knowledge-based web service integration for industrial automation

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: [email protected] 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

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, [email protected], [email protected]

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

Outline

16/09/14 Knowledge-based web service integration for industrial automation 3

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

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

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)?

Architecture


Production line system layout


OWL system model main classes and properties


System model instances


Class Instances

Conveyor conveyor_1, conveyor_2, conveyor_3

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

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/

System UI: Initial state


System UI: Query execution


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

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

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.



THANK YOU! Any questions?

http://www.youtube.com/user/fastlaboratory

https://www.facebook.com/fast.laboratory

http://www.slideshare.net/fastlaboratory