  This paper presents an innovative machine learning methodology that leverages
on long-term vibroacoustic measurements to perform automated predictions of the
needed pigging operations in crude oil trunklines. Historical pressure signals
have been collected by Eni (e-vpms monitoring system) for two years on discrete
points at a relative distance of 30-35 km along an oil pipeline (100 km length,
16 inch diameter pipes) located in Northern Italy. In order to speed up the
activity and to check the operation logs, a tool has been implemented to
automatically highlight the historical pig operations performed on the line.
Such a tool is capable of detecting, in the observed pressure measurements, the
acoustic noise generated by the travelling pig. All the data sets have been
reanalyzed and exploited by using field data validations to guide a decision
tree regressor (DTR). Several statistical indicators, computed from pressure
head loss between line segments, are fed to the DTR, which automatically
outputs probability values indicating the possible need for pigging the
pipeline. The procedure is applied to the vibroacoustic signals of each pair of
consecutive monitoring stations, such that the proposed predictive maintenance
strategy is capable of tracking the conditions of individual pipeline sections,
thus determining which portion of the conduit is subject to the highest
occlusion levels in order to optimize the clean-up operations. Prediction
accuracy is assessed by evaluating the typical metrics used in statistical
analysis of regression problems, such as the Root Mean Squared Error (RMSE).
