AMPP Store - Products tagged with 'bayesian network'

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51316-7070-Reliability Assessment Of Offshore Asset Under Pitting Corrosion

Product Number: 51316-7070-SG

ISBN: 7070 2016 CP

Author: Jyoti Bhandari

Publication Date: 2016

$20.00

Corrosion is a major cause of structural deterioration in the marine/offshore industry. FOr that reason, reliability assessment and maintenance planning of these structures are crucial. In the current work a combination multi-phase phenomenological and mechanistic model for pitting corrosion is tested using Bayesian network (BN) approach.

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Bayesian Modeling Coating Performance

Product Number: 51322-17612-SG

Author: James A. Ellor, Daniel Pope, Anthony Florimbio, C.Thomas Savell, Lisa A. Barker, John Repp

Publication Date: 2022

$20.00

Coating degradation on Army ground systems represents a significant maintenance cost and effort. The objective of this proposed work is to develop a predictive model for coating degradation and subsequent substrate corrosion on Army ground assets. Provided with a better understanding of the root causes, steps can be taken to reduce corrosion impacts on Army materiel.

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Decision Making Under Uncertain Erosion Conditions Using The Probabilistic Model

Product Number: 51322-17813-SG

Author: Guanlan Liu, Francois Ayello, Jose Vera, Richard Eckert

Publication Date: 2022

$20.00

Erosion is one of the major threats of the pipeline integrity1 when it’s transporting liquid hydrocarbon products with solid particles. The erosion process decreases the effective wall thickness and therefore reduces the capacity of the pipeline to contain the pressured product. This can induce serious consequences including property, health and safety, environment, and business costs.

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IR 4.0 Integrity Management Using Data Analytics

Product Number: MPWT19-15487

Author: Dr. Haaken Ahnfelt, Dr. Luis Caetano, Dr. Hilde Aas Nøst, Dr. Knut Nordanger, Reidar Kind, Dr. Zeeshan Lodhi, Dr. Lay Seong Teh

Publication Date: 2019

$0.00

One of the pillars of the fourth industrial revolution (4IR) is to let machines make decisions on behalf of humans; this paper describes new technology that allows machines to decide inspection programs and field validation and testing of results. The technology described is a part of integrity management, and uses data, statistics and expert decisions to design inspection programs. These inspection programs are an important part of the safeguarding of equipment to maintain production and safety.
This technology is a data-driven predictive model of material loss from corrosion, based on domain expert input and historical data in the form of non-destructive testing (NDT) tests. The technology trends is based on historical data and SME input, while accounting for uncertainties in NDT measurements, with uncertainties in historical trends and uncertainties in future trends. This produces a more realistic failure prediction to enhance existing RBIs and adds safety by improving on early detection of trends in data. In total, this enables the machine to update inspection plans autonomously, reducing the number of inspections significantly.
The paper also describes how the technology can be developed further to use production data and integrity operating windows to improve predictions, deal with localised corrosion and assess if the test points on a corrosion circuit are sufficient, can be reduced in number or should be manually evaluated by adding more test points.

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Probabilistic Assessment of Hydrogen Stress Cracking of Steels and CRA in Sour Environments

Product Number: 51324-20409-SG

Author: Narasi Sridhar; Mariano A. Kappes

Publication Date: 2024

$40.00

Hydrogen Stress Cracking (HSC) of steels and CRA has been studied for a long time and it is recognized that the results have significant variability depending on many factors, including test procedures, material microstructures, and environmental variables. Although the standards used for acceptance of a material for sour service are deterministic, the variabilities in the test data as well as field conditions merit probabilistic treatment. This paper provides a Bayesian network model of HSC that connects the variabilities in material, environment, and loading conditions through mechanistic factors related to hydrogen transport, deformation mode, and strain rate. The model tries to encapsulate the state of knowledge in this area and tests the outcomes in terms of probability of HSC. The sensitivity of the model to input variables is assessed.

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Quantitative Assessment Of Failure Probability Of Underground Natural Gas Storage Wells Using An Integrated Bow-Tie Bayesian Network Approach

Product Number: 51322-17849-SG

Author: Francois Ayello, Narasi Sridhar, Arun Agarwal, Vincent Demay

Publication Date: 2022

$20.00

Underground natural gas storage (UGS) is an important component of the overall natural gas transportation and distribution system. It enables the utilities to supply natural gas during high seasonal demand periods and store gas during periods of lower demand. There are approximately 627 underground gas storage sites worldwide with a working gas capacity of 319.³ Billion m3 ( about 11.8 Trillion Cubic feet). The U.S. has a total of 414 natural gas storage fields, out of which 25 are inactive.

Products tagged with 'bayesian network'

51316-7070-Reliability Assessment Of Offshore Asset Under Pitting Corrosion

Bayesian Modeling Coating Performance

Decision Making Under Uncertain Erosion Conditions Using The Probabilistic Model

IR 4.0 Integrity Management Using Data Analytics

Probabilistic Assessment of Hydrogen Stress Cracking of Steels and CRA in Sour Environments

Quantitative Assessment Of Failure Probability Of Underground Natural Gas Storage Wells Using An Integrated Bow-Tie Bayesian Network Approach

Association for Materials Protection and Performance