Model driven engineering of operations management systems (OMS) using standardized plant and process models

Prosjektet har hatt som mål å bruke digitale modeller av industrielle anlegg til å støtte utvikling og skalering av programvare for disse industrielle anleggene. Det har blitt utviklet en mer effektiv, interaktiv måte å integrere ulike kilder for digitale modeller på i en felles representasjon kalt en graf. Prosjektet har videre sett på nyttiggjøring av disse grafene til å utvikle analytisk programvare og for å utvikle programvare som sporer objekter i produksjonsprosesser. Prosjektet har utviklet en ny metodikk og verktøy for kontekstualisert tilgang til tidsserier som støtter en kombinasjon av skybasert og lokal infrastruktur som fungerer i situasjoner som ikke er støttet av nåværende verktøy og som har langt høyere ytelse i situasjoner hvor begge verktøyene fungerer. Prosjektet har også resultert i en ny fremgangsmåte for å bygge programvare for objektsporing som kan gjøre det enklere å utvikle og vedlikeholde denne typen programvare, siden programvaren rekonfigurerer seg selv når anlegget og de digitale modellene av anlegget blir forandret.

The results can potentially simplify the development, maintenance and scaling of industrial software. The project has led to new knowledge within the use of knowledge graphs in industry through a new methodology that has been shown to solve problems that have not been able to be solved so far in a way that has better performance than existing approaches. Due to the fact that the methodology is largely based on open, established standards, it can potentially represent an open ecosystem for industrial contextualization that has greater competition, more innovation and better conditions for SMEs than the existing closed platforms. The project has given Prediktor expertise in graph technology and model-based software development, which now forms the basis for the new software in the investment area of asset management for renewable energy (solar, wind). The principle of using queries to integrate software is used in this software.

Companies managing physical processes need to supervise the operation of the processes involved. E.g. in a bakery one might want to know how recipe- and operating parameters, such as baking temperatures and time, influences the produced bread parameters such as taste, crust, consistency and looks. Many producers use manual systems to register production data based on e.g. paper or in Excel. Big companies often invest in computer-based systems, often termed Operation Management Systems (OMS). Realization of a computer-based OMS is usually expensive, due to the amount of labor required to build a new system. The goal of this project is to find methods so that OMS applications can be developed with minimum engineering time by persons holding limited knowledge of the software, but with knowledge about the actual production facility and its needs. Model driven (software) engineering is about doing software development by manipulating domain specific models instead of writing code. Code is in turn generated automatically from domain specific models in prescribed ways. This PhD will address methods for minimizing the manual effort needed to construct an OMS by using a model-driven approach that leverages the standardized information sources existing in a production unit. The basic idea is that these information sources can be understood to partially specify OMS software, and that engineering time can be further reduced by integrating these data sources iwhen specifying the model. This research problem is the first among the open research problems listed by Vyatkin (2013) in his review of Software Engineering in Industrial Automation.