNN的模型被用于特征提取,K-means集群分类疲劳长度.
- 转移学习模型 (ResNet50V2,VGG16) 与定制CNN进行骨折长度分类进行了比较.
主要成果:
- 该AE数据集已成功集群,使用PCA可视化数据点的近距离.
- 美国有线电视新闻网的模型准确地将断裂长度分为三个不同的范围.
- 转移学习模型实现了显著更高的准确性 (约. 99%) 与传统的CNN (约. 93%的人).
结论:
- 深度学习,特别是转移学习,对于分析AE数据非常有效.
- 在理解AE信号以监测疲劳裂方面,CNN表现出强大的能力.
- 拟议的框架推进了对金属元件的数据驱动结构健康监测.
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