The Application of the Big Data Algorithm for Pipeline Lifetime Analysis

The Application of the Big Data Algorithm for Pipeline Lifetime Analysis

Bayes methods

big data

Big Data

ensemble learning

machine learning

Mathematical model

Oils

pipeline integrity

Pipelines

Predictive models

remaining life prediction

Support vector machines

Oil and gas pipeline integrity management has always been a field of huge data accumulation. In recent years, the rise of big data technology has provided new ideas for pipeline integrity evaluation technology. Firstly, this paper systematically studies the key technologies related to the lifetime analysis of oil and gas pipelines and big data. Meanwhile, we also studies the next generation oil and gas pipeline big data system architecture, the development direction of big data architecture tends to be batch-stream unified and integrated. We completes the model establishment process from theoretical derivation, algorithm design and gives the key algorithm steps to accurately predict the pipeline inspection period. From the results, in the single models the minimum risk decision based on Naive Bayes is the best, the accuracy rate is 91.86%, and in the ensemble model the accuracy of GBDT is slightly better than that of the random forest, which accuracy rate reached 99.7%. In contrast, the ensemble learning method has a much better dataset fitting performance, which provides an idea for the selection of the algorithm in engineering applications.

824-829

J. Gu

H. Zhang

L. Chen

S. Lian

2019 Chinese Automation Congress (CAC)

2019

conferencePaper

2688-0938

10.1109/CAC48633.2019.8996228