Gas pipeline leakage identification method based on convolution neural network

Abstract

The invention provides a gas pipeline leakage identification method based on a convolution neural network. The method comprise the following steps that after a leakage sound signal and a background sound signal of a typical leakage type are collected, framing processing and short-time Fourier transform are carried out to obtain a time-frequency diagram representing the original leakage sound signal; then a convolution neural network classification model aiming at the leakage sound signal is built, a traditional square convolution kernel is changed into a specific strip-shaped rectangular convolution kernel, so that the line spectrum characteristics in the time-frequency diagram are better extracted; and the time-frequency diagram of the leakage sound and the time-frequency diagram of the background sound are mixed and sent to the built convolution neural network for training, K-fold cross validation is adopted for training, and a network model superparameter is optimized, so that the optimal model superparameter is selected and the robustness and universality of the model are enhanced. Compared with the pipeline leakage identification method in the prior art, the method has the advantages that the identification rate is further improved, and the problem of feature screening which is most difficult to process in the prior art can be effectively solved.

Description

technical field [0001] The invention belongs to the field of gas pipeline leakage identification, and in particular relates to a gas pipeline leakage identification method based on a convolutional neural network. Background technique [0002] With the development of the economy and the continuous improvement of people's living standards, natural gas has been popularized in the daily life of urban and rural residents, and a large number of gas pipelines are distributed in the underground space of modern cities. With the passage of time and the development of the city, the pipelines distributed in the underground space will gradually age, corrode or be artificially damaged, which will inevitably lead to gas leakage. Gas leakage not only causes great pollution to the environment, but also poses a great hidden danger to the personal safety of urban and rural residents. Therefore, it is particularly important to detect the leakage source in time to ensure the personal safety of ...

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Problems solved by technology

[0004] In order to solve the time-consuming and labor-intensive problems of the traditional leakage identification method and the high rate of false alarms and false alarms, the present invention proposes a leakage identification method based on convolutional neural network, especially on the convolution kernel of convolutional

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