Liquid-chlorine leak detection method based on power spectrum comparison

LIAN Longjie; LIN Weiguo; WU Haiyan

doi:10.3969/j.issn.0438-1157.2013.12.029

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Liquid-chlorine leak detection method based on power spectrum comparison

更新时间：2025-12-10

- Liquid-chlorine leak detection method based on power spectrum comparison
- CIESC Journal Issue 12, Pages: 4461-4467(2013)
- 作者机构：
  
  北京化工大学信息科学与技术学院,北京,100029
- 作者简介：
- 基金信息：
- DOI：10.3969/j.issn.0438-1157.2013.12.029
  CLC： E973
- Online First：05 December 2013，
  
  Published：05 December 2013
- 稿件说明：
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LIAN Longjie, LIN Weiguo, WU Haiyan. Liquid-chlorine leak detection method based on power spectrum comparison[J]. CIESC Journal, 2013, (12): 4461-4467.
DOI：

LIAN Longjie, LIN Weiguo, WU Haiyan. Liquid-chlorine leak detection method based on power spectrum comparison[J]. CIESC Journal, 2013, (12): 4461-4467. DOI： 10.3969/j.issn.0438-1157.2013.12.029.

摘要

针对液氯输送管道泄漏检测难以获取泄漏信号样本用于特征提取和诊断建模的难题，提出了一种基于功率谱比对的无泄漏信号样本管道泄漏检测方法。鉴于相同类型传感器应用于不同介质时表现出相似的输出信号特性，通过分析、比较现有的气体管道泄漏信号和正常信号的频域特征，提出了基于双移动窗和功率谱比对的频域特征提取方法。以液氮模拟输送管道正常信号为目标类数据，在无泄漏信号样本条件下建立了基于SVDD的面向液氯输送管道泄漏检测的诊断模型，实现泄漏的可靠检测。现场模拟检验结果表明：该方法能够有效实现液氯输送管道泄漏检测。

Abstract

In liquid-chlorine pipeline leak detection

leak signals are difficult to obtain for feature extraction and modeling

a pipeline leak detection method with none leakage samples based on power spectrum comparison is proposed.In view of the same type of sensors have the similar output signal characteristics used in different transportation medium

by analyzing and comparing the frequency domain features of existing leak signals and normal signals of gas pipeline

frequency domain feature extraction method based on double moving windows and power spectrum comparison is proposed.Taking the normal signals of liquid-nitrogen pipeline as the target object

leak diagnose model for liquid-chlorine pipeline leak detection based on SVDD with none leakage samples is completed

and reliable leak detection is implemented.The results of online application show that this method can detect pipeline leak effectively.

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