1. Project
  2. PreCoM

Predictive Cognitive Maintenance Decision Support System

Summary

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.

More information
Website: www.precom-project.eu
Duration: 36 months
Start date: 01-11-2017
End date: 31-10-2020
Number of participants: 17
Total budget - EC contribution: 7263332 Euro - 6146402 Euro
Call topic: Novel design and predictive maintenance technologies for increased operating life of production systems (FoF.2017.09)
Instrument: Innovation Action (IA)
Location
Characteristics

Challenges - (12)  

  1. Challenges
    1. Economic sustainability
      1. Addressing economic performance across the supply chain
      2. High performance production, combining flexibility, productivity, precision and zero-defect while remaining energy-efficient
      3. Resource efficiency in manufacturing, including addressing the end-of-life of products.
    2. Social sustainability
      1. Safe and attractive workplaces
    3. Environmental sustainability
      1. Reducing the consumption of energy, while increasing the usage of renewable energy
      2. Reducing the consumption of water and other process resources.
      3. Optimising the exploitation of materials in manufacturing processes
        1. Reduction of scraps (defective workpieces) and reworks

Technologies and enablers - (20)  

  1. Technologies and enablers
    1. Mechatronics for advanced manufacturing systems
      1. Control technologies
      2. Advanced machine interaction with humans
      3. Condition and performance monitoring
      4. Advanced materials in manufacturing systems
        1. Smart and functional materials
    2. Information and communication technologies
      1. IoT - ICT solutions for factory floor and physical world inclusion
      2. ICT solutions for next generation data storage and information mining
      3. ICT solutions for implementing secure, high performance and open services platforms
      4. ICT solutions for modelling and simulation tools
        1. Modelling and simulation of production process constraints influencing the characteristics and performances of the materials.
        2. Modelling and simulation methods of manufacturing processes involving mechanical, energetic, fluidic and chemical phenomena
    3. Knowledge-workers
    4. Modelling, simulation and forecasting
      1. Modelling and simulation for the (co-)design and management of integrated product-process-production systems
    5. Standards
      1. ICT standards
      2. Mechanical standards

Digitalisation pathways - (8)  

  1. Digitalisation pathways
    1. Hyperconnected Factories
    2. Autonomous Smart Factories
      1. General purpose software
      2. Dedicated software in silos
      3. Connected IT and OT
      4. Off-line optimisation
      5. Realtime optimisation

Research priorities - (4)  

  1. Research priorities
    1. Domain 3: Digital, virtual and resource-efficient factories
      1. RP 3.2 Intelligent maintenance systems for increased reliability of production systems
      2. RP 3.4 Energy monitoring and management in future manufacturing enterprises

Diverse - (4)  

  1. Diverse
    1. Clustering
    2. International
    3. Standards
Results
  • No published items
Demonstrators
  • No published items
Organisations
Contacts
Francesco Barbabella

Francesco Barbabella
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