Novel feature extraction method of pipeline signals based on multi-scale dispersion entropy partial mean of multi-modal component

This paper considers the problem of feature extraction of pipeline acoustic signals under different working conditions. A novel method is proposed based on multi-scale dispersion entropy partial mean (MDEPM) of multi-modal component to extract features of pipeline signals. First, variational mode decomposition (VMD) algorithm is applied to decompose the acoustic signals to obtain several mode components. Then, Kolmogorov–Smirnov distance (KSD) is introduced as the index to measure the correlation between each mode component and the original signal, and the mode component with a smaller KSD is selected as the feature component. Finally, the MDEPM of the feature component is calculated so as to form the feature vector, which realizes signal feature extraction. In experiment, 13 different condition signals collected are divided into three categories, the experimental results show that the proposed method could extract the characteristics of the different pipeline acoustic signals. Furthermore, the extracted features could be accurately identified and classified by extreme learning machine (ELM) under different working conditions, and then through comparing with other methods, the feasibility and superiority of the proposed method are verified. •To select the feature components by analyzing the Kolmogorov–Smirnov distance between each mode component and the original signal.•The multi-scale dispersion entropy partial mean could characterize the features of different pipeline signals at different scales.•The method of MMC-MDEPM could be used to extract the different signal features.