Prediction method of pipeline corrosion defect size based on multi-source data fusion

Abstract

The invention discloses a multi-source data fusion-based system and method for predicting pipeline corrosion defect size. The system comprises a sensor group, a lower computer and an upper computer, wherein the sensor group is arranged at the head end or the tail end of a pipeline and is in contact with a transmission medium; the sensor group is connected with the lower computer connected with the upper computer. The method comprises the steps of firstly building a gray GM (1,1) prediction model and a BP neural network model by using historical data; then using the two models to obtain a prediction value; next building an inducted orderly weighted harmonic average (IOWHA) operator combination model according to the historical data and the prediction value of the two models; for new data, using the three models in sequence to predict the corrosion defect size in three directions of axial direction, circumference and depth respectively, so that a corrosion defect size prediction value of the pipeline is finally acquired. By adopting the system and the method, the prediction accuracy is higher, and the input of data is reduced.

Description

technical field [0001] The invention belongs to the technical field of pipeline risk prediction, and in particular relates to a pipeline corrosion defect size prediction method based on multi-source data fusion. Background technique [0002] The role of pipeline transportation in economic development is becoming more and more important, such as urban tap water pipelines, land crude oil pipelines, and submarine oil and gas pipelines. One of the important safety hazards of pipeline transportation. In order to prevent leakage accidents, it is necessary to use pipeline detection equipment to detect the pipeline, understand the corrosion status of the pipeline, evaluate the remaining strength of the pipeline, and predict the remaining life of the pipeline, so as to maintain and repair the pipeline in a planned and targeted manner. . [0003] There are many factors for the occurrence of pipeline corrosion. The internal factors are the difference in the composition of the pipe wa...

AI Technical Summary

Problems solved by technology

At present, the methods for determining the change trend of corrosion defect size with time mainly include: online real-time detection through pipeline detectors to obtain the current corrosion defect size, using prediction algorithms such as gray GM (1, 1), based on time series, through Calculation and analysis to obtain the change of corrosion defect size with time, this method requires less data, but the prediction accuracy is not high

In addition, it is also possible to analyze the environment of the pipeline, use the knowledge of chemical disciplines to establish a specific pipeline corrosion rate model, and solve the corrosion defect size change with time. This method is more accurate than the gray GM (1, 1) method. High, but the amount of required data information is large, and it is difficult to obtain a sufficient amount of detection data, which also reduces the accuracy of this method to a certain extent

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