Sensor-data-driven prognosis approach of liquefied natural gas satellite plant

This paper proposes a sensor-data-driven prognosis approach for the predictive maintenance of a liquefied natural gas (LNG) satellite plant. By using data analytics of sensors installed in the satellite plants, it is possible to predict the remaining time to refill the tank of the remote plants. In the proposed approach, the first task of data validation and correction is presented in order to transform raw data into reliable validated data. Then, the second task presents two methods for the prognosis of gas consumption in real time and the forecast of remaining time to refill the tank of the plant. The obtained results with real satellite plants showed good performance for direct implementation in a predictive maintenance plan.This work was funded by the Spanish Ministry of Economy and Competitiveness (MINECO) and FEDER through projects SCAV (ref. DPI2017-88403-R), and by AGAUR ACCIO RIS3CAT UTILITIES 4.0–P1 ACTIV 4.0. ref. COMRDI-16-1-0054-03 and SMART Project (ref no EFA153/16 Interreg Cooperation Program POCTEFA 2014- 2020).Peer ReviewedPostprint (published version

Data-driven modeling and learning in science and engineering

FJM acknowledges support from Agencia Estatal de Investigación of Spain, grant PGC-2018-097257-B-C32. JNK acknowl-edges support from the Air Force Office of Scientific Research (AFOSR) grant FA9550-17-1-0329.In the past, data in which science and engineering is based, was scarce and frequently obtained by experiments proposed to verify a given hypothesis. Each experiment was able to yield only very limited data. Today, data is abundant and abundantly collected in each single experiment at a very small cost. Data-driven modeling and scientific discovery is a change of paradigm on how many problems, both in science and engineering, are addressed. Some scientific fields have been using artificial intelligence for some time due to the inherent difficulty in obtaining laws and equations to describe some phenomena. However, today data-driven approaches are also flooding fields like mechanics and materials science, where the traditional approach seemed to be highly satisfactory. In this paper we review the application of data-driven modeling and model learning procedures to different fields in science and engineering