In the explosion experiment, the explosion vibration signal is the important information to evaluate the explosion equivalent or to locate the explosion point. However, the environment at the scene of the explosion is very complicated. The explosion vibration signal collected by the Distributed fiber Acoustic Sensor (DAS) system not only contains optical noise, but also contains a lot of non-stationary and non-Gaussian environmental background noise. In order to solve this issue, feed-forward denoising convolutional neural network (DnCNN) are used to denoise the signals during the noise suppression of explosion vibration signals. The initial application of this network was to purge images of additive Gaussian white noise. In order to make DnCNN adapt to the de-noising of explosion vibration data, we have performed numerous optimization tasks. Firstly, the input data is processed by an reversible downsampling factor in this paper, expanding the network’s perceptual field while also making training easier. Secondly, rather than using Gaussian white noise, the training set of DnCNN is rebuilt using background noise collected in the actual environment. Finally, the DnCNN parameters that impact network performance are modified and improved. From the experimental results in this paper, the DnCNN can effectively suppress the noise in the explosion vibration signal and preserve the effective signal.
Underwater pipeline transportation, as an important means of natural gas transportation, will cause great economic loss and environmental pollution in case of leakage damage. Aiming at improving the accuracy of pipeline leakage monitoring research, a method was proposed to monitor the leakage process of underwater natural gas pipelines using distributed optical fiber acoustic sensing technology. In this paper, a processing algorithm combining empirical modal decomposition method and wavelet decomposition reconstruction is used to extract the signal frequency domain features. The experimental results show that the frequency domain amplitude of the vibration signal gradually grows as the leakage orifice diameter and internal pipe pressure increase, and the standard deviation of the vibration signal of the pipeline exhibits a quadratic fitting relationship with the size of the leak aperture. The method proposed in this paper has a minimum leak aperture identification error of 7.2%, which can effectively improve the pipeline leak monitoring accuracy.
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