Acoustic-wave-method gas pipeline leakage monitoring method based on ANN (Artificial Neural Network)

Abstract

The invention discloses an acoustic-wave-method gas pipeline leakage monitoring method based on ANN (Artificial Neural Network). The acoustic-wave-method gas pipeline leakage monitoring method comprises the following steps that sound wave signal samples on multiple working conditions of a gas pipeline are acquired, the sound wave signal samples on the different working conditions are denoised and characteristic values of the sound wave signal samples on the different working conditions are extracted; the Gaussian membership function is adopted to conduct fuzzy segmentation on the sound wave signal samples on the different working conditions to acquire fuzzy segmentation amount; the F-self-adaption genetic algorithm is used for optimizing the initial training value of the BP (Back Propagation) neural network, the fuzzy segmentation amount is substituted into the BP neural network for training, and a gas pipeline instant working condition judging BP neural network is acquired; gas pipeline working conditions are judged with the output value of the BP neural network according to the principle of proximity and the time-phased statistical method, and the leakage position is confirmed through the cross-correlation function when leakage occurs. The acoustic-wave-method gas pipeline leakage monitoring method has the advantages that the leakage recognition accuracy rate is increased and the leakage judging reliability degree is increased due to the fact that the principle of proximity and the time-phased statistical method are adopted to determine the working condition states of pipelines.

Description

technical field [0001] The invention relates to the field of gas transmission pipeline leakage monitoring, in particular to an ANN-based sonic method gas transmission pipeline leakage monitoring method. Background technique [0002] Gas pipeline leakage monitoring has developed relatively maturely, but it is still lacking in signal feature extraction, multi-working condition leakage judgment algorithm and modal recognition algorithm optimization. Therefore, the false alarm rate of the existing gas pipeline leakage method is high, and the accuracy rate is not ideal. In order to efficiently and accurately identify pipeline leakage conditions and eliminate other similar interference conditions, in-depth research is still needed in these three aspects. In the present invention, ANN is the abbreviation of artificial neural network. Contents of the invention [0003] The purpose of the present invention is to make up for the above-mentioned technical deficiencies, and to provi...

AI Technical Summary

Problems solved by technology

[0002] Gas pipeline leakage monitoring has developed relatively maturely, but it is still lacking in signal feature extraction, multi-working condition leakage judgment algorithm, and modal recognition algorithm optimization.

Therefore, the false alarm rate of the existing gas pipeline leakage method is high, and the accuracy rate is not ideal

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