使用机器学习模型对航空航天结构结构健康状况进行能源评估和被动损坏检测
Francesco Nicassio1, Flavio Dipietrangelo1, Antonella Gaspari2
1Department of Engineering for Innovation, University of Salento, Via per Monteroni, 73100 Lecce, Italy.
Sensors (Basel, Switzerland)
|August 28, 2025
概括
机器学习算法准确地评估对航空航天结构的影响. 这种人工智能方法可以在10%的误差范围内预测冲击能量,并以超过95%的准确度识别受损区域,用于结构健康监测.
科学领域:
- 航空航天工程
- 材料科学
- 人工智能
背景情况:
- 结构健康监测 (SHM) 在航空航天领域越来越依赖人工智能 (AI).
- 评估事件对航空航天结构的影响对于安全和维护至关重要.
研究的目的:
- 训练机器学习算法以识别和描述航空航天板上的结构冲击.
- 评估人工智能在预测撞击严重程度和检测事先存在的损害方面的有效性.
主要方法:
- 使用钢筋板上的压力传感器的振动数据开发冲击数据集.
- 浅层神经网络用于回归 (冲击能量) 和分类 (损伤检测).
- 根据现象的物理和机械解释进行特征选择.
主要成果:
- 准确预测撞击能量,误差不超过10%.
- 以超过95%的准确度精确识别以前受损的区域.
- 对人工智能方法的计算效率的证明.
结论:
- 机器学习为现实航空结构中的SHM提供了有效和高效的工具.
- 人工智能可以有效地描述影响,增强结构完整性的评估.
- 这项研究验证了人工智能在航空航天应用中的实时监控和损坏检测.
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