Activity Area #1:
Analysis of technology in the market
and in the pipeline



1. Identification of a reference model for compiling existing technology that:

  • provides the means for diverse data collection, consolidation and interoperability
  • supports digital automation functions that will close the loop to the field and will enable “autonomous” maintenance functionalities
  • allows the use of novel concepts (e.g. AR) in maintenance scenarios with particular emphasis on remote maintenance
  • complies with Industry 4.0 standardization

2. Analysis of existing approaches, including:

  • competitor analysis
  • technology maturity analysis

Main activities:

1. ForeSee Cluster reference architectural model

  • Mapping ForeSee Cluster projects’ architecture and components to the reference architecture, e.g. RAMI 4.0
  • 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


Objectives and main activities:

1. Identification of the scientific and technological
common ground that links the Foresee Cluster projects:

  • Reliability-based maintenance approaches
  • Trustworthy predictive maintenance
  • Trend reference models for prediction of equipment condition
  • Integrated quality-maintenance methods and tools
  • Synchronise maintenance with production planning and logistics
  • Failure modes, effects, and criticality analysis (FMECA) techniques
  • Physically-based models
  • Optimization of availability, maintainability, quality, safety while considering the system as a whole and throughout the production lifecycle.
  • Transferability to different industrial sectors (versatility)
  • Integration of very different actors (common platform vs networks of Smart Objects Technologies)
  • Standard communication protocols and data formats
  • Vertical and horizontal scalability
  • Security at all levels
  • Data safety
  • Big data management
  • Low level data collection, filtering and cleaning
  • Exploitability from low CPU power end points (mobiles)

2. Creation of a common reference model that allows
depicting all ForeSee Cluster projects workflows

  • Select and agree on a general model to cover all projects architectures (e.g. RAMI 4.0)
  • Adapt the general model to specific project needs to allow further indentification if integration of cluster solutions is possible

Activity Area #3:
for the future factory

Lead: Z-BRE4K

Objectives and main activities:

1. Interoperability of future factory approaches for PdM

  • Retrofitting of physical models design
  • RUL estimation
  • Digital twins
  • Data spaces
  • Cognitive manufacturing
  • Industrie 4.0 and RAMI compliance

2. Applications of key indicators for PdM

  • Maintenance Terminology – EN 13306:2017

3. Identifications of available maintenance standards

  • Describe the maintenance landscape
  • Identify the knowledge required at different levels of:
  • Technical director / Physical asset manager
  • Maintenance manager
  • Maintenance engineer and supervisor
  • Maintenance technicians

Activity Area #4:
New model for sustainable factories
through efficient predictive maintenance


Objectives and main activities:

  • ForeSee business model: Predictive Maintenance offered as a service
  • Regulatory aspects
  • Data ownership and management


Activity Area #5:
Skills building paradigm
for predictive maintenance


Objectives and main activities:

1. Education and training methods on the new PdM methods and platforms

  • Training on maintenance strategies and reliability metrics
  • Virtual predictive analytics and AI techniques
  • Live data stream and application of data analytics
  • Simulated maintenance operation planning and operations
  • Provision of training materials and test releases

2. Roadmap and future trend for the industry

  • Condition monitoring and assessment at the edge
  • Data transfer to the cloud
  • Predictive analytics and fault diagnosis/prognosis
  • Maintenance operations management
  • Standardisation

Activity Area #6:
Community building
and dissemination



  • To build a sustainable European industrial and academic community for predictive maintenance
  • To create opportunities for joint exploitation
  • To foster transfer of expertise and best practices

Main activities:

1. Dissemination

  • Improve academic results by collaboration on common topics
  • Improve dissemination impact and awareness based on joint initiatives
  • Exchange best practices

2. Community Building & Management

  • Establish infrastructure for cluster community management
  • Widen outreach by combining existing project communities
  • Foster a broad ecosystem of potential customers

3. Synergies with other Initiatives

  • Exchange best practices on cross-cutting topics
  • Increase awareness of predictive maintenance in other communities
  • 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.