首页|New Machine Learning Study Findings Have Been Reported by In- vestigators at Federal University Rio Grande do Sul UFRGS (Emer- gency Shutdown Valve Damage Classification By Machine Learning Using Synthetic Data)
New Machine Learning Study Findings Have Been Reported by In- vestigators at Federal University Rio Grande do Sul UFRGS (Emer- gency Shutdown Valve Damage Classification By Machine Learning Using Synthetic Data)
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NETL
NSTL
Elsevier
2024 FEB 02 (NewsRx) – By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News Daily News – Data detailed on Machine Learning have been presented. According to news reporting originating in Porto Alegre, Brazil, by NewsRx journalists, research stated, “Emergency Shutdown Valves (EDSVs) are used in industrial applications to interrupt internal fluid flow in pipelines during hazardous events. During their operation, these valves can suffer cumulative damage on their reinforced polytetraflu- oroethylene (RPTFE) seats that can put their operation and effectiveness at risk.” The news reporters obtained a quote from the research from Federal University Rio Grande do Sul UFRGS, “One of the options for detecting the occurrence of damage is to analyse data acquired from fluid pressure and torque sensors during the closing and opening cycles of the valve. The resulting operational signatures are commonly evaluated through so-called Transition Points (TPs) that are manually marked in a subjective manner by a trained operator. In addition to being slow and laborious, this approach discards most of the acquired information. Alternatives to this method would be the use of Damage Indexes (DIs) that are extracted from the signatures, or even the evaluation of the complete pressure or torque signature. These methods, when associated with machine learning (ML) algorithms, could use the acquired information more efficiently and reliably, and would have the potential to completely automate the monitoring process. In this work, these three processing options were tested and compared using real and synthetic data that were generated with the Monte Carlo (MC) method.”
Porto AlegreBrazilSouth AmericaCyborgsEmerging Tech- nologiesMachine LearningFederal University Rio Grande do Sul UFRGS
NETL
NSTL
Elsevier
