Giuseppe Giunta

TL;DR: A predictive maintenance strategy where the condition of a centrifugal pump is tracked by solely exploiting standard pressure measurements, recorded also on remote points along the pipeline, and using an unsupervised learning approach is presented and validated on historical pressure signals collected by Eni for several years on a crude oil transportation pipeline.

TL;DR: In this article, a method and system for continuous remote monitoring of integrity of pressurized pipelines and properties of fluids transported is presented. But the authors focus on the measurement of elastic signals propagating in the walls of the pipeline and acoustic signals in the transported fluid.

TL;DR: In this paper, the authors used clustering algorithms to compute the typical Italian load profile in different days of the week in different seasons and found that better results are obtained if the clustering is not performed directly on the data, but on some features extracted from the data.

TL;DR: In this article, the formation of critical faults is determined and localized, which arise in the walls of the pipeline or the growth of an already localized fault is monitored, through a method which comprises detecting, by means of acoustic sensors regularly distributed along the pipeline, the ultrasounds diffused along the walls, or when a controlled fault increases, and processing the digital signals associated with the sound waves in order to identify the position or evaluate the development of a critical fault.