Conference: 13th IEEE International Conference on Industrial Informatics, INDIN 2015. Cambridge, UK – July 22-24 2015 Title of the paper: Knowledge-driven Finite-State Machines. Study case in monitoring industrial equipment Authors: Luis E. Gonzalez Moctezuma, Borja Ramis, Xiangbin Xu, Andrei Lobov, Jose L. Martinez Lastra

1. Knowledge-driven Finite-State Machines.
Study case in monitoring industrial
equipment
• Date: July, 2015
• 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: 13th IEEE International
Conference on Industrial Informatics,
INDIN 2015. Cambridge, UK – July 22-24
2015
Title of the paper: Knowledge-driven
Finite-State Machines. Study case in
monitoring industrial equipment
Authors: Luis E. Gonzalez Moctezuma,
Borja Ramis, Xiangbin Xu, Andrei Lobov,
Jose L. Martinez Lastra
If you would like to receive a reprint of
the original paper, please contact us

2. Knowledge-driven Finite-State
Machines. Study case in monitoring
industrial equipment
Authors: Luis E. Gonzalez Moctezuma, Borja Ramis,
Xiangbin Xu, Andrei Lobov, Jose L. Martinez Lastra
{luis.gonzalezmoctezuma, borja.ramisferrer, xiangbin.xu,
andrei.lobov, jose.lastra}@tut.fi
Tampere University of Technology
Factory Automation Systems and Technology Lab
13th IEEE International Conference on Industrial Informatics,
INDIN 2015. Cambridge, UK – July 22-24 2015

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Knowledge-driven Finite-State Machines. Study case in
monitoring industrial equipment
3
• Motivation
• Background
– Finite-State Machines
– Ontologies and languages
• Proposed approach
– Industrial resources monitoring systems
– Knowledge-driven FSM
• Study case and results
• Conclusions
Outline

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Knowledge-driven Finite-State Machines. Study case in
monitoring industrial equipment
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Motivation (1/2)
• During programming, states machines
usually are hardcoded
• State machines are stored and processed
by using specific language programming
frameworks
• Big momentum for ontological and
semantic web-based technologies

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Knowledge-driven Finite-State Machines. Study case in
monitoring industrial equipment
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Motivation (2/2)
• How to use web semantic technologies for
modelling and computing finite-state
machines?
• How to apply this for monitoring the state
of industrial equipment using knowledge-
driven FSM?

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Knowledge-driven Finite-State Machines. Study case in
monitoring industrial equipment
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• Abstract model to define the behaviour of a
system
• A FSM can be defined by a set of four
elements:
– Set of possible states
– Set of possible inputs
– Set of possible transitions between states
– Set of actions executed on each transition
Finite-State Machines (1/2)

7. Finite-State Machines (2/2)
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monitoring industrial equipment
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8. Ontologies and languages (1/2)
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monitoring industrial equipment
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• An ontology is “an explicit specification of a
conceptualisation” [Gruber93]
• An ontology is an engineering artefact:
– Constituted by a specific vocabulary used for any
domain description and a set of explicit
assumptions regarding the meaning of the
vocabulary
• Then, an ontology describes a formal specification
of a domain

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monitoring industrial equipment
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Ontologies and languages (2/2)
• Ontology markup languages…
*
*Jose L. Martinez Lastra, Ivan M. Delamer, Fernando Ubis;
“Domain Ontologies for Reasoning Machines in Factory Automation”;
ISBN: 9781936007011, 2010; 138 pages

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Knowledge-driven Finite-State Machines. Study case in
monitoring industrial equipment
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Industrial resources monitoring
systems
Knowledge-driven FSM base
Monitoring
service
Third-­‐party
applica5ons
SPARQL/REST
JSON/REST
JSON/REST

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Knowledge-driven Finite-State Machines. Study case in
monitoring industrial equipment
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State_x TransitsTo State_y ! A t1 B
Knowledge-driven FSM

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monitoring industrial equipment
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• Generic SPARQL query to compute the
Next State of a FSM:
Computations over the
Knowledge-driven FSM

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monitoring industrial equipment
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• Generic SPARQL query to retrieve the
State Transition Table of a FSM:
Computations over the
Knowledge-driven FSM

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• Testbed
Knowledge-driven Finite-State Machines. Study case in
monitoring industrial equipment
Study case

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monitoring industrial equipment
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Results (1/2)
Computing Next State
(Oligvo editor interface)

16. Results (2/2)
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Knowledge-driven Finite-State Machines. Study case in
monitoring industrial equipment
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Computing StateTransition Table
(Oligvo editor interface)

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monitoring industrial equipment
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• Ontological models were used to host the
description of FSMs
• Generic SPARQL queries to compute data:
– Next state
– State Transition Table
• Approach implemented and tested with
industrial equipment
Conclusions

18. • 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 (http://www.escop-project.eu/)
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Knowledge-driven Finite-State Machines. Study case in
monitoring industrial equipment
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Acknowledge

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monitoring industrial equipment
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THANK YOU!
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