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 [Naijar2004]. 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 [Mobley2002].
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 deliverables of eSONIA include:
- Tools for 3D visualization of operations at the factory floor,
- An In-plant (Indoor & Outdoor) Geo-location System for Real-time Asset Management
- A Service Management System for Enhanced Manufacturing Control.
- Reference models and tools for Services Oriented Architecture implementation in a factory environment
- A set of processes running on embedded devices and offered to the outside world as Web Services to support
(asset) health assessment, prognostics, maintenance scheduling (i.e. the best mix of cyclic, condition-based and
predictive maintenance);
- Tools for seamless and flexible networking
The expected outcomes of eSONIA are: greater predictability of plant behaviour and visibility, reduced safety risks,
enhanced security and cost efficiency.
For example, the following figures ask for improvements in work and maintenance safety:
􀂃 70% of industrial accidents are caused by human errors during complex operations [Venkatasubramanian2005],
􀂃 5% of all mortal work accidents in Spain in 2007 took place during the carrying out of maintenance-related
activities.
On the other hand several studies have indicated that a significant increase in Overall Equipment Effectiveness (OEE),
(from today’s 60% to 75% ([Ahlmann1997]), entailing important profit improvements, is achievable through
maintenance improvements by using new technology, modern high-tech equipment and better planning.

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 [Naijar2004]. 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 [Mobley2002].
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 deliverables of eSONIA include:
- Tools for 3D visualization of operations at the factory floor,
- An In-plant (Indoor & Outdoor) Geo-location System for Real-time Asset Management
- A Service Management System for Enhanced Manufacturing Control.
- Reference models and tools for Services Oriented Architecture implementation in a factory environment
- A set of processes running on embedded devices and offered to the outside world as Web Services to support
(asset) health assessment, prognostics, maintenance scheduling (i.e. the best mix of cyclic, condition-based and
predictive maintenance);
- Tools for seamless and flexible networking
The expected outcomes of eSONIA are: greater predictability of plant behaviour and visibility, reduced safety risks,
enhanced security and cost efficiency.
For example, the following figures ask for improvements in work and maintenance safety:
􀂃 70% of industrial accidents are caused by human errors during complex operations [Venkatasubramanian2005],
􀂃 5% of all mortal work accidents in Spain in 2007 took place during the carrying out of maintenance-related
activities.
On the other hand several studies have indicated that a significant increase in Overall Equipment Effectiveness (OEE),
(from today’s 60% to 75% ([Ahlmann1997]), entailing important profit improvements, is achievable through
maintenance improvements by using new technology, modern high-tech equipment and better planning.

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

7H10012

English

Smrž Pavel, doc. RNDr., Ph.D. - principal person responsible

Department of Computer Graphics and Multimedia
- responsible department (6.4.2010 - not assigned)
Department of Computer Graphics and Multimedia
- beneficiary (1.3.2010 - 28.2.2013)

Responsibility: Smrž Pavel, doc. RNDr., Ph.D.