Abstract

Due to fluctuations in flow rate, pressure, and pump operating states, as well as environmental disturbances such as temperature variations and structural vibrations, pipeline leakage signals exhibit significant nonstationary characteristics. The traditional fixed sensor is limited by the layout position, resulting in suboptimal detection performance. For micro-leakage, it is even more difficult to achieve detection. With the advantages of small size and strong passing ability, the acoustic inner detector is well-suited to the task of comprehensive pipeline detection. Therefore, this paper carried out unsteady micro-leakage detection based on acoustic internal inspection signals. The unsteady micro-leakage simulation experiment of pipeline was carried out, and the leakage acoustic signal was collected for method verification. This paper investigates the integration of variational mode decomposition (VMD), random forest (RF) and least squares support vector machine (LSSVM) for signal processing and leakage classification. An unsteady micro-leakage detection method based on acoustic internal inspection signals was proposed, which is well-suited to the leakage detection task of pipelines. Experimental results indicated that the proposed method achieved a recognition accuracy of 95.31%, outperforming conventional leakage detection methods.

IPC Classification

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