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Interoperability and machine-to-machine translation model with mappings to machine learning tasks
Luleå tekniska universitet, Institutionen för system- och rymdteknik, EISLAB.ORCID-id: 0000-0003-4881-8971
Luleå tekniska universitet, Institutionen för system- och rymdteknik, EISLAB.ORCID-id: 0000-0001-5662-825x
Luleå tekniska universitet, Institutionen för system- och rymdteknik, EISLAB.ORCID-id: 0000-0002-4133-3317
(engelsk)Manuskript (preprint) (Annet vitenskapelig)
Abstract [en]

Modern large-scale automation systems integrate thousands to hundreds of thousands of physical sensors and actuators. Demands for more flexible reconfiguration of production systems and optimization across different information models, standards and legacy systems challenge current system interoperability concepts. Automatic semantic translation across information models and standards is an increasingly important problem that needs to be addressed to fulfill these demands in a cost-efficient manner under constraints of human capacity and resources in relation to timing requirements and system complexity. Here we define a translator-based operational interoperability model for interacting cyber-physical systems in mathematical terms, which includes system identification and ontology-based translation as special cases. We present alternative mathematical definitions of the translator learning task and mappings to similar machine learning tasks and solutions based on recent developments in machine learning. Possibilities to learn translators between artefacts without a common physical context, for example in simulations of digital twins and across layers of the automation pyramid are briefly discussed.

HSV kategori
Forskningsprogram
Industriell elektronik
Identifikatorer
URN: urn:nbn:se:ltu:diva-73562OAI: oai:DiVA.org:ltu-73562DiVA, id: diva2:1303760
Forskningsfinansiär
EU, Horizon 2020, 737459Tilgjengelig fra: 2019-04-10 Laget: 2019-04-10 Sist oppdatert: 2019-10-03
Inngår i avhandling
1. System of Systems Interoperability Machine Learning Model
Åpne denne publikasjonen i ny fane eller vindu >>System of Systems Interoperability Machine Learning Model
2019 (engelsk)Licentiatavhandling, med artikler (Annet vitenskapelig)
Abstract [en]

Increasingly flexible and efficient industrial processes and automation systems are developed by integrating computational systems and physical processes, thereby forming large heterogeneous systems of cyber-physical systems. Such systems depend on particular data models and payload formats for communication, and making different entities interoperable is a challenging problem that drives the engineering costs and time to deployment. Interoperability is typically established and maintained manually using domain knowledge and tools for processing and visualization of symbolic metadata, which limits the scalability of the present approach. The vision of next generation automation frameworks, like the Arrowhead Framework, is to provide autonomous interoperability solutions. In this thesis the problem to automatically establish interoperability between cyber-physical systems is reviewed and formulated as a mathematical optimisation problem, where symbolic metadata and message payloads are combined with machine learning methods to enable message translation and improve system of systems utility. An autoencoder based implementation of the model is investigated and simulation results for a heating and ventilation system are presented, where messages are partially translated correctly by semantic interpolation and generalisation of the latent representations. A maximum translation accuracy of 49% is obtained using this unsupervised learning approach. Further work is required to improve the translation accuracy, in particular by further exploiting metadata in the model architecture and autoencoder training protocol, and by considering more advanced regularization methods and utility optimization.

sted, utgiver, år, opplag, sider
Luleå University of Technology, 2019
Serie
Licentiate thesis / Luleå University of Technology, ISSN 1402-1757
Emneord
system of systems interoperability, machine learning, message translation, information interoperability, autoencoder, cyber-physical systems
HSV kategori
Forskningsprogram
Industriell elektronik
Identifikatorer
urn:nbn:se:ltu:diva-76229 (URN)978-91-7790-458-8 (ISBN)978-91-7790-459-5 (ISBN)
Presentation
2019-11-28, E632, Regnbågsallén E7, Luleå, 10:00 (engelsk)
Opponent
Veileder
Prosjekter
Productive 4.0
Tilgjengelig fra: 2019-10-03 Laget: 2019-10-03 Sist oppdatert: 2019-10-03bibliografisk kontrollert

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https://arxiv.org/abs/1903.10735

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Nilsson, JacobSandin, FredrikDelsing, Jerker

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