相关概念视频
01:17Convolution Properties II
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The important convolution properties include width, area, differentiation, and integration properties.
The width property indicates that if the durations of input signals are T1 and T2, then the width of the output response equals the sum of both durations, irrespective of the shapes of the two functions. For instance, convolving two rectangular pulses with durations of 2 seconds and 1 second results in a function with a width of 3 seconds.
The area property asserts that the area under the...
The width property indicates that if the durations of input signals are T1 and T2, then the width of the output response equals the sum of both durations, irrespective of the shapes of the two functions. For instance, convolving two rectangular pulses with durations of 2 seconds and 1 second results in a function with a width of 3 seconds.
The area property asserts that the area under the...
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01:21Fatigue
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Fatigue occurs when materials rupture under repeated or fluctuating loads, even at stress levels far below their static breaking strength. It typically results in brittle failure, even for ductile materials. It is a critical consideration in designing machines and structural components subjected to repetitive or varying loads. The nature of these loadings can range from fluctuating loads like unbalanced pump impellers causing vibrations to repeatedly bending a thin steel rod wire back and forth...
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02:39Emission Spectra
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When solids, liquids, or condensed gases are heated sufficiently, they radiate some of the excess energy as light. Photons produced in this manner have a range of energies, and thereby produce a continuous spectrum in which an unbroken series of wavelengths is present.
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01:20Convolution Properties I
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Convolution computations can be simplified by utilizing their inherent properties.
The commutative property reveals that the input and the impulse response of an LTI (Linear Time-Invariant) system can be interchanged without affecting the output:
The commutative property reveals that the input and the impulse response of an LTI (Linear Time-Invariant) system can be interchanged without affecting the output:
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02:26Protein Networks
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An organism can have thousands of different proteins, and these proteins must cooperate to ensure the health of an organism. Proteins bind to other proteins and form complexes to carry out their functions. Many proteins interact with multiple other proteins creating a complex network of protein interactions.
These interactions can be represented through maps depicting protein-protein interaction networks, represented as nodes and edges. Nodes are circles that are representative of a protein,...
These interactions can be represented through maps depicting protein-protein interaction networks, represented as nodes and edges. Nodes are circles that are representative of a protein,...
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01:28Types of Non-structural Cracks in Concrete
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Non-structural cracks are primarily of three types: plastic, early-age thermal, and drying shrinkage cracks. Plastic cracks are further classified into plastic shrinkage cracks and plastic settlement cracks.
Plastic shrinkage cracks typically form within hours after the concrete is poured. The concrete's surface dries faster than the bottom, creating tensile stress that the still-plastic concrete cannot withstand, leading to diagonal or randomly patterned cracks on the concrete surface.
Plastic shrinkage cracks typically form within hours after the concrete is poured. The concrete's surface dries faster than the bottom, creating tensile stress that the still-plastic concrete cannot withstand, leading to diagonal or randomly patterned cracks on the concrete surface.
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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.