基于CNN的供热管道泄漏识别方法研究

为了快速识别出供热管道泄漏故障,以管道泄漏时产生的负压波特征,研究提出了利用卷积神经网络(CNN)识别压力数据的管道漏损诊断方法。通过搭建供热管道实验平台,采集了正常、泄漏、调阀三种工况下的压力数据作为卷积神经网络的训练集和测试集。对原始数据进行小波降噪处理,应用硬阈值的处理方法有效消除了噪声信号,同时在调阀工况中出现了强化特征,增强了卷积神经网络的分类能力。针对一维数据特征采用改进的AlexNet卷积网络模型对采集的数据进行学习及识别。结果发现,在对实验室数据测试中,CNN模型的平均识别正确率达98.39%。在对实际管网的验证中,三个热力站的泄漏数据均被正确识别,表明CNN模型具备良好的故障诊断能力。

Research on leakage identification method of heating pipeline based on CNN

In order to quickly identify the leakage fault of heating pipeline, a method of pipeline leakage diagnosis using convolutional neural network (CNN) to identify the pressure data was proposed for the negative pressure wave characteristics of pipeline leakage.By setting up the experimental platform of heating pipeline, the pressure data under normal, leakage and regulating valve conditions are collected as the training set and test set of convolutional neural network.The original data are denoised by wavelet, and the hard threshold processing method is used to effectively eliminate the noise signal. Meanwhile, the enhancement feature appears in the valve condition, which is helpful to enhance the classification ability of the convolutional neural network.The improved AlexNet convolution network model is used to learn and identify the collected data.The results show that the average recognition accuracy of CNN model is 98.3% in laboratory data testing. In the verification of the actual pipe network, the leakage data of the three thermal stations were correctly identified, indicating that the CNN model has good fault diagnosis ability.