STREAM - Innovations for Sustainable, Smart, and Efficient Automation

STREAM is a VINNOVA funded challenge driven innovation, where researchers from SICS, in collaboration with industry, have created new and competitive automation solutions based on advanced IT and customized business models. The automation solutions were created through integration and further development of current technologies for automated condition monitoring, diagnosis, planning and optimization in existing systems.

The project started in 2012 and represent a strong constellation. The project is led by researchers from SICS in Västerås and Kista and by researchers from Mälardalen University and is implemented in collaboration with ABB, Addiva, Atlas Copco, Blue Institute, Bombardier, Eduro, Mälarenergi, Prevas, Trafikverket and Volvo CE. 

The case studies done in participation with industry is an important part of the project. The results from the case studies is what the STREAM toolbox is built on. The toolbox consists of tools and algorithms that, with some adjustment, can be used in other production environments. Eventually this toolbox will consist of a broad spectrum of innovative products and services for process automation with direct impact on future production and maintenance management.

The innovations in the STREAM project contributes to increased competitiveness through technical solutions that are translated into new products and services. These products are generic, meaning that they can be used in several industries and will be available to stakeholders outside the project. It will also strengthen Swedish industry with a more energy efficient operation and with a safer workplace which will lead to increased competitiveness.

The STREAM toolbox is available as open source code and as precompiled binaries for major platforms under a permissive open source licence at GitHub. The toolbox is free for anyone to download, use and integrate in new products. You can find the toolbox at

STREAM – Movie with explanation about the solutions and how they were developed:


Short version about the three cases in STREAM:


Short version about the STREAM toolbox:


More information

strömmen av information för smart effektiv automation


Suppliers: ABB, Bombardier Transportation, Prevas, Addiva, Atlas Copco,

Academy and institutes: SICS Swedish ICT, Mälardalens Högskola, Blue Institute

Customers: TTI Algeciras, Long Beach Container Terminal, SJ, Green gargo, Outokumpu

Innovation team: Automation Region, Westermo, Länsstyrelsen Västmanlands län, Motion Control,  Linköpings Universitet, ICA, Volvo Construction Equipment, Trafikverket

Case studies
STREAM is a cross-industry project for advanced automation. Three case studies are included in the project. The results of the case studies will be part of a toolbox of relatively generic modules that STREAM will produce. The modules of the toolbox will be applicable throughout the automation industry.


The case study will develop decision support to train drivers for energy optimal driving. Given parameters like scheduling, track profile, load or traffic lights along the way it can be energetically advantageous to pull up or slow down at different times. The results of STREAM will be used to produce energy optimal motion profiles for drivers as well as for automatic control systems. This knowledge will also increase the possibility of replacing diesel trains with battery-operated trains on non-electrified sections of track.


The case study is about managing the vast amount of operational messages and errors from cranes in container terminals, in order to react quickly when something is about to go wrong. At the moment, the case study is analysing data from the container cranes. From event data we are hoping to find cranes with an abnormal frequency of events, which may indicate errors. The case study will also collect information on the movements of containers the cranes perform. From these data, one can assess whether some cranes systematically takes more time for the transfers than they should, and thus find cranes with errors that cause production losses.