Branch pipeline leakage detection method based on information fusion

To solve the problem of a high false positive rate in leakage detection in branch pipelines, this paper proposes a pipeline detection method based on acousto-pressure signal information fusion. The transformer architecture is introduced as the basic mode of the acousto-pressure fusion diagnosis model to enhance the signal timing feature extraction ability. The cross-self-attention mechanism is used to make the sound signal and pressure signal interact and fuse in the process of feature extraction, so as to realise end-to-end branch pipeline fault diagnosis. Firstly, the noise reduction method of complementary ensemble empirical mode decomposition-least mean squares (CEEMD-LMS) is used to denoise the collected sound pressure signal. Secondly, an acousto-pressure cross-fusion model based on a cross-fusion transformer is proposed to fuse the information in two types of denoised signal and classify different leakage types. The experimental results show that the accuracy of leakage detection for a 2 mm leak size is 97.21%, while the accuracies of leakage detection for 5 mm and 8 mm sizes are 98.57% and 98.94%, respectively. The accuracy of the model for detecting the presence or absence of any leakage is 99.03%, which verifies the effectiveness of the proposed method.