一种混合深度学习方法:整合短时间里埃变换和连续波形变换,以改进管道泄漏检测
Muhammad Farooq Siddique1, Zahoor Ahmad1, Niamat Ullah1
1Department of Electrical, Electronics and Computer Engineering, University of Ulsan, Ulsan 44610, Republic of Korea.
Sensors (Basel, Switzerland)
|October 14, 2023
概括
本研究介绍了一种混合深度学习模型,用于使用增强的短时间里埃变换 (STFT) 和连续波量变换 (CWT) 谱图来检测管道泄漏. 该方法准确地识别和分类泄漏,提高管道安全.
科学领域:
- 工程 工程师 工程师 工程师
- 计算机科学 计算机科学
- 信号处理 信号处理
背景情况:
- 管道完整性对于流体运输安全至关重要.
- 有效的泄漏检测对于防止环境损害和经济损失至关重要.
- 传统的方法往往难以处理复杂的声学数据和噪声.
研究的目的:
- 开发一个强大的混合深度学习模型,用于准确的管道泄漏检测.
- 使用STFT和CWT增强信号处理,以改善特征提取.
- 为了验证模型在实时泄漏识别和分类中的有效性.
主要方法:
- 一种混合深度学习方法,将STFT和CWT结合起来用于声信号分析.
- 应用Sobel和波形无声过器来提高信号质量.
- 使用卷积神经网络 (CNN) 提取特征.
- 通过主要组件分析 (PCA) 减少尺寸.
- 使用t分布式静态邻居嵌入 (t-SNE) 和人工神经网络 (ANN) 进行泄漏分类.
主要成果:
- 混合模型在检测和分类管道泄漏方面表现出高精度和可靠性.
- 结合STFT和CWT的方法有效地捕获了光谱和时间信号细节.
- 功能空间维度减少提高了计算效率和区分能力.
结论:
- 拟议的混合深度学习模型为实时管道泄漏检测提供了一个有希望的解决方案.
- 这种方法对推进管道监测和维护战略做出了重大贡献.
- 该方法显示了在各种工业流体输送系统中应用的潜力.
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