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PreCoM SIGNED

Predictive Cognitive Maintenance Decision Support System

Total Cost €

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EC-Contrib. €

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Partnership

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 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.

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

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

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

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