DELIVERABLES

This deliverable reports on the specification of the FACTLOG architecture, reflecting the work performed in the context of Tasks 1.3 “Requirements analysis” and 1.4 “Architecture definition”, and the outcomes thereof. Following the analysis of the pilot scenarios identified, as well as the elaboration of use cases and system requirements, this deliverable proceeds with describing the components and main functions of the FACTLOG ecosystem.

To a great extent, FACTLOG architecture is centred around the concept of Digital Twins (DTs). In the context of Cyber-Physical Systems (CPS), a digital twin of a production system can address the challenge of making systems easily and quickly reconfigurable by being applied for realising and testing reconfiguration scenarios in a simulative environment; recommissioning of the production system therefore takes less time and enables a higher system availability. FACTLOG in fact extends this concept with the Enhanced Cognitive Twin (ECT), which can be seen as the evolution of the digital twin in the big data era. As such it provides cognitive capabilities to the digital twin, enabling learning from the vast streams of data that flow through it and thus the continuous modelling of the physical element’s behaviour. In other words, an ECT consists of all the characteristics of a digital twin, with the addition of artificial intelligence features enabling it to optimize operation as well as continuously test what-if-scenarios, paving the way for predictive maintenance and an overall more flexible and efficient production using stored operation data in the digital twin throughout its lifecycle. Furthermore, having an intelligent cyber layer expands the digital twin with self-x capabilities such as self-learning or self-healing, facilitating its inner data management as well as its autonomous communication with other digital twins.

In order to achieve the above, a digital twins platform is put in place as the focal point of access to all manufacturing entities (MEs) operational context in FACTLOG, facilitating the realisation of the three main aspects characterising digital twins: synchronisation with the physical asset, active data acquisition from the production environment, and the ability of simulation through the appropriate interfaces. On this basis, a number of cognitive services cooperate in order to turn digital twins into ECTs.

The analytics modules hold the critical role of extracting knowledge from the data ingested into FACTLOG. This may take various forms, beginning with the creation of data-driven models based on detected anomalies and variations in past data. They may also perform predictions, which, combined with the above models, help identify situations of interest and thus facilitate variation understanding and root-cause establishment in the cognition cycle. Certain analytics tools are also in position to perform simulations based on industry data, which can in turn be exploited for indicating potential actions to improve the situations (insights).

However, data is not the only source of knowledge in FACTLOG; the solution also incorporates human domain experience formally expressed by means of two frameworks, the Knowledge Graph Model (KGM) and the Process & Simulation Model (PSM). The former formalises rather static aspects of FACTLOG deployments, denoting a generic ontology representation and description of all FACTLOG mechanisms and tools, but also of the specific MEs on occasion guiding their consistent representation through digital twins. The latter models the dynamic behaviour of each considered manufacturing process, may it be a continuous or a discreet one. Both the KGM and the PSM are invaluable in putting the results generated by other components in context, as well as verifying and evaluating them, while they themselves are refined through their interactions with other modules, thus exploiting newly-acquired knowledge towards the continuous evolution of FACTLOG cognition capabilities.

FACTLOG also contains a dedicated module which can solve short-, mid- and long-term well-defined production optimization problems. Depending on each particular production setting, it can address a variety of optimization issues, ranging from production schedules for approval at the shopfloor to re-scheduling and re-configuration of machines or entire processes, taking into account a variety of constraints. In that, the optimization module uses appropriate outputs of analytics as inputs to perform its functions, guided throughout the procedure by the KGM and PSM. This in itself is, among others, a good example of how new knowledge extracted from the data and used to refine the behaviour models of the digital twin benefits the cognition process; indeed, given that the optimization module provides assistance to optimize the production in various concerns, above refinement allows the digital twin to incrementally improve its behaviour and features and thus to steadily optimize the above-mentioned assistance, due to the fact that the data analytics results are directly incorporated into the system and models.

Finally, in order to provide for effective interaction, coordination and orchestration of FACTLOG components and operations, the architecture includes the Message and Service Bus (MSB), being a mediation middleware between the components of the FACTLOG ecosystem. The MSB comprises a messaging and streaming system providing for asynchronous and point-to-point message exchange between the system entities, circulation of events, and delivery of data in a real-time or batch-oriented fashion. Furthermore, the MSB supports integration of the production infrastructure objects and data sources, by means of appropriate connectors. The MSB incorporates also the functionality for the orchestration of FACTLOG components and operations as regards the execution of dataflows.