It becomes apparent particularly in process industries that cognition can improve the behaviour of a complex process system. One of the main expectations of the use of digital twins is to give us the capability to observe and monitor the behaviour of their respective physical twins. In order to make it happen, we need to combine digital twins, which are driven by domain models (i.e. knowledge), with the models derived from data (i.e., experience).
In order to realize it, we need a real-time processing layer where observations (i.e. events), knowledge and experience interoperate to understand and control the behaviour of a complex system (i.e. cognition). FACTLOG offers such a layer and aims at deploying and adjusting it to several process industries.
By incorporating different pipelines of machine learning and analytical tools at different levels (from machines to process steps and from processes to the whole production plant), FACTLOG enables the realization of the Cognitive Factory as an ensemble of independent but intertwined ECTs, that are (i) able to self-learn, and thus to effectively detect and react to anomalies and disruptions but also to opportunities that may arise, (ii) enjoy a local or global view of operations and (iii) are capable for short-, mid- and long-term reasoning and optimization.
FACTLOG is driven by several specific, yet indicative, business cases in the process industry and focuses its innovation regarding analytics, AI and optimization on the deployment and assessment of coherent Enhanced Cognitive Twins for the specific sectors represented in FACTLOG and even for the plants in which it will be pilot tested and evaluated.
It will be implemented by a just right consortium of twenty partners, five of which are manufacturers and a further three represent manufacturing clusters. Technology and scientific contributors from leading academic institute and focused ICT vendors (mostly SMEs) bring in all necessary knowledge and innovation.
Start to End Date: 01/10/2019 - 01/04/2022
Duration: 42 months