基于半分布式纤维传感器的管道健康监测方法:生成和分析基于物理的模拟数据集以进行分类
Pengdi Zhang1, Abhishek Venketeswaran1, Ruishu F Wright2
1Mechanical Engineering and Materials Science, University of Pittsburgh, 3700 O'Hara Street, Pittsburgh, PA 15261, USA.
Sensors (Basel, Switzerland)
|July 8, 2023
概括
本研究介绍了一个框架,使用模拟分布式声学传感 (DAS) 数据来检测管道损坏,如腐蚀. 该研究强调了传感系统和噪声影响分类准确性如何用于可靠的管道监控.
科学领域:
- 管道完整性和结构健康监测.
- 声学传感技术和数据分析.
- 工业基础设施中的机械故障检测.
背景情况:
- 管道的机械损坏带来了重大风险,需要先进的检测方法.
- 分布式声学传感 (DAS) 为监测管道完整性提供了一个有希望的方法.
- 生成现实的模拟数据对于训练和验证检测算法至关重要.
研究的目的:
- 开发一个框架,用模拟的DAS响应来检测机械管道损坏.
- 创建一个强大的数据集来分类管道事件,如接,剪贴和腐蚀.
- 分析传感系统和噪声对分类性能的影响.
主要方法:
- 模拟超声波引导波 (UGW) 响应并将其转化为DAS或准DAS信号.
- 为管道事件分类生成一个物理稳固的数据集.
- 评估不同传感器配置和噪声水平对检测准确性的影响.
主要成果:
- 该框架成功生成模拟的DAS数据用于管道损坏检测.
- 经证明,分类性能取决于所选择的传感系统和噪声条件.
- 该研究表明,在现实的噪音水平下,各种传感器部署的稳定性.
结论:
- 开发的框架为检测机械管道损坏提供了一种可靠的方法.
- 模拟的DAS数据对于培训和验证管道监控系统是有效的.
- 了解传感系统和噪声之间的相互作用对于有效的管道检查至关重要.
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