Conference: 12th IEEE International Conference on Industrial Informatics, INDIN 2014. Porto Alegre, Brazil – July 27-30 2014 Title of the paper: Knowledge-based web service integration for industrial automation Authors: Borja Ramis, Luis Gonzalez, Sergii Iarovyi, Andrei Lobov, José L. Martinez Lastra, Valeriy Vyatkin, William Dai

1. Knowledge-based web service
integration for industrial automation
Date: July, 2014
Linked to: eScop
Contact information
Tampere University of Technology,
FAST Laboratory,
P.O. Box 600,
FIN-33101 Tampere,
Finland
Email: fast@tut.fi
www.tut.fi/fast
Conference: 12th IEEE International
Conference on Industrial Informatics,
INDIN 2014. Porto Alegre, Brazil – July
27-30 2014
Title of the paper: Knowledge-based
web service integration for industrial
automation
Authors: Borja Ramis, Luis Gonzalez,
Sergii Iarovyi, Andrei Lobov,
José L. Martinez Lastra, Valeriy Vyatkin,
William Dai
If you would like to receive a reprint of
the original paper, please contact us

2. Knowledge-based web
service integration for
industrial automation
Authors: Borja Ramis, Luis Gonzalez, Sergii Iarovyi, Andrei Lobov,
José L. Martinez Lastra, Valeriy Vyatkin, William Dai
{borja.ramis, luis.gonzalezmoctezuma, sergii.iarovyi, andrei.lobov,
jose.lastra}@tut.fi, vyatkin@ieee.org, william.dai@ltu.se
Tampere University of Technology
Factory Automation Systems and Technology Lab
12th IEEE International Conference on Industrial Informatics, INDIN
2014. Porto Alegre, Brazil – July 27-30 2014

3. Outline
1. Introduction and motivation
2. Architecture
3. Production line system layout
4. OWL system model main classes and properties
5. System model instances
6. eScop demo link
7. System UI
8. Conclusions
9. Further work
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4. Introduction and motivation (1)
• Current research focuses on knowledge-based integration
and on exploiting full potentials of run-time reconfiguration
and adaptation of industrial automation systems
• SOA eases the interactions for knowledge-based system,
but the service description should be machine-readable
• The means for such description lies in semantics, which
can provide metadata about devices
• OWL provides required level of abstraction for Knowledge
Representation
• OWL and SPARQL permits keeping the KR of system
updated and controlling the workflow execution based on
the KB
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5. Introduction and motivation (2)
• How to create a manufacturing system, which
information is fully represented in ontology allowing
runtime orchestration of manufacturing system (from
visualization services to devices hosting control
services)?
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6. Architecture
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7. Production line system layout
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8. OWL system model main
classes and properties
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9. System model instances
Class Instances
Conveyor conveyor_1, conveyor_2, conveyor_3
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ConveyorZone
input_Cell_1, input_Cell_2, input_Cell_3
output_Cell_1, output_Cell_2, output_Cell_3
systemInput, systemOutput
workingPosition_Cell_1, workingPosition_Cell_2,workingPosition_Cell_3
ManufacturingCell manufacturingCell_1, manufacturingCell_2, manufacturingCell_3
Robot robot_1, robot_2, robot_3
ManufacturingCellStatus CelldownStatus, CellworkingStatus
RobotStatus downStatus,executingStatus, idleStatus
AssemblyOperation assemblyOperation_1, assemblyOperation_2, assemblyOperation_3,
assemblyOperation_4, assemblyOperation_5, assemblyOperation_6
Component component_A, component_B, component_C, component_D, component_E,
component_F

10. eScop demo link
• ARTEMIS Co-Summit demonstration
– Complete process execution and production
line control runnable by introduction of
SPARQL and SPARQL Update queries
– Tutorial and guidelines for using the system
– Access to OWL domain model and description
– Dynamic User Interface for process
monitoring
– Link: http://www.escop-project.eu/teaser/
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11. System UI: Initial state
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12. System UI: Query execution
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13. Conclusions
• The knowledge-based system and ontology is accessible
online to test queries and get additional details on
running implementation
• This approach permits a knowledge-based integration of
industrial automation systems
• The presented knowledge-based service integration
exploits full potentials of run-time reconfiguration of
industrial systems
• The presented model describes a generic production
system ontology, adaptable to different use cases in the
manufacturing domain
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14. Further work
• We plan to elaborate basic architecture blocks
performance and handling of exceptional cases at the
production floor
• Runtime knowledge aggregation principles have to be
elaborated, as the use of distributed Knowledge Bases
and reasoning capabilities at embedded device level
• Besides query algorithm utilization, we plan to add a set
of rules, as SWRL rules, to support the knowledge and
we expect to infer the model with the use of reasoner
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15. Acknowledge
• The research leading to these results has received
funding from the ARTEMIS Joint Undertaking under
grant agreement n° 332946 and from the Finnish
Funding Agency for Technology and Innovation
(TEKES), correspondent to the project shortly entitled
eScop, Embedded systems for service-based control of
open manufacturing and process automation.
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16. THANK YOU!
Any questions?
http://www.youtube.com/user/fastlaboratory
https://www.facebook.com/fast.laboratory
http://www.slideshare.net/fastlaboratory
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