Production companies need systems for execution, reporting, analyzing and optimizing their production. These systems may be computer-based OMS systems - meaning Operation Management Systems, or they are ad hoc systems based on paper, Excel sheets etc. The project focuses on making applications and tools that dramatically lowers the barriers of cost and complexity for realizing and operating such systems. The target system based on the project findings will be cloud hosted and OMS- and related applications can be generated automatically from generic plant information models (digital twin patterns). One OMS related field which is presently often neglected, is the preparation of data, prior for applying artificial intelligence (AI) and machine learning (ML) methods. A lot of companies and methods has been created for AI/ML purposes. The methods planned to be developed in this project is almost a prerequisite for the success of such applications. R&D challanges targeted in the SkyTrack project are: - To classify plants into types that can be represented by similar digital twins. - To find algorithms and methods for automatic or semiautomatic transformation of signal names (tags) and parameter names in plant databases into SkyTrack digital twins names. - To find methods for automatic generation of plant models into digital twins. - How to optimize the user interface to enable end users and system integrators to configure and realize cloud based OMS based on basic information models and automatic application generators. Solving these research challenges will make possible to reduce the engineering side of the cost of realization of an OMS applications. In addition, the entry cost will be small and scalable for the user.
Project leader: Anders Lydersen
Institution: PREDIKTOR AS