Intelligent analysis system and method for magnetic flux leakage detection data in pipeline

A technology of intelligent analysis and data, applied in pipeline systems, electrical digital data processing, special data processing applications, etc. Analytical system view and other issues

Active Publication Date: 2019-05-21
NORTHEASTERN UNIV
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Problems solved by technology

However, the existing research on magnetic flux leakage data analysis pays too much attention to the development of local points and lacks a systemat

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  • Intelligent analysis system and method for magnetic flux leakage detection data in pipeline
  • Intelligent analysis system and method for magnetic flux leakage detection data in pipeline
  • Intelligent analysis system and method for magnetic flux leakage detection data in pipeline

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Embodiment Construction

[0162] The present invention will be further described below in conjunction with the accompanying drawings and embodiments.

[0163] The invention provides a software system and method for intelligent analysis of magnetic flux leakage data detected in pipelines. It proposes an internal detection magnetic flux leakage data analysis system from the perspective of non-destructive testing and evaluation as a whole, and invents a method based on time-like sparse sampling and KNN- A softmax data complete set construction method, a pipeline connection component discovery method based on the combination of selective search and convolutional neural network, an anomaly candidate region search and Identification method, a defect quantification method based on random forest and an improved pipeline solution based on ASME B31G standard. Realize the safe operation and maintenance of the pipeline.

[0164] Such as figure 2 As shown, it is a system diagram of the magnetic flux leakage data...

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Abstract

The invention provides an intelligent analysis system and method for magnetic flux leakage detection data in a pipeline. The process comprises the following steps: adopting time domain-based sparse sampling and KNN-softmax data complete set construction module, obtaining a complete magnetic flux leakage data set; a pipeline connection assembly discovery method based on combination of selective search and a convolutional neural network is adopted in the discovery module to obtain the accurate position of the weld joint; adopting an abnormal candidate region searching and identifying method based on a Lagrangian multiplication frame and multi-source magnetic flux leakage data fusion in the discovery model to find out defective magnetic flux leakage signals; Adopting a defect quantification method based on a random forest in a quantification module to obtain a defect size; a pipeline solution improved based on the ASME B31G standard is adopted in the solution module, and an evaluation result is output. An analysis method is provided from the overall perspective, and pretreatment, connection assembly detection and abnormity detection, defect size inversion and final maintenance decision are achieved.

Description

technical field [0001] The invention belongs to the technical field of pipeline detection, and in particular relates to an intelligent analysis system and method for detecting magnetic flux leakage data in a pipeline. Background technique [0002] Pipeline transportation is widely used as a continuous, economical, efficient and green means of transportation. The design life of pipelines stipulated in the national standard is 20 years. With the increase of operation time, the damage caused by pipeline material problems, construction, corrosion and external force will make the pipeline condition deteriorate year by year, and the potential danger will increase sharply. Once a leak occurs, it will not only cause air pollution, but also easily cause a violent explosion. Therefore, in order to ensure the safety of energy transportation and ecological environment, the pipeline must be regularly inspected and maintained. [0003] Non-destructive testing (NDT) is widely used as an ...

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Application Information

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IPC IPC(8): G06F17/50G06N3/04G01N27/83F17D5/02
CPCG06N3/04G01N27/83F17D5/02
Inventor 刘金海付明芮卢森骧张化光马大中汪刚冯健张鑫博于歌魏红秋
Owner NORTHEASTERN UNIV
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