Natural gas pipeline leak diagnosis based on improved variational modal decomposition and locally linear embedding feature extraction method

Natural gas pipeline leaks can cause serious hazards to natural gas transportation and pose considerable risks to the environment and the safety of residents. Therefore, feature extraction of pipeline signals is crucial in natural gas pipeline leak detection. However, the quality of feature extraction directly affects the effectiveness of pipeline leak detection. Therefore, this paper proposes a pipeline leakage feature extraction method based on variable mode decomposition (VMD) and local linear embedding (LLE). First, the pipeline signal is decomposed into several modal components by VMD; then, the dispersion entropy is used to select the feature modes. Secondly, the time-frequency domain features of different components are extracted to construct a high-dimensional feature matrix, which LLE reduces to obtain the classified low-dimensional feature vectors. Finally, the extracted feature vectors are used to train and test the support vector machine (SVM). By analyzing the experimental results, it can be seen that the proposed method can classify pipeline signals with an accuracy of up to 95%, which effectively solves the problem of false alarms and missed alarms in pipeline leakage detection.