Show simple item record

dc.contributor.authorKeprate, Arvind
dc.contributor.authorChatterjee, Supratik
dc.date.accessioned2022-04-07T07:44:49Z
dc.date.available2022-04-07T07:44:49Z
dc.date.created2022-01-24T17:09:45Z
dc.date.issued2021-06-29
dc.identifier.isbn978-1-7281-5934-8
dc.identifier.isbn978-1-7281-5935-5
dc.identifier.urihttps://hdl.handle.net/11250/2990377
dc.description.abstractTopside piping is the most commonly failed equipment in the Petroleum and Maritime industry. The prominent degradation mechanism causing piping failure is fatigue which results in unnecessary hydrocarbon release from these assets. In order to avoid the unexpected fatigue failure of piping, it is essential to estimate the remaining fatigue life (RFL) of the aforementioned assets. Generally, engineering companies either rely on experimentally derived SN curves or on a probabilistic fracture mechanics approach to predict RFL. More recently, researchers have utilized surrogate models (classical ML models) to predict the same, and the results seem to be promising. In this manuscript, authors have tried to employ Deep Learning in order to predict the RFL of a topside piping in which crack has been detected. Firstly, different sources of uncertainty in the crack growth process are identified and quantified, with suitable distributions and parameters obtained from the literature. Thereafter, Monte Carlo Simulation is used to generate 5000 samples of the data consisting of 3 input parameters and 1 target feature (RFL class). Afterwards, the data is preprocessed, and feature importance criteria is applied. Finally, a custom Deep Learning model is developed to estimate the RFL class. An accuracy of 0.96 is achieved.en_US
dc.language.isoengen_US
dc.publisherInstitute of Electrical and Electronics Engineersen_US
dc.relation.ispartofProceedings of the International Conference on Applied Artificial Intelligence (ICAPAI) 2021
dc.relation.ispartofseriesInternational Conference on Applied Artificial Intelligence (ICAPAI);2021 International Conference on Applied Artificial Intelligence (ICAPAI)
dc.subjectDeep learningen_US
dc.subjectFeature engineeringen_US
dc.subjectData preprocessingen_US
dc.subjectRemaining fatigue livesen_US
dc.subjectTopside pipingen_US
dc.titlePredicting Remaining Fatigue Life of Topside Piping Using Deep Learningen_US
dc.typeConference objecten_US
dc.description.versionacceptedVersionen_US
cristin.ispublishedtrue
cristin.fulltextpostprint
cristin.fulltextoriginal
cristin.qualitycode1
dc.identifier.doihttps://doi.org/10.1109/ICAPAI49758.2021.9462055
dc.identifier.cristin1988899
dc.source.volume1en_US
dc.source.issue1en_US
dc.source.pagenumber6en_US


Files in this item

Thumbnail

This item appears in the following Collection(s)

Show simple item record