Opendata, web and dolomites

PreCoM SIGNED

Predictive Cognitive Maintenance Decision Support System

Total Cost €

0

EC-Contrib. €

0

Partnership

0

Views

0

 PreCoM project word cloud

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

learning    localize    tool    10    direct    industrial    ultimately    service    leveraging    spend    performance    supplier    data    augmented    factories    assets    artificial    efficiency    reactive    analytics    false    components    opportunity    cheaper    unprecedented    maintenance    evolution    issue    connecting    statistical    deploy    additional    least    health    combining    sensors    broad    dashboards    safety    spectrum    intelligence    conduct    probability    platform    actions    planning    machine    business    algorithms    accurate    notices    remaining    cloud    15    preventive    models    total    smes    industry    machines    external    alarms    continuous    big    secure    cognitive    vs    validate    severity    preventative    modules    reduce    scenarios    healing    document    predict    academic    individual    innovative    interfaces    suppliers    predictive    powerful    maintainability    dynamic    detection    manufacturers    human    track    decision    interface    integration    manufacturing    module    physical    private    life    damage    supporting    acquisition    asset    volume    self   

Project "PreCoM" data sheet

The following table provides information about the project.

Coordinator
LINNEUNIVERSITETET 

Organization address
address: LINNAEUS UNIVERSITY
city: VAXJO
postcode: 35195
website: www.lnu.se

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 Sweden [SE]
 Project website http://www.precom-project.eu
 Total cost 7˙221˙611 €
 EC max contribution 6˙146˙402 € (85%)
 Programme 1. H2020-EU.2.1.5.1. (Technologies for Factories of the Future)
 Code Call H2020-FOF-2017
 Funding Scheme IA
 Starting year 2017
 Duration (year-month-day) from 2017-11-01   to  2020-10-31

 Partnership

Take a look of project's partnership.

# participants  country  role  EC contrib. [€] 
1    LINNEUNIVERSITETET SE (VAXJO) coordinator 792˙475.00
2    TECHNISCHE UNIVERSITAET MUENCHEN DE (MUENCHEN) participant 820˙125.00
3    IDEKO S COOP ES (ELGOIBAR) participant 687˙000.00
4    TECHNISCHE UNIVERSITAET CHEMNITZ DE (CHEMNITZ) participant 564˙812.00
5    E-MAINTENANCE SWEDEN AB SE (VAXJO) participant 490˙568.00
6    COMMISSARIAT A L ENERGIE ATOMIQUE ET AUX ENERGIES ALTERNATIVES FR (PARIS 15) participant 470˙920.00
7    PARAGON ANONYMH ETAIREIA MELETON EREVNAS KAI EMPORIOU PROIGMENHS TEXNOLOGIAS EL (GALATSI N ATTIKIS) participant 323˙312.00
8    CONSORCIO INSTITUTO TECNOLOXICO MATEMATICA INDUSTRIAL ITMATI ES (SANTIAGO DE COMPOSTELA) participant 302˙250.00
9    SAVVY DATA SYSTEMS SL ES (DONOSTIA) participant 225˙437.00
10    OVERBECK GMBH DE (HERBON) participant 218˙312.00
11    SORALUCE S. COOP. ES (BERGARA) participant 218˙312.00
12    LANTIER SL ES (TOLOSA) participant 213˙937.00
13    BOSCH REXROTH AG DE (LOHR) participant 202˙370.00
14    SAKANA, SOCIEDAD COOPERATIVA ES (LAKUNTZA) participant 199˙325.00
15    GOMA CAMPS SOCIEDAD ANONIMA ES (LA RIBA TARRAGONA) participant 171˙893.00
16    SPINEA SRO SK (PRESOV) participant 129˙500.00
17    VERTECH GROUP FR (NICE) participant 115˙850.00

Map

 Project objective

Cheaper and more powerful sensors, together with big data analytics, offer an unprecedented opportunity to track machine-tool performance and health condition. However, manufacturers only spend 15% of their total maintenance costs on predictive (vs reactive or preventative) maintenance. The project will deploy and test a predictive cognitive maintenance decision-support system able to identify and localize damage, assess damage severity, predict damage evolution, assess remaining asset life, reduce the probability of false alarms, provide more accurate failure detection, issue notices to conduct preventive maintenance actions and ultimately increase in-service efficiency of machines by at least 10%. The platform includes 4 modules: 1) a data acquisition module leveraging external sensors as well as sensors directly embedded in the machine tool components, 2) an artificial intelligence module combining physical models, statistical models and machine-learning algorithms able to track individual health condition and supporting a large range of assets and dynamic operating conditions, 3) a secure integration module connecting the platform to production planning and maintenance systems via a private cloud and providing additional safety, self-healing and self-learning capabilities and 4) a human interface module including production dashboards and augmented reality interfaces for facilitating maintenance tasks. The consortium includes 3 end-user factories, 3 machine-tool suppliers, 1 leading component supplier, 4 innovative SMEs, 3 research organizations and 3 academic institutions. Together, we will validate the platform in a broad spectrum of real-life industrial scenarios (low volume, high volume and continuous manufacturing). We will also demonstrate the direct impact of the platform on maintainability, availability, work safety and costs in order to document the results in detailed business cases for widespread industry dissemination and exploitation.

 Deliverables

List of deliverables.
Knowledge based and cloud deployment report Documents, reports 2019-10-30 09:16:51
Safety Monitoring Report Documents, reports 2019-10-30 09:16:51
Model implementation report (I) Documents, reports 2019-10-30 09:16:52
Embedded sensor requirements specifications Documents, reports 2019-10-30 09:16:51
Project Quality Handbook Documents, reports 2019-10-30 09:16:51
Target parameter selection report Documents, reports 2019-10-30 09:16:52
Project website (including internal project repository) Websites, patent fillings, videos etc. 2019-10-30 09:16:52
AR/PLIV requirement specifications Documents, reports 2019-10-30 09:16:51
Model implementation report (II) Documents, reports 2019-10-30 09:16:51
Scheduling model report (I) Documents, reports 2019-10-30 09:16:51
LCC: system boundaries and functional unit test definition Documents, reports 2019-10-30 09:16:51
Open data management plan Open Research Data Pilot 2019-10-30 09:16:52
LCA: system boundaries and functional unit test definition Documents, reports 2019-10-30 09:16:52

Take a look to the deliverables list in detail:  detailed list of PreCoM deliverables.

 Publications

year authors and title journal last update
List of publications.
2019 Hatem Algabroun
Dynamic sampling rate algorithm (DSRA) implemented in self-adaptive software architecture: a way to reduce the energy consumption of wireless sensors through event-based sampling
published pages: , ISSN: 0946-7076, DOI: 10.1007/s00542-019-04631-9
Microsystem Technologies 2020-01-29
2018 Al-Najjar, Basim; Algabroun, Hatem; Jonsson, Mikael
Smart Maintenance Model using Cyber Physical System
published pages: 1-6, ISSN: , DOI:
\"Conference on \"\"Role of Industrial Engineering in Industry 4.0 Paradigm\"\" (ICIEIND)\" 27-30/09/2018 2019-10-30
2018 Simon Zhai, Gunther Reinhart
Predictive Maintenance als Wegbereiter für die instandhaltungsgerechte Produktionssteuerung
published pages: 298-301, ISSN: 0947-0085, DOI: 10.3139/104.111912
ZWF Zeitschrift für wirtschaftlichen Fabrikbetrieb 113/5 2019-10-30

Are you the coordinator (or a participant) of this project? Plaese send me more information about the "PRECOM" 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 "PRECOM" are provided by the European Opendata Portal: CORDIS opendata.

More projects from the same programme (H2020-EU.2.1.5.1.)

MF 2019 (2019)

MANUFUTURE 2019 - Sustainable Smart Manufacturing

Read More  

OPEN_NEXT (2019)

Company-Community Collaboration for Open Source Development of products and services

Read More  

HR-Recycler (2018)

Hybrid Human-Robot RECYcling plant for electriCal and eLEctRonic equipment

Read More