EMDet: An entropy blending and multi-link parallel feature enhancement detection model for gas pipeline weak leakage detection

Abstract

To improve the detection accuracy of gas pipeline leakage detection (PLD) especially when the leakage is weak, we propose a hybrid deep learning model, named EMDet in this paper, which incorporates two proposed algorithms called entropy blending (EB) and multi-link parallel feature enhancement (MPFE). Firstly, the EB algorithm is used to determine the optimal number of decomposition layer Lo and the optimal intrinsic mode function Io for variational mode decomposition (VMD), elevating the leakage-related information in Io. Then, three coarse-grained scales have been applied to extract effective and robust features, where the coarse-grained scales are calculated based on the sizes required by the MPFE algorithm. Moreover, the MPFE algorithm is proposed to enhance the representation of features, thereby improving the detection accuracy of PLD. After that, the performance of the proposed EMDet has been evaluated on the negative pressure wave (NPW) data collected from realistic urban environments. Compared with the state-of-the-art PLD models, the performance of EMDet not only reaches the total accuracy of 98.67%, the F1-score of 98.65%, and the fault detection rate of 98.63%, but also reduces the false alarm rate to 0.67% and the missing alarm rate to 1.37%. Finally, the robustness results demonstrate the superior performance and broad application prospects of EMDet for PLD.