Objectives:

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

• example: UPTIME Strategic Intelligence Platform

Objectives and main activities:

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

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

Objectives and main activities:

1. Interoperability of future factory approaches for PdM

• Retrofitting of physical models design

• RUL estimation

• FMECA

• 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

Objectives and main activities:

• ForeSee business model: Predictive Maintenance offered as a service

• Regulatory aspects

• Data ownership and management

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

Objectives:

• 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