基于BP神经网络的管道泄漏声信号识别方法研究

Research on acoustic signal recognition method for pipeline leakage with BP neural network

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入参数。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。

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.