在不同振动条件下的板损坏检测领域分离的囊网络
Qi Jiang1, Xin Huang1, Wenzhong Qu1
1Department of Engineering Mechanics, Wuhan University, Wuhan 430072, China.
Ultrasonics
|May 17, 2025
概括
一个新的域隔离囊网络 (DS-CapsNet) 能够有效地检测合金飞机部件的损坏,克服环境振动干扰,提高结构健康监测的准确性.
科学领域:
- 材料科学 材料科学 材料科学
- 航空航天工程 航空航天工程
- 机器学习 机器学习
背景情况:
- 由于其强度和耐疲劳性,合金对飞机至关重要.
- 超声波引导波用于金属板的结构健康监测 (SHM).
- 环境振动显著降低了SHM中损坏检测的准确性.
研究的目的:
- 开发一种可靠的方法来检测合金板的损坏.
- 为了减轻环境振动对SHM精度的影响.
- 提高关键飞机结构损坏识别的可靠性.
主要方法:
- 开发了一个新的域分离囊网络 (DS-CapsNet).
- DS-CapsNet将囊网络与用于特征提取和重建的注意力机制集成在一起.
- 使用动态对抗因素和多头自我注意力来提高稳定性和特征捕捉.
主要成果:
- 与传统和深度学习基线模型相比,DS-CapsNet表现出卓越的性能.
- 该模型在不同的环境振动条件下在损坏检测方面表现出显著的稳定性.
- 功能对齐和复杂损坏功能捕获得到有效增强.
结论:
- DS-CapsNet提供了一种强大的解决方案,用于准确检测合金结构中的损坏.
- 拟议的方法显著减少了环境振动对SHM的影响.
- 这一进步有望提高飞机的安全性和维护.
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