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Opendata, web and dolomites

Foresight

Foresight: Autonomous machine monitoring and prognostics system for the Oil and Gas and Maritime sectors

Total Cost €

EC-Contrib. €

Partnership

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Outcomes and
results

Foresight project word cloud

Explore the words cloud of the Foresight project. It provides you a very rough idea of what is the project "Foresight" about.

environmental module ml data collect drilling requiring human avoiding fleet competitors grant bearing sound 98 holistically outperforms onto injuries vibration tbm size unexpected happen provides units unnecessary catastrophes connections labour flows monitor cloud expert time unmaintained onshore vessels sensors calculations forecast operation nominal possibility adapt mobile obsolete packets index reliability minimising services tailor removing gigabytes relieves generate inefficient reducing actions designed platforms technologies maintenance crews nowadays hardware dated lowering onboard installation machine synthesizing appropriate machinery model downtimes offshore autonomously send parts speed gathered handled 2 excessive reduce communication software modus waste lifecycle monitoring learning health mainly replacement

Project "Foresight" data sheet

The following table provides information about the project.

Coordinator	MACHINE PROGNOSTICS AS Organization address address: JON LILLETUNS VEI 9 city: GRIMSTAD postcode: 4879 website: n.a. contact info title: n.a. name: n.a. surname: n.a. function: n.a. email: n.a. telephone: n.a. fax: n.a.
Coordinator Country	Norway [NO]
Project website	https://www.machineprognostics.no
Total cost	71˙429 €
EC max contribution	50˙000 € (70%)
Programme	1. H2020-EU.3. (PRIORITY 'Societal challenges) 2. H2020-EU.2.3. (INDUSTRIAL LEADERSHIP - Innovation In SMEs) 3. H2020-EU.2.1. (INDUSTRIAL LEADERSHIP - Leadership in enabling and industrial technologies)
Code Call	H2020-SMEInst-2018-2020-1
Funding Scheme	SME-1
Starting year	2019
Duration (year-month-day)	from 2019-04-01 to 2019-09-30

Partnership

Take a look of project's partnership.

#	participants	country	role	EC contrib. [€]
1	MACHINE PROGNOSTICS AS MACHINE PROGNOSTICS AS Organization address address: JON LILLETUNS VEI 9 city: GRIMSTAD postcode: 4879 website: n.a. contact info title: n.a. name: n.a. surname: n.a. function: n.a. email: n.a. telephone: n.a. fax: n.a.	NO (GRIMSTAD)	coordinator	50˙000.00

Map

Project objective

Maintenance processes applied on vessels and offshore platforms are obsolete. The technologies commonly applied to monitor machinery generate Gigabytes of technical data, requiring an expert to process it. Data cannot be handled by real-time monitoring services onshore, as the data connections available offshore are not designed for such flows. As a result, only 2% of the Mobile Drilling Units (MODUs) fleet in operation nowadays implement a real-time machinery monitoring, while the other 98% apply the out-dated Time-Based Maintenance (TBM) model. TBM increases lifecycle costs due to unexpected downtimes, higher labour costs and waste of parts in working condition. MODUs and platforms are bearing today unnecessary and excessive costs due to inefficient maintenance, even human injuries or environmental catastrophes are more likely to happen due to unmaintained machinery. Our technology provides to vessels’ and platforms’ crews the possibility to monitor machinery health in real-time, allowing them to forecast and undertake the most appropriate actions. Foresight’s hardware is composed mainly by vibration monitoring equipment, that grant an easy installation onto any type of machinery. Foresight’s sensors continuously monitor the machinery, collect data, process them to reduce the size of the data packets and send them to the software on the cloud. Foresight Machine Learning (ML) module holistically processes the data gathered by sensors, synthesizing them into a comprehensive Health Index. It outperforms competitors in speed and reliability and is able to autonomously adapt and tailor its calculations on each machinery nominal behaviour. Foresight relieves vessels and platforms maintenance costs by: (1) lowering the number of sensors needed; (2) reducing data communication needs; (3) removing the need for a technical expert onboard (4) minimising unexpected downtimes; (5) avoiding replacement of sound parts.

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The information about "FORESIGHT" are provided by the European Opendata Portal: CORDIS opendata.

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