Lifetime Prediction Using a Tribology-Aware, Deep Learning-Based Digital Twin of Ball Bearing-Like Tribosystems in Oil and Gas

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dc.citation.articleNumber922en_US
dc.citation.issueNumber6en_US
dc.citation.journalTitleProcessesen_US
dc.citation.volumeNumber9en_US
dc.contributor.authorDesai, Prathamesh S.en_US
dc.contributor.authorGranja, Victoriaen_US
dc.contributor.authorHiggs, C. Freden_US
dc.date.accessioned2021-06-25T13:35:02Zen_US
dc.date.available2021-06-25T13:35:02Zen_US
dc.date.issued2021en_US
dc.description.abstractThe recent decline in crude oil prices due to global competition and COVID-19-related demand issues has highlighted the need for the efficient operation of an oil and gas plant. One such avenue is accurate predictions about the remaining useful life (RUL) of components used in oil and gas plants. A tribosystem is comprised of the surfaces in relative motion and the lubricant between them. Lubricant oils play a significant role in keeping any tribosystem such as bearings and gears working smoothly over the lifetime of the oil and gas plant. The lubricant oil needs replenishment from time to time to avoid component breakdown due to the increased presence of wear debris and friction between the sliding surfaces of bearings and gears. Traditionally, this oil change is carried out at pre-determined times. This paper explored the possibilities of employing machine learning to predict early failure behavior in sensor-instrumented tribosystems. Specifically, deep learning and tribological data obtained from sensors deployed on the components can provide more accurate predictions about the RUL of the tribosystem. This automated maintenance can improve the overall efficiency of the component. The present study aimed to develop a deep learning-based digital twin for accurately predicting the RUL of a tribosystem comprised of a ball bearing-like test apparatus, a four-ball tester, and lubricant oil. A commercial lubricant used in the offshore oil and gas components was tested for its extreme pressure performance, and its welding load was measured using a four-ball tester. Three accelerated deterioration tests was carried out on the four-ball tester at a load below the welding load. Based on the wear scar measurements obtained from the experimental tests, the RUL data were used to train a multivariate convolutional neural network (CNN). The training accuracy of the model was above 99%, and the testing accuracy was above 95%. This work involved the model-free learning prediction of the remaining useful lifetime of ball bearing-type contacts as a function of key sensor input data (i.e., load, friction, temperature). This model can be deployed for in-field tribological machine elements to trigger automated maintenance without explicitly measuring the wear phenomenon.en_US
dc.identifier.citationDesai, Prathamesh S., Granja, Victoria and Higgs, C. Fred. "Lifetime Prediction Using a Tribology-Aware, Deep Learning-Based Digital Twin of Ball Bearing-Like Tribosystems in Oil and Gas." <i>Processes,</i> 9, no. 6 (2021) MDPI: https://doi.org/10.3390/pr9060922.en_US
dc.identifier.digitalprocesses-09-00922-v2en_US
dc.identifier.doihttps://doi.org/10.3390/pr9060922en_US
dc.identifier.urihttps://hdl.handle.net/1911/110840en_US
dc.language.isoengen_US
dc.publisherMDPIen_US
dc.rightsThis is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly citeden_US
dc.rights.urihttps://creativecommons.org/licenses/by/4.0/en_US
dc.titleLifetime Prediction Using a Tribology-Aware, Deep Learning-Based Digital Twin of Ball Bearing-Like Tribosystems in Oil and Gasen_US
dc.typeJournal articleen_US
dc.type.dcmiTexten_US
dc.type.publicationpublisher versionen_US
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