在板中自主缺陷估计和通过随机过程建模预测
Mrudul Jambulkar1, Shivam Ojha2, Amit Shelke2
1Department of Electrical Engineering, Indian Institute of Technology Bombay, Powai, Mumbai, Maharashtra, 400076, India.
Scientific reports
|August 12, 2025
概括
本研究介绍了一种无监督的机器学习方法,用于使用扫描声学显微镜检测和预测合金组件的损伤. 该方法以低计算成本准确地模拟缺陷进展,增强结构健康监测.
科学领域:
- 材料科学与工程 材料科学与工程
- 机械工程 机械工程
- 数据科学数据科学数据科学
背景情况:
- 合金对于轻量化工程至关重要,需要强大的损害评估方法.
- 现有的监督机器学习 (ML) 对损害检测的方法面临诸如大数据需求,过度装配和高计算费用等挑战.
研究的目的:
- 开发一个无监督学习框架,用于准确有效地检测和预测合金组件中的损坏.
- 在结构健康监测中克服传统监督ML方法的局限性.
主要方法:
- 利用扫描声学显微镜 (SAM) 来获取数据.
- 在时间,频率和时间频率域中通过短时间里埃转换 (STFT) 提取特征.
- 结合k-means集群用于缺陷定位/大小与多相马过程用于损伤进展建模.
主要成果:
- 在没有标记数据的情况下,实现了表面缺陷的准确定位和尺寸.
- 成功建模了随着时间的推移损伤的随机进展.
- 在估计缺陷几何和预测轨迹的低计算复杂度方面表现出高精度.
结论:
- 拟议的无监督框架为损害检测和预后提供了一个可解释和可概括的解决方案.
- 该方法显示了在安全关键应用中实时结构健康监测 (SHM) 的巨大潜力.
- 该方法可以适应各种材料,进步非破坏性测试领域.
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