Pipeline Multipoint Leakage Detection Method Based on KKL-MSCNN

To address the issue of multipoint leakage detection in energy transportation systems, a multiscale convolutional neural network based on kurtosis and Kullback-Leibler divergence (KKL-MSCNN) was proposed for multipoint leakage detection in energy transportation systems. Initially, the collected infrasound data undergo complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN). Furthermore, a hierarchical processing technique for intrinsic mode functions (IMFs) is proposed to reconstruct IMFs at two different feature levels. Following this, a lightweight MSCNN is constructed, comprising two channels. The reconstructed IMF features are then extracted through serial and parallel convolution at varying scales, facilitating the completion of pipeline leakage level classification. In comparison to conventional methods, the proposed approach achieves a significant 20.26% increase in accuracy for multipoint leakage detection.