An incremental learning framework for pipeline weld crack damage identification and leakage rate prediction

Abstract

The weld crack leakage due to stress concentration and external load is a significant safety risk in pressure pipelines. Microstructural variations and dynamic propagation lead to unpredictable changes in leakage rate over time and conditions. To address the above problems, a novel framework called OILS-TCN for weld crack pattern recognition and leakage rate prediction is proposed. Firstly, the adaptive threshold optimization algorithm is introduced into the self-organizing incremental neural network to update and increase the crack leakage pattern. Secondly, the depth first search algorithm is combined with the radial basis function neural network to perform online increment labelling of the leakage state. Then, according to the attenuation characteristics of acoustic emission signals, a portable input-attention module is designed to add different weights to the input sequence. Finally, the accurate prediction of leakage rate under different conditions is realized based on the temporal convolutional network. Compared with other advanced methods, the proposed method has obvious advantages in the adaptability and accuracy of pipeline weld crack leakage rate prediction. In addition, the validity and necessity of each part of the framework proposed are discussed based on ablation experiments. The proposed method can predict the leakage rate in real time without modifying the hyperparameters of the model, and can provide a powerful guide for the online monitoring of the leakage AE technology of pressure pipeline in complex systems.

Keywords

• Leakage rate
• pipeline weld crack
• acoustic emission
• incremental learning
• temporal convolution network