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In a submarine liquid pipeline, the timely detection of tiny amounts of leakage (less than 1 L/min) can have a critical impact on pipeline safety. Hence, a method based on a passive acoustic internal detection for pipeline small leakages was established. Firstly, the variation laws between leakage rates and sound power levels with different leakage apertures and internal pressures were simulated using Ansys Fluent 19.0 software. Furthermore, an experimental platform for pipeline small leakage detection was constructed. The acoustic experiments of small leakage detection were carried out under different leakage apertures and internal pressures using a high-sensitivity acoustic sensor placed into the pipeline. It was found that consistency between the experimental results and the finite element simulation results was established. A novel improved empirical mode decomposition (IEMD) was used to denoise the leakage signals. The characteristic parameters of different signal processing fields were extracted. A novel decision tree support vector machine (DTSVM), based on parameter optimization, was used to construct a classification model to identify small leakages through different apertures. The results show that compared with three commonly used models, the accuracy of this novel model of pipeline small leakage detection was 92.2%. This model was found to improve the small leakages detection rate of different apertures and also provided a theoretical basis for developing an acoustic internal detector for submarine liquid pipelines.

Study on small leaks in submarine liquid pipelines based on passive acoustic internal detection method