AMPP Store - Products tagged with 'bayesian networks'

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51316-7078-Internal Corrosion Direct Assessment Using Bayesian Networks Modeling With Limited Data-A Case Study

Product Number: 51316-7078-SG

ISBN: 7078 2016 CP

Author: Shan Guan

Publication Date: 2016

$20.00

Bayesian networks (BN) are useful tools for corrosion modeling. This paper is a case study demonstrating how to perform Internal Corrosion Direct Assessment (ICDA) using BN modeling with limited data. A BN model was developed for ICDA of a 50 km refined oil pipeline. Internal corrosion probability of failure along the pipeline was assessed.

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51318-11267-Big Data Analysis using Bayesian Network Modeling: A Case Study with WG-ICDA of a Gas Storage Field

Product Number: 51318-11267-SG

Author: Kyle Hilgefort

Publication Date: 2018

$20.00

Bayesian network modeling was explored as a solution to the challenge of effective use of large datasets, due to its ability to analyze complex cause-effect relationships while considering the variability and uncertainty in the data.

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Application of Probabilistic Model in SCCDA

Product Number: 51323-18956-SG

Author: Francois Ayello, Ramgopal Thodla, Guanlan Liu, Narasi Sridhar

Publication Date: 2023

$20.00

Stress Corrosion Cracking (SCC) is a serious threat to our pipeline infrastructure. Past SCC failures have shown that both NN pH SCC and high pH SCC may lead to catastrophic pipeline failure. This is due to the formation of cracks that are difficult to detect. Moreover, SCC is difficult to predict, as multiple mechanisms must interact to lead to the formation of these cracks.

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Combination Of High Ph SCC And Near Neutral Ph SCC Models Using Bayesian Networks

Product Number: 51322-17851-SG

Author: Francois Ayello, Guanlan Liu, Narasi Sridhar, Ramgopal Thodla

Publication Date: 2022

$20.00

Environmentally Assisted Cracking (EAC) of gas transmission lines constitute about 2.6% of the total number of significant incidents recorded in the U.S. Pipeline and Hazardous Materials Administration (PHMSA) database [1]. For the hydrocarbon liquid pipelines, the EAC-related incidents constitute about 1%. Although Stress Corrosion Cracking (SCC) incidents are a relatively small percentage of significant incidents, it is important to predict the location and rate of growth of SCC because of the potential for catastrophic consequences from the growth of undetected cracks.

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Probabilistic Digital Twins For Transmission Pipelines

Product Number: 51321-16780-SG

Author: Francois Ayello; Yonghe Yang; Long Li; Guanlan Liu; Yuchong Zhang; Shuhui Zhang

Publication Date: 2021

$20.00

Products tagged with 'bayesian networks'

51316-7078-Internal Corrosion Direct Assessment Using Bayesian Networks Modeling With Limited Data-A Case Study

51318-11267-Big Data Analysis using Bayesian Network Modeling: A Case Study with WG-ICDA of a Gas Storage Field

Application of Probabilistic Model in SCCDA

Combination Of High Ph SCC And Near Neutral Ph SCC Models Using Bayesian Networks

Probabilistic Digital Twins For Transmission Pipelines

Association for Materials Protection and Performance