基于声波排放的管道泄漏检测和尺寸识别,使用定制的一维密度网
Faisal Saleem1, Zahoor Ahmad1, Muhammad Farooq Siddique1
1Department of Electrical, Electronics and Computer Engineering, University of Ulsan, Ulsan 44610, Republic of Korea.
Sensors (Basel, Switzerland)
|February 26, 2025
概括
本研究介绍了一种先进的声学排放 (AE) 管道监测系统,使用实证波纹转换 (EWT) 和DenseNet深度学习来准确检测泄漏和大小分类,显著提高工业安全.
科学领域:
- 工程 工程师 工程师 工程师
- 信号处理 信号处理
- 人工智能的人工智能
背景情况:
- 工业管道的完整性依赖于有效的泄漏检测.
- 传统方法在噪声灵敏度,适应性和计算成本方面的局限性.
- 实时监控需要强大而高效的泄漏识别解决方案.
研究的目的:
- 为精确的管道泄漏检测和尺寸分类开发一种基于声辐射 (AE) 的新方法.
- 克服传统监测技术的局限性.
- 提高工业管道的安全性和寿命.
主要方法:
- 利用经验波形变换 (EWT) 进行自适应频率分解和信号细分.
- 应用适应性值和无声化来提高信号质量.
- 采用定制的一维DenseNet深度学习模型进行特征提取和分类.
主要成果:
- 在真实世界AE数据上实现了99.76%的特殊泄漏检测准确度.
- 证明了正常运行和各种泄漏严重程度之间的可靠区分.
- 在各种环境中展示了降低计算成本的强大性能.
结论:
- 拟议的基于AE的方法与EWT和DenseNet提供了一个非常准确和高效的管道泄漏监控解决方案.
- 这种方法通过准确的泄漏检测和严重程度分类来提高运营安全和完整性.
- 该技术可以适应各种操作条件,比传统方法有了显著的进步.
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