Valve internal leakage is easily found because of various defects resulting from environmental factors and load fluctuation. The timely detection of valve internal leakage is of great significance to the safe operation of pipelines. As an effective means for detecting valve internal leakage, the acoustic emission technique is characterized by nonintrusive and strong anti-interference ability, which can realize the in situ monitoring of the valve running status in real time. In this paper, acoustic emission signals from an internal leaking valve were obtained experimentally. Then, the dimensionality reduction technology based on factor analysis was introduced to the processing of valve internal leakage detection data. Next, the wavelet decomposition was carried out to decompose the sample feature set into four subsets. Finally, the decomposed sample feature sets were inputted into the error backpropagation (BP) neural network quantitative model, respectively. The optimized results show that the predicted internal leakage rate by the wavelet-BP neural network model has good precision with an error of less than 10%. The wavelet-BP neural network model can realize the analysis of the valve internal leakage rate quantitatively and has good robustness, which provides technical support and guarantees the safe operation of natural gas pipeline valves.

中文翻译：

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基于声波分析和小波BP神经网络的阀门内部泄漏率量化

由于环境因素和负载波动导致的各种缺陷，很容易发现阀门内部泄漏。及时发现阀门内部泄漏对管道的安全运行具有重要意义。声发射技术作为一种检测阀门内部泄漏的有效手段，具有非干扰性强，抗干扰能力强等特点，可以实现对阀门运行状态的实时监测。本文通过实验获得了来自内部泄漏阀的声发射信号。然后，将基于因子分析的降维技术引入到阀门内部泄漏检测数据的处理中。接下来，进行小波分解以将样本特征集分解为四个子集。最后，将分解后的样本特征集分别输入到误差反向传播（BP）神经网络定量模型中。优化结果表明，小波BP神经网络模型预测的内部泄漏率具有良好的精度，误差小于10％。小波BP神经网络模型可以定量分析阀门内部泄漏率，具有良好的鲁棒性，为天然气管道阀门的安全运行提供技术支持。