Emergency Shutdown Valve damage classification by machine learning using synthetic data

Abstract

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 polytetrafluoroethylene (RPTFE) seats that can put their operation and effectiveness at risk. 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. The results indicate that the evaluation of the complete signature with a Gradient Boosting Classifier (GBC) algorithm would be the most effective strategy, which correctly detects the damage in 99% of the cases considered.