Predictive Cognitive Maintenance Decision Support System


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:

  • A data acquisition module leveraging external sensors as well as sensors directly embedded in the machine tool components,
  • 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,
  • 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
  • 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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Start date: 01-11-2017
End date: 31-10-2020
Total budget - Public funding: 7 263 332,00 Euro - 6 146 402,00 Euro
Call topic: Novel design and predictive maintenance technologies for increased operating life of production systems (FoF.2017.09)
Twitter: @PreCoM_Project
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Economic sustainability Supply chain and value network efficiency Circular economy Social sustainability Environmental sustainability Reducing the consumption of energy, while increasing the usa...
Reducing the consumption of water and other process resource... Waste minimisation Product quality - Quality assurance Occupational safety and health Skills, training, new job profiles Increasing human achievements in manufacturing systems

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Mechatronics Control technologies Advanced and ubiquitous human machine interaction Measurement, condition and performance monitoring technologi... Smart and functional materials IoT - Internet of Things ICT solutions for next generation data storage and informati... Digital manufacturing platforms ICT solutions for modelling and simulation tools Modelling and simulation methods of manufacturing processes ... Skills - Knowledge-workers
System modelling, simulation and forecasting Modelling and simulation for the (co-)design and management ... Data analytics Data acquisition Data storage Data processing Data modelling Data collection, storage, analytics, processing and AI Cognitive and artificial intelligence (AI) technologies - ma... Cloud computing, edge computing
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Manufacturing system levels and life-cycle stages Manufacturing system levels
Work centres - Production lines Enterprise - Factory

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Cybersecurity Hyperconnected Factories Pathway Autonomous Smart Factories Pathway General purpose software
Dedicated software in silos Connected IT and OT Off-line optimisation Realtime optimisation

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Business model aspects of digital platform deployment Business ecosystems associated to digital platforms Target clients
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