Pipeline leakage detection method based on neural network

Abstract

The invention provides a pipeline leakage detection method based on a neural network. The pipeline leakage detection method based on a neural network comprises the steps of collecting pressure data of the whole pipe through a pressure collection device under leakage conditions and non-leakage conditions, and marking the collected data into a leakage type and a non-leakage type to serve as training samples; performing normalization processing on the training samples, and respectively computing a maximum value, a minimum value, an average value and a variance; establishing a BP neural network model to perform training; collecting the pressure data in a real-time mode, and inputting the processed data into the trained neural network to obtain pipeline leakage results. The pipeline leakage detection method based on the neural network is suitable for all working conditions of the pipeline and has good detection accuracy.

Description

technical field [0001] The invention belongs to the artificial intelligence application field, in particular to the field of pipeline leakage detection. Background technique [0002] Preventing pipeline production accidents is a very important task in pipeline safety management. With the rapid development of pipeline construction in various countries in the world, pipeline accidents also occur frequently. Once a pipeline leakage accident occurs, it will not only cause a large amount of property loss, but also cause environmental pollution and waste of resources. cause personal injury. [0003] In order to effectively prevent pipeline accidents, pipeline leakage detection technology has been developed rapidly. For this reason, a lot of research has been done at home and abroad. In recent years, with the development of computer technology, pipeline leakage detection technology is developing towards the combination of software and hardware. At present, various new pipeline le...

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Problems solved by technology

[0008] Since the pipeline pressure may be affected by various factors, the pressure curve may have nonlinear problems. The BP neural network has unique and excellent performances such as parallel distributed processing, self-organization, self-adaptation, self-learning and good fault tolerance. Therefore, The present invention adopts BP neural network to train pipeline pressure data, so that the problem of nonlinearity of pipeline pressure curve can be better solved

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