减少传感器对声波发射数据的影响,通过数据合并创建一个可概括的库
Xi Chen1, Nathalie Godin1, Aurélien Doitrand1
1INSA-Lyon, Universite Claude Bernard Lyon 1, CNRS, MATEIS, UMR5510, 69621 Villeurbanne, France.
Sensors (Basel, Switzerland)
|April 27, 2024
概括
这项研究调查了传感器如何影响声辐射 (AE) 信号. 使用主要组件分析和Z-score规范化的新方法减少了传感器效应,为机器学习数据库提供一致的AE数据.
科学领域:
- 材料科学 材料科学 材料科学
- 非破坏性测试 不破坏性测试
- 信号处理 信号处理
背景情况:
- 声波发射 (AE) 是一种强大的非破坏性测试技术.
- 传感器的可变性显著影响AE信号的解释和数据的可复制性.
- 标准化AE数据采集对于可靠的分析和数据库开发至关重要.
研究的目的:
- 分析不同传感器对声辐射信号的影响.
- 开发一种方法来减轻 AE 测量中传感器诱导的影响.
- 为机器学习应用程序创建通用的AE签名库.
主要方法:
- 使用PMMA板上笔断的控制AE实验.
- 对比各种AE传感器用于板波再现.
- 主要组件分析 (PCA) 和Z-score规范化用于数据处理的应用.
- 使用克鲁斯卡尔-瓦利斯测试进行统计分析和异常值的识别.
主要成果:
- 不同的AE传感器表现出不同的响应,导致AE描述符和测试结果的变化.
- 拟议的方法有效地减少了传感器效应,在所有传感器中产生了一个共同的描述符.
- 为了实现一致的AE数据分布,Z-score规范化和异常值的识别是关键.
结论:
- 传感器的选择和数据处理显著影响AE签名分析.
- 开发的程序标准化了AE数据,促进了描述符的合并到一个统一的库.
- 这项工作为通用AE签名库和基于机器学习的AE源分类铺平了道路.
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