Opendata, web and dolomites

Foresight

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

Total Cost €

0

EC-Contrib. €

0

Partnership

0

Views

0

 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.

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

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 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.

Are you the coordinator (or a participant) of this project? Plaese send me more information about the "FORESIGHT" project.

For instance: the website url (it has not provided by EU-opendata yet), the logo, a more detailed description of the project (in plain text as a rtf file or a word file), some pictures (as picture files, not embedded into any word file), twitter account, linkedin page, etc.

Send me an  email (fabio@fabiodisconzi.com) and I put them in your project's page as son as possible.

Thanks. And then put a link of this page into your project's website.

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

More projects from the same programme (H2020-EU.3.;H2020-EU.2.3.;H2020-EU.2.1.)

IMPACT (2019)

Improving DNA Analysis for Correct Healthcare

Read More  

COPI (2020)

Carbon Offset Plug-in

Read More  

TankSensor (2020)

Smart monitoring solution for better procurement and supply chain management - Speeding up chemical industry digitalization

Read More