BibTeX

@Article{HeidarySHM2020,
author = {Heidary, Roohollah and Groth, Katrina M.},
title = {A hybrid model of internal pitting corrosion degradation under changing operational conditions for pipeline integrity management},
journal = {Structural Health Monitoring},
year = {2020},
volume = {X},
number = {X},
note = {Accepted 2 September 2019.},
doi = {10.1177/1475921719877656},
abstract = {This article proposes a new framework to estimate the degradation level in oil and gas pipelines corroded by internal pitting when operational conditions change over time. Despite the fact that the operational conditions of a pipeline change at various times, this change has not been addressed in the current available pipeline corrosion degradation models. In this framework, a hierarchical Bayesian method and augmented particle filtering are used for data fusion to address this issue. This framework is applied on a case study and the results are compared with the estimations of a state of the art pitting corrosion degradation model},
file = {:Journal Papers/HeidarySHM2020_PittingCorrosionDefectPHM/HeidarySHM2020_PublishedVersion.pdf:PDF},
keywords = {Data fusion; augmented particle filtering; pipeline integrity management; prognostics and health management; pitting corrosion},
owner = {kgroth},
publisher = {{SAGE} Publications},
timestamp = {2020-01-13},
}

Abstract

This article proposes a new framework to estimate the degradation level in oil and gas pipelines corroded by internal pitting when operational conditions change over time. Despite the fact that the operational conditions of a pipeline change at various times, this change has not been addressed in the current available pipeline corrosion degradation models. In this framework, a hierarchical Bayesian method and augmented particle filtering are used for data fusion to address this issue. This framework is applied on a case study and the results are compared with the estimations of a state of the art pitting corrosion degradation model.