基于深度学习的机器学习分析,用于碳纤维增强聚合物的疲劳诊断
Ahmed Salah Al-Shati1, Thamer J Mohammed1
1Department of Chemical Engineering Processes, College of Chemical Engineering, University of Technology, Baghdad, Iraq.
PloS one
|January 9, 2026
概括
一个新的混合深度学习模型准确地对碳纤维增强聚合物 (CFRP) 结构中的疲劳进行分类. 这种先进的框架增强了结构健康监测 (SHM) 以改善安全和维护决策.
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:
- 碳纤维增强聚合物 (CFRP) 结构中的疲劳退化对长期结构健康监测 (SHM) 构成重大挑战.
- 需要可靠的方法来检测和分类CFRP复合材料的疲劳状态,以确保结构完整性.
研究的目的:
- 为CFRP复合材料准确分类疲劳状态提出混合深度学习框架.
- 为了利用基于传感器的监控数据来实现先进的SHM应用.
主要方法:
- 一个混合深度学习框架,它结合了用于空间模式提取的1D-CNN和用于捕获时间依赖的xLSTM网络.
- 基于相互信息的特征选择和基于包装的组合分类器,用于强大的疲劳歧视.
主要成果:
- 拟议的框架在NASA-CFRP数据集上实现了99%的平均分类准确性.
- 已证明有效的时空特征提取和融合用于疲劳诊断.
结论:
- 开发的混合深度学习方法为CFRP结构的疲劳诊断提供了可靠的解决方案.
- 该框架支持维护决策,在基于膜的气体分离系统中具有潜在的应用.
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