A CNN-based transfer learning method for leakage detection of pipeline under multiple working conditions with AE signals

Pipeline leakage detection is a crucial part of pipeline integrity management. Acoustic emission (AE) based leakage detection is widely used in this field. The latest detection methods are combined AE with convolutional neural networks. However, these methods are often confined to the complex signal processing and computing power and only target specific working conditions. To address these issues, this study proposes a convolutional neural network-based transfer learning (CNN-TL) method for pipeline leakage detection under multiple working conditions. Seven AE datasets are collected from pipeline leakage experiments under different work environments, transporting medium, and fluid pressure. The proposed method converted raw AE signals into threechannel images by a novel conversion method, which avoids reliance on expert knowledge and complex signal processing. CNN-TL is investigated by two different approaches, feature-based CNN-TL and parameterbased CNN-TL. The following nine pre-trained CNN models are used to select the optimal CNN-TL model: Alexnet, Squeezenet, Vgg19, Googlenet, Inceptionv3, Mobilenetv2, Xception, Resnet101, and Densenet201. Results show that the proposed feature-based CNN-TL method significantly outperformed parameter-based CNNTL and traditional CNN methods, especially on two-phase flow datasets. The highest accuracy of seven AE datasets obtained by the feature-based CNN-TL methods are 100.00%, 100.00%, 100.00%, 99.33%, 85.67%, 87.67%, 74.33%, 83.33% respectively. Moreover, the computation time of proposed method is 16.78 s on average by using the best layers in feature-based CNN-TL. It can be concluded that the proposed method does not rely on signal processing, requires less computational power, and can accomplish accurate detection of pipeline leaks under multiple working conditions.