PreCoM | Predictive Cognitive Maintenance Decision Support System
01-11-2017
-31-10-2020
01-11-2017
-31-10-2020
01-10-2017
-31-03-2021
01-09-2017
-31-08-2020
UPTIME platform focusses on the use of condition monitoring techniques, e.g. event monitoring and data processing systems, that will enable manufacturing companies having installed sensors to fully exploit the availability of huge amounts of data and to handle the real-time data in complex, dynamics environement in order to get meaningful insights and to decide and act ahead of time to resolve problems before they appear, e.g. to avoid or mitigate the impact of a future failure, in a proactive manner. Moreover, UPTIME proposed unified framework will not be limited to monitoring and diagnosis but it aims to cover the whole prognostic lifecycle from signal processing and diagnostics till prognostics and maintenance decision making along with their interactions with quality management, production planning and logistics decisions.
01-10-2017
-31-03-2021
Z-break will make it possible to combine the current manufacturing systems with current and new mechatronic systems. These combinations will lead to smarter manufacturing systems and thus a shorter ramp up in generating higher quality and productivity.
Part of the Z-BRE4K project is the development, of a novel embedded condition monitoring solution with cognitive capabilities, by applying deep learning techniques to reduce the dimensionality of multimodal sensor data associated to a given machine/device, and provide meaningful features to predictive maintenance services on the cloud. Most suitable IoT edge devices, for optimal trade-off between computational power and energy consumption, sensors, providing relevant information of the condition of different components, and signal processing algorithm are proposed for different machines and processes. Data gathering is enabled by the installation of IoT gateways, where data in different protocols are homogenised and sent to the cloud for storage. Real-time data, relevant KPIs and information about components status are visualised through dedicated dashboards.
01-10-2018
-31-03-2022
The ROSSINI modular KIT offers advance components for the different layers of a robotic application (sensing, perception, cognition, control, actuation and integration)
The RS4 Controller gather and fuse data from different sensor sources. Within ROSSINI the following sensor sources have been developed as EXTRA components able to be connected with the RS4 controller: 3D Vision cameras, Lidar arrays, Radars and Skins.
The ROSSINI Controller (CORE component of the ROSSINI Modular KIT) integrates a Semantic Scena Map, a Flexible layer (Scheduler) and an Execution layer (Motion Planner) to guarantee optimal efficiency of the robot (also considering Job Quality factors)
Within the ROSSINI project an advanced collaborative robot have been developed, equipped with advanced and novel interfaces, and able to perfom very low breaking time.
All the three ROSSINI demonstrators (white goods, electronic equipment, and food packaging) proof the feasibility and the advantages of ROSSINI Platform Implementations in relevant (diffenet and complex) industrial environments
Human-Robot Collaboration is key in the development of the Rossini solution: all the use cases look at this scenario, allowing the human operation working in the same cell with the robot, on the same machine and even on the same work-piece.
01-11-2018
-31-10-2022
01-10-2018
-31-03-2022
01-10-2018
-30-09-2022
01-12-2018
-30-11-2022
Nikon K-CMM Metrology - By positioning LEDs on the robot end effector, and on the target parts, the K-CMM system can measure relative positioning to a very high degree of accuracy even over large distances.
Siemens TIA Portal, WinCC, PLCs – The lower level control of resources in the system was performed with Siemens brand programmable logic controllers
Nikon Adaptive Robotic Control (ARC) – this technology allows data from metrology systems to correct a robot controller’s coordinate system and compensate for inaccuracies and variability.
KUKA Robotics – Compatible with the ARC system, the KUKA robots were used for part positioning.
Nikon K-CMM Metrology - By positioning LEDs on the robot end effector, and on the target parts, the K-CMM system can measure relative positioning to a very high degree of accuracy even over large distances.
Siemens TIA Portal, WinCC, PLCs – The lower level control of resources in the system was performed with Siemens brand programmable logic controllers
Nikon Adaptive Robotic Control (ARC) – this technology allows data from metrology systems to correct a robot controller’s coordinate system and compensate for inaccuracies and variability.
KUKA Robotics – Compatible with the ARC system, the KUKA robots were used for part positioning.
01-10-2020
-30-09-2023