第37卷第11期 2016年11月
仪器仪表学报 Chinese Journal of Scientific Instrument
Vol. 37 No. 11 Nov.2016
基于BP神经网络的管道泄漏声信号识别方法研究
焦敬品，李勇强，吴斌，何存富
（北京工业大学机械工程与应用电子技术学院北京100124）
摘要：针对城市供水管网泄漏检测需求，进行了泄漏声信号识别方法研究。分析了泄漏信号的时域、频域及波形特点，提取出可用于泄漏信号表征的20种特征参数；基于提取的泄漏声信号特征参数，构建了泄漏声信号BP神经网络识别系统；研究了神经网络结构（隐含节点数、传递函数、学习率及输入参数的数量和种类对泄漏信号识别效果的影响，并优化出最佳的神经网络结构及输人参数。在以上研究基础上，利用优化后的神经网络对实验室及现场管道泄漏信号进行了交叉训练和识别，结果表明，提出的基于泄漏特征参数的神经网络系统具有较高的可靠性和普适性，可以很好地实现不同场景下泄漏信号的交叉识别，
整体识别率达92.5%，为解决不同工况下泄漏信号识别做了有益的探索。关键词：管道泄漏；声发射：特征提取；BP神经网络；信号识别
中图分类号：TB52*9TH825
文献标识码：A国家标准学科分类代码：460.40
Researchonacousticsignalrecognitionmethodforpipelineleakage
withBPneuralnetwork
Jiao Jingpin,Li Yongqiang,WuBin,He Cunfu
( College of Mechanical Engineering and Application Electronics Technology, Beijing University of Technology, Bejing 100124, China) Abstract : In view of the urban water supply pipeline leak detection, the method of leak acoustic signal recognition is studied. The features of time domain, frequency domain and waveform of the leakage signals are analyzed, 20 features which can be used to characterize the leakage signal are extracted. Based on the features , the BP neural network identification system for leakage acoustic signal is constructed. The influences of the neural network structure ( the number of hidden nodes, transfer function, learning rate) and the number and type of the input parameters on the leakage signal recognition performance are studied, the best structure and input parameters of the neural network are optimized. Based on the above research, the optimized neural network was used to cross train and identify the leak signal of the laboratory and water supply pipelines. The overall recognition rate reaches 92. 5% . The results show that the neural network system based on the leakage features has high reliability and universality, which can be well recognition the leakage signals under different
scenarios. The research work has done a useful exploration to solve the leakage signal identification under different working conditions Keywords: pipeline leakage; acoustic emission; feature extraction; BP neural network; signal identification
现对管道泄漏点的精确定位，对于维护管网的安全运行
1引言
城市供水管网是现代社会的重要基础设施。受环境腐蚀、土壤松动以及人为破坏等因素影响，供水管道易出现破损而产生泄漏"。供水管网的泄漏不仅会造成水资源的浪费，而且会冲刷周边地基造成地面塌陷等次生灾害事故，因此，研究管网泄漏的检测理论、检测方法，实
避免资源的浪费，有者重要的理论意义和应用价值。
针对城市管网安全运行需要，国内外学者开展了管道泄漏检测方法相关研究工作，发展了多种泄漏检测方法。根据检测原理，泄漏检测方法可分为非声学检测法和声学检测法两类。非声学检测法主要包括：分区检漏法[2]、探地雷达法[3]、红外热成像法、负压波法(4等。目前，这些检测方法在检测效率、实用性等方
Received Date:2016-06
收稿日期：2016-06
*基金项台：有繁科学基金（11572010，11272017）、国家重点研发计划（2016YFF0203002）项目资助