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通过声波发射信号分类来预测保护涂层解离的故障严重程度

Noor A'in A Rahman^1,2, Zazilah May^1,3, Rabeea Jaffari⁴

¹Department of Electrical and Electronics Engineering, Universiti Teknologi PETRONAS, Seri Iskandar 32610, Malaysia.

Sensors (Basel, Switzerland)

|August 12, 2023

概括

机器学习模型有效地使用声辐射 (AE) 信号预测保护涂层的解离. 决策森林模型在分类涂层故障严重性方面取得了很高的准确性,为传统方法提供了更快的替代方案.

科学领域:

材料科学材料科学材料科学
工程工程师工程师工程师
数据科学数据科学数据科学

背景情况:

结构健康监测使用声辐射 (AE) 信号来检测基础设施中的故障.
由于材料特定的特征,从涂层故障 (如解结) 来分类AE信号具有挑战性.
目前用于涂层故障分析的方法耗时且昂贵.

研究的目的:

评估机器学习 (ML) 分类模型,以预测使用AE信号的环氧基保护涂层解结.
开发一种更有效,更准确的方法来评估涂层故障的严重程度.
为了应对来自涂层故障的AE信号分类的挑战.

主要方法:

研究人员使用涂层碳钢板进行了一项实验,用于在涂层解离过程中收集AE信号.
处理AE信号以创建用于训练ML分类模型的数据集.
特性提取,数据失衡处理和偏差减少技术应用于ML模型.

主要成果:

决策森林分类模型在分类涂层脱离严重程度方面表现出卓越的表现.
决策森林模型实现了高性能指标:准确率为99.48%,精度为98.76%,回忆率为97.58%,F1得分为98.17%.
ML模型有效地将涂层脱离分为三个严重程度等级.

结论:

机器学习分类模型有效地预测使用AE信号的保护涂层的故障严重性.
决策森林模型显示了实时结构健康监测和缺陷检测的重大前景.
这种方法为传统的涂层完整性评估方法提供了更有效,更准确的替代方案.

关键词:

声学排放的声音排放.这是分类分类的分类.涂层的解脱涂层的解脱机器学习是机器学习.

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