Activity Area #1:
Analysis of technology in the market
and in the pipeline
Lead: PROPHESY
Objectives:
1. Identification of a reference model for compiling existing technology that:
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provides the means for diverse data collection, consolidation and interoperability
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supports digital automation functions that will close the loop to the field and will enable “autonomous” maintenance functionalities
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allows the use of novel concepts (e.g. AR) in maintenance scenarios with particular emphasis on remote maintenance
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complies with Industry 4.0 standardization
2. Analysis of existing approaches, including:
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competitor analysis
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technology maturity analysis
Main activities:
1. ForeSee Cluster reference architectural model
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Mapping ForeSee Cluster projects’ architecture and components to the reference architecture, e.g. RAMI 4.0
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Alignment to Predictive Maintenance related Standards, e.g. EN 13306, EN 17007
2. Database providing Predictive Maintenance state-of-the-art approaches,
technologies and platforms
Activity Area #2:
ForeSee Predictive Maintenance
concept for the Factory of the Future
Lead: PROGRAMS
Objectives and main activities:
1. Identification of the scientific and technological
common ground that links the Foresee Cluster projects:
Scientific:
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Reliability-based maintenance approaches
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Trustworthy predictive maintenance
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Trend reference models for prediction of equipment condition
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Integrated quality-maintenance methods and tools
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Synchronise maintenance with production planning and logistics
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Failure modes, effects, and criticality analysis (FMECA) techniques
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Physically-based models
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Optimization of availability, maintainability, quality, safety while considering the system as a whole and throughout the production lifecycle.
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Transferability to different industrial sectors (versatility)
Technological:
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Integration of very different actors (common platform vs networks of Smart Objects Technologies)
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Standard communication protocols and data formats
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Vertical and horizontal scalability
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Security at all levels
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Data safety
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Big data management
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Low level data collection, filtering and cleaning
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Exploitability from low CPU power end points (mobiles)
2. Creation of a common reference model that allows
depicting all ForeSee Cluster projects workflows
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Select and agree on a general model to cover all projects architectures (e.g. RAMI 4.0)
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Adapt the general model to specific project needs to allow further indentification if integration of cluster solutions is possible
Activity Area #3:
Trend-setting
for the future factory
Lead: Z-BRE4K
Objectives and main activities:
1. Interoperability of future factory approaches for PdM
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Retrofitting of physical models design
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RUL estimation
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FMECA
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Digital twins
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Data spaces
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Cognitive manufacturing
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Industrie 4.0 and RAMI compliance
2. Applications of key indicators for PdM
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Maintenance Terminology – EN 13306:2017
3. Identifications of available maintenance standards
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Describe the maintenance landscape
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Identify the knowledge required at different levels of:
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Technical director / Physical asset manager
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Maintenance manager
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Maintenance engineer and supervisor
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Maintenance technicians
Activity Area #4:
New model for sustainable factories
through efficient predictive maintenance
Lead: PRECOM
Objectives and main activities:
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ForeSee business model: Predictive Maintenance offered as a service
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Regulatory aspects
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Data ownership and management
Activity Area #5:
Skills building paradigm
for predictive maintenance
Lead: SERENA
Objectives and main activities:
1. Education and training methods on the new PdM methods and platforms
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Training on maintenance strategies and reliability metrics
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Virtual predictive analytics and AI techniques
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Live data stream and application of data analytics
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Simulated maintenance operation planning and operations
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Provision of training materials and test releases
2. Roadmap and future trend for the industry
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Condition monitoring and assessment at the edge
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Data transfer to the cloud
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Predictive analytics and fault diagnosis/prognosis
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Maintenance operations management
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Standardisation
Activity Area #6:
Community building
and dissemination
Lead: UPTIME
Objectives:
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To build a sustainable European industrial and academic community for predictive maintenance
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To create opportunities for joint exploitation
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To foster transfer of expertise and best practices
Main activities:
1. Dissemination
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Improve academic results by collaboration on common topics
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Improve dissemination impact and awareness based on joint initiatives
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Exchange best practices
2. Community Building & Management
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Establish infrastructure for cluster community management
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Widen outreach by combining existing project communities
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Foster a broad ecosystem of potential customers
3. Synergies with other Initiatives
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Exchange best practices on cross-cutting topics
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Increase awareness of predictive maintenance in other communities
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Capture new channels for exploitation of cluster results
ForeSee is a cluster of six projects, which have received funding from the European Union’s Horizon 2020 research and innovation programme under the FoF9 call.