In Europe, manufacturing represents approximately 22% of GDP, and it is estimated that 75% of GDP and 70%
of employment is related to manufacturing. The direct cost of maintenance is equivalent to 4% to 8% of the total
sales turnover. Depending on the industry, maintenance costs can represent between 15% (food-related
industries) and 60% (iron and steel, pulp and paper and other heavy industries) of production cost.
However today’s factories plant states are isolated and cannot be fully understood since there is no infrastructure
for holistic and continuous measurement and visualization of relevant information. This lack of insight prevents
efficient decision taking in real-time (e.g. recovery from undesired situations).
The objective of the eSONIA project is to realize the asset-aware and self-recovery plant through:
- pervasive heterogeneous (wirelines and wireless) IPv6-based embedded devices
- bringing on-board specialized services
- glued through a middleware capitalizing the service oriented approach
All that will be used for the first time in industry to support continuous monitoring/diagnostics/prognostics/control
of assets, regardless of their physical location.
The delivered information will be elaborated and visualized in 3D-geolocation mode to infer:
- efficient automatic maintenance schedules
- improved operator dispatch and repair performance
- efficient runtime planning of product/supplies routes (for continuous track & trace systems), automatic
triggering of re-sequencing and line-balancing processes in response to unscheduled maintenance
actions or equipments’ failure.
The expected outcomes of eSONIA are: greater predictability of plant behaviour and visibility, reduced safety
risks, enhanced security and cost efficiency.

Description in Czech
In Europe, manufacturing represents approximately 22% of GDP, and it is estimated that 75% of GDP and 70%
of employment is related to manufacturing. The direct cost of maintenance is equivalent to 4% to 8% of the total
sales turnover. Depending on the industry, maintenance costs can represent between 15% (food-related
industries) and 60% (iron and steel, pulp and paper and other heavy industries) of production cost.
However today’s factories plant states are isolated and cannot be fully understood since there is no infrastructure
for holistic and continuous measurement and visualization of relevant information. This lack of insight prevents
efficient decision taking in real-time (e.g. recovery from undesired situations).
The objective of the eSONIA project is to realize the asset-aware and self-recovery plant through:
- pervasive heterogeneous (wirelines and wireless) IPv6-based embedded devices
- bringing on-board specialized services
- glued through a middleware capitalizing the service oriented approach
All that will be used for the first time in industry to support continuous monitoring/diagnostics/prognostics/control
of assets, regardless of their physical location.
The delivered information will be elaborated and visualized in 3D-geolocation mode to infer:
- efficient automatic maintenance schedules
- improved operator dispatch and repair performance
- efficient runtime planning of product/supplies routes (for continuous track & trace systems), automatic
triggering of re-sequencing and line-balancing processes in response to unscheduled maintenance
actions or equipments’ failure.
The expected outcomes of eSONIA are: greater predictability of plant behaviour and visibility, reduced safety
risks, enhanced security and cost efficiency.

Keywords
automatic maintenance schedules, continuous track & trace systems, IPv6,
Service Oriented Architecture, (Semantic) Web Services, 3D visualization,
asset monitoring, asset management