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Pipeline leakage detection method based on hierarchical neural network

A neural network and detection method technology, which is applied in the field of pipeline leak detection based on hierarchical neural network, can solve the problems of difficult data, low pipeline accuracy, and data errors, so as to improve the accuracy and overcome the unsatisfactory prediction effect. Effect

Inactive Publication Date: 2017-01-25
CHINA PETROLEUM & CHEM CORP
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Problems solved by technology

[0005] Whether it is a hardware-based detection method or a software-based detection method, it is difficult to obtain various data of all pipe sections of the entire pipeline, and the pipeline data may be affected by various factors during the collection process. Therefore, the collected data There may be errors, which lead to low accuracy of pipeline leak detection. To solve this problem, the present invention provides a new leak detection technology based on layered neural network

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Embodiment Construction

[0015] Embodiments of the present invention will be described below in conjunction with the accompanying drawings.

[0016] 1. a pipeline leak detection method based on layered neural network is characterized in that: comprise the following steps:

[0017] A. The pressure sensor is used to collect the pipeline pressure data, and the pressure value of each node of the entire pipeline is obtained, and the data set is normalized at the same time, so that its range is limited between [0-1];

[0018] B. Divide the data set into training data set and verification data set according to the proportion, determine the layer number of layered neural network, determine the complexity of each layer of neural network according to the scale of training data set, and train the first layer on the training data set. k layer neural network, k Represents a layered neural network;

[0019] C. On the validation dataset, set the first k The rejection threshold of the positive and negative sa...

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Abstract

The invention provides a pipeline leakage detection method based on a hierarchical neural network. The neural network is layered, a rejection threshold and a rejection interval of each layer of neural network are set, a test sample is input into each layer of neural network in the leakage detection process, if one layer of neural network judges the category of the test sample, the classification process is finished, and if the layer of neural network rejects the test sample, the test sample is input to the next layer of neural network till the test sample obtains a category label. The method can effectively judge whether an existing pipeline leaks or not, accuracy of pipeline leakage detection is improved, and detection precision is good.

Description

technical field [0001] The invention relates to a pipeline leakage detection method, in particular to a pipeline leakage detection method based on a layered neural network. Background technique [0002] With the rapid development of pipeline construction in various countries in the world, pipeline accidents also occur frequently. In order to effectively prevent pipeline accidents, pipeline leakage monitoring technology has also developed rapidly. For this reason, a large number of researches have been carried out at home and abroad. [0003] Existing pipeline leak detection and location methods are generally divided into two categories: hardware-based methods and software-based methods, but the existing hardware methods usually require the installation of many sensors, the installation process is also very complicated and expensive, and the scope of application is narrow ; The software method is to calculate the leakage position through the model, which usually has a large ...

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): F17D5/02
CPCF17D5/02
Inventor 朱铁军李体明赵红燕蒋明达李忠波
Owner CHINA PETROLEUM & CHEM CORP
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