SeCoIIA | Secure Collaborative Intelligent Industrial Assets
01-12-2019
-31-05-2022
01-12-2019
-31-05-2022
01-11-2019
-30-04-2023
01-11-2018
-30-04-2022
M4.0 metal domain addresses the BUSINESS LAYER of the vertical side of RAMI and the ENTERPRISE and CONNECTED WORLD layers of the hyerarchical side
Used components:
IDS connectors for data provider, data consumer ; Clearing house ; Broker
01-09-2017
-31-08-2020
The UPTIME platform is built upon the predictive maintenance concept, the technological pillars (i.e. Industry 4.0, IoT and Big Data, Proactive Computing) and the existing baseline tools (i.e. USG, preInO, PANDDA, SeaBAR, DRIFT) resulting in a unified information system for predictive maintenance. The extended UPTIME baseline tools (SENSE, DETECT, PREDICT, DECIDE, ANALYZE, FMECA, VISUALIZE) will address the various steps of the unified predictive maintenance approach and will incorporate interconnections with other industrial operations related to production planning, quality management and logistics management.
Open Platform Communication Unified Architecture (OPC-UA) is considered for UPTIME platform and for modular edge data collection and diagnosis of UPTIME_SENSE component.
To ensure secure access, the UPTIME Platform offers appropriate authorization and authentication mechanisms. These are based on the JWT technology and are implemented by using the Spring Security framework. Currently, JWT is used to ensure a secure log-in; as components are iteratively integrated. JWT will also be used to ensure secure communications between components.
The UPTIME conceptual architecture was designed according to the ISO/IEC/IEEE 42010 “System and software engineering – Architecture description” and mapped to RAMI 4.0 in order to ensure that it can be represent predictive maintenance in the frame of Industry 4.0.
The UPTIME vision converges and synthesizes predictive maintenance, proactive computing, the Gartner’s levels of industrial analytics maturity and the ISO 13374 as implemented in MIMOSA OSA-CBM in order to create a consistent basis for a generic predictive maintenance architecture in an IoT-based industrial environment. In this way, the Operational Technology and the Information Technology can also be converged in the context of Industry 4.0.
One of the main functionalities of UPTIME Platform is the batch data analytics implemented by UPTIME_ANALYZE component to analyse maintenance-related data from legacy and operational data and UPTIME_FMECA component that provides estimation of possible failure modes. The interoperability interfaces with UPTIME End-Users' (e.g. Whirlpool) legacy systems are defined, specified and developed according to latest practices and standards for APIs.
01-10-2017
-31-03-2021
Α Web API will return semantic data. The communication interface is through the SPARQL query engine. Z-BRE4K ontology is implemented with the Open Semantic Framework (OSF), an integrated software stack using semantic technologies for knowledge management. Furthermore, JSON formatted data from the shop floor is transferred through a MQTT broker, to be finally stored in I-LiKe machines internal data repository. IDS connectors are used to transform data into the NGSI format, move the data to the ORION context broker to be finally consumed by other applications. Also, the Quality Information Framework (QIF) standard guarantees interoperability since it defines an integrated set of information models that enable the effective exchange of metrology data throughout the entire manufacturing quality measurement process – from product design to inspection planning to execution to analysis and reporting. OpenCPPS (part of AUTOWARE) will provide support for selected mainstream communication protocols and will define the proper interfaces for other communication protocols to be plugged-in.
Orion is a C++ implementation of the NGSIv2 REST API binding developed as a part of the FIWARE platform that allows the management of the entire lifecycle of context information including updates, queries, registrations and subscriptions. It is an NGSIv2 server implementation to manage context information and its availability allowing subscription to context information so when some condition occurs notifications are sent. The Industrial Data Space foster secure data exchange among its participants, while at the same time ensuring data sovereignty for the participating data owners. The architecture of the Industrial Data Space does not require central data storage capabilities but follows a decentralized approach, meaning that data physically remain with the respective data owner until they are transmitted to a trusted party. Thus, the Industrial Data Space is not a cloud platform, but an architectural approach to connect various, different platforms.
Ontology-based data integration is part of the Z-BRE4K solution. Ontology effectively combines data and/or information from multiple heterogeneous sources. The ontology semantics used by SPL program is described through OWL. OWL follows the RDF syntax, so SPARQL is suitable for seamlessly querying the ontology defined by OWL. SPARQL will be used as the transformation language for converting Semantic data to corresponding syntax data. IDS connectors are used in Z-BRE4K to guarantee the interoperability among the various components that are not part of the Industrial Data Space. Part of connectors functionality is to transform data to/from NGSI format data in order to be shared by the ORION context broker.
Z-BRE4K ontology contains information about all Z-BRE4K relevant data (metadata), linked in a way described by a controlled, shared vocabulary. The data relationships are part of the data itself, in one self-describing information package that is independent of any information system. In simple terms, this means that data from various sources can be easily harmonised. The shared vocabulary, and its associated links to an ontology, provide the foundation and the capabilities of machine interpretation, inference, and logic.
The Z-Bre4k solution is based on the blackboard architectural model. This model is mainly an artificial intelligence approach, where a common knowledge base, the "blackboard", is iteratively updated by a diverse group of specialist knowledge sources, starting with a problem specification and ending with a solution. Each knowledge source updates the blackboard with a partial solution when its internal constraints match the blackboard state. In this way, the specialists work together to solve the problem. The blackboard model was originally designed as a way to handle complex, ill-defined problems, where the solution is the sum of its parts. The blackboard component acts as a central repository system. The rest of the software applications (components) act independently at the common data structure stored on the blackboard, they respond on changes and create new reactions according to changes. Interaction between components is implemented via the blackboard.
01-10-2018
-31-03-2022
The ROSSINI modular KIT solution is based on ROS to spur scalability and wide adoption
The Virtual Design Tool facilitates the integration of the ROSSINI components at the platform level.
The ROSSINI Controller (Semantic Scena Map, Flexible and Execution layers) is designed to improved efficiency in the production line/robotic cell (adapting to the possible changing in the environment and including job quality factors)
The RS4 System (RS4 Controller and sensors) is designed to improve safety also for standard robots
The ROSSINI Modular KIT is a set of components that can be integrated to implement robotic workcells, capable of increasing job quality and reducing reconfiguration time.
01-01-2019
-31-12-2022
01-05-2019
-31-07-2022
Check video at 48:05 and 1:51:04 (translation system)
01-11-2019
-31-10-2023
01-10-2019
-30-09-2023
01-10-2019
-31-03-2024
ATS Bus - Enabled a single, common service bus for data exchange between the PLCs and other high level components of the system, including a SCADA system. Used a broker-based publish-subscribe approach to decouple the physical sources and destinations of the data to facilitate reconfigurability.
Nservicebus - The underlying technology which enabled the ATS Bus to exchange data.
OPC UA (Kepware) – Many devices could not interface directly with the service bus, so OPC UA was used to extract data and publish it to the service bus.
OPC UA (Kepware) – Many devices could not interface directly with the service bus, so OPC UA was used to extract data and publish it to the service bus.
B2MML – Business to Manufacturing Mark-up Language. Data standard used to define the process (i.e. the set of operations to be carried out by the cell for each unique product), what resources are required for each process, the materials needed and more. The full process would be designed by engineers, and then the SCADA would break the ‘master’ B2MML process representation into sub-processes and send these to the resources via the service bus. These would then trigger the start of processes by the PLCs.
B2MML – Business to Manufacturing Mark-up Language. Data standard used to define the process (i.e. the set of operations to be carried out by the cell for each unique product), what resources are required for each process, the materials needed and more. The full process would be designed by engineers, and then the SCADA would break the ‘master’ B2MML process representation into sub-processes and send these to the resources via the service bus. These would then trigger the start of processes by the PLCs.
ATS Bus - Enabled a single, common service bus for data exchange between the PLCs and other high level components of the system, including a SCADA system. Used a broker-based publish-subscribe approach to decouple the physical sources and destinations of the data to facilitate reconfigurability.
Nservicebus - The underlying technology which enabled the ATS Bus to exchange data.
OPC UA (Kepware) – Many devices could not interface directly with the service bus, so OPC UA was used to extract data and publish it to the service bus.
OPC UA (Kepware) – Many devices could not interface directly with the service bus, so OPC UA was used to extract data and publish it to the service bus.
B2MML – Business to Manufacturing Mark-up Language. Data standard used to define the process (i.e. the set of operations to be carried out by the cell for each unique product), what resources are required for each process, the materials needed and more. The full process would be designed by engineers, and then the SCADA would break the ‘master’ B2MML process representation into sub-processes and send these to the resources via the service bus. These would then trigger the start of processes by the PLCs.
B2MML – Business to Manufacturing Mark-up Language. Data standard used to define the process (i.e. the set of operations to be carried out by the cell for each unique product), what resources are required for each process, the materials needed and more. The full process would be designed by engineers, and then the SCADA would break the ‘master’ B2MML process representation into sub-processes and send these to the resources via the service bus. These would then trigger the start of processes by the PLCs.
Used as the primary date exchange method for the demonstrator. Links items on the shop floor to the SCADA.
Used as the primary date exchange method for the demonstrator. Links items on the shop floor to the SCADA.
01-10-2022
-30-09-2026
01-09-2022
-31-08-2025
01-06-2022
-31-05-2025
01-01-2020
-31-12-2023
Interoperability (sharing of data and resources between different systems)
01-10-2020
-30-09-2023