The Leakage Identification and Location of Ship Pipeline System Based on Vibration Signal Processing

Pan Su, JieChang Wu, Guanghui Chang, Shuyong Liu, Xuejiao Feng and Sheng Du

Complexity, 2023, vol. 2023, 1-16

Abstract: The leakage of the ship’s pipeline system will bring great risks to the engine equipment and seriously threaten the vitality of the ship. In this paper, the pipeline leakage detection and localization research are carried out based on the vibration signal generated by pipeline leakage. First, the finite element model of the pipeline is constructed to obtain the variation law of the vibration signal when the pipeline leaks are carried out. Second, the vibration signal is processed based on the variational mode decomposition (VMD) and radial basis function (RBF) neural networks. The wavelet packet threshold noise reduction is conducted before signal decomposition to improve the signal-to-noise ratio. Then, the denoised signal is decomposed by VMD. The effective component is identified by analyzing the correlation coefficient between the component and the denoised signal. The center frequency and energy of the effective component are used as feature vector to train the RBF neural network to identify and locate leakage. Finally, a pipeline leakage test platform is built under laboratory conditions. After processing the data samples collected from the test, the RBF neural network is trained to identify and locate leaks. The test sample identification results show that the leak identification and localization method based on VMD-RBF has a high accuracy.

Date: 2023
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Persistent link: https://EconPapers.repec.org/RePEc:hin:complx:9646710

DOI: 10.1155/2023/9646710

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