在强烈的噪音背景下,特征提取和对天然气管道流动噪声信号的模式识别
Enbin Liu1, Chang Lu1, Zhaorong Wen2
1School of Petroleum and Natural Gas Engineering, Southwest Petroleum University, Chengdu, China.
PeerJ. Computer science
|July 10, 2024
概括
本研究介绍了天然气管道流量噪声的特征提取和模式识别方法. 该技术在检测泄漏方面达到97%的准确性,提高了城市管道安全.
科学领域:
- 声学和信号处理
- 土木和环境工程 土木和环境工程
- 人工智能和机器学习
背景情况:
- 中国城市天然气基础设施的快速扩张涉及复杂的地下环境和并存的管道.
- 由于潜在的气体泄漏,未知的地下条件和管道破裂带来了重大安全风险.
- 现有基础设施需要加速老化评估和转型的方法,以确保安全和经济发展.
研究的目的:
- 开发天然气管道流量噪声信号的特征提取和模式识别方法.
- 为评估管道完整性和促进基础设施更新提供一个可行的方法.
- 提高城市天然气管道网络的安全性和可靠性.
主要方法:
- 结合实验测试和数值模拟,创建管道流动噪声信号的数据库.
- 利用主要组件分析 (PCA) 来从流量噪声信号中提取特征特征.
- 开发了一种反向传播神经网络 (BPNN) 模型,用于噪声信号的分类和识别.
主要成果:
- 开发了一个用于特征提取的数学模型.
- 开发的方法实现了高达97%的识别准确度,即使在强大的背景噪声下.
- 数字模拟通过实验验证地面流噪声信号来验证.
结论:
- 拟议的声学特征分析方法为安全的城市天然气管道建设和运营提供了指导.
- 特性提取和模式识别技术为检测和识别城市天然气管道流量噪声提供了理论基础和技术支持.
- 这项研究有助于确保城市天然气管道网络的安全运行,并促进经济发展.
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