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Predictive Cognitive Maintenance Decision Support System

Predictive Cognitive Maintenance Decision Support System

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.
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Coordinator

LINNEUNIVERSITETET

Address

Linnaeus University
35195 Vaxjo

Sweden

Activity type

Higher or Secondary Education Establishments

EU Contribution

€ 749 350

Participants (16)

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E-MAINTENANCE SWEDEN AB

Sweden

EU Contribution

€ 435 443,75

PARAGON ANONYMH ETAIREIA MELETON EREVNAS KAI EMPORIOU PROIGMENHS TEXNOLOGIAS

Greece

EU Contribution

€ 320 512,50

SAVVY DATA SYSTEMS SL

Spain

EU Contribution

€ 206 937,50

VERTECH GROUP

France

EU Contribution

€ 289 625

BOSCH REXROTH AG

Germany

EU Contribution

€ 202 370

SORALUCE S. COOP.

Spain

EU Contribution

€ 218 312,50

SAKANA, SOCIEDAD COOPERATIVA

Spain

EU Contribution

€ 199 325

OVERBECK GMBH

Germany

EU Contribution

€ 218 312,50

SPINEA SRO

Slovakia

EU Contribution

€ 129 500

GOMA CAMPS SOCIEDAD ANONIMA

Spain

EU Contribution

€ 171 893,75

LANTIER SL

Spain

EU Contribution

€ 213 937,50

IDEKO S COOP

Spain

EU Contribution

€ 654 500

COMMISSARIAT A L ENERGIE ATOMIQUE ET AUX ENERGIES ALTERNATIVES

France

EU Contribution

€ 470 920

CONSORCIO INSTITUTO TECNOLOXICO MATEMATICA INDUSTRIAL ITMATI

Spain

EU Contribution

€ 302 250

TECHNISCHE UNIVERSITAET MUENCHEN

Germany

EU Contribution

€ 803 250

TECHNISCHE UNIVERSITAET CHEMNITZ

Germany

EU Contribution

€ 559 962,50

Project information

Grant agreement ID: 768575

Status

Ongoing project

  • Start date

    1 November 2017

  • End date

    31 October 2020

Funded under:

H2020-EU.2.1.5.1.

  • Overall budget:

    € 7 263 332,50

  • EU contribution

    € 6 146 402,50

Coordinated by:

LINNEUNIVERSITETET

Sweden