Demonstration of PreCoM system in high-volume manufacturing (Spinea)
Project: PreCoM
Updated at: 04-10-2022
Project: PreCoM
Updated at: 04-10-2022
Project: PreCoM
Updated at: 04-10-2022
Project: Z-BRE4K
Updated at: 04-10-2022
Project: Z-BRE4K
Updated at: 04-10-2022
PHILIPS supports the idea of predictive maintenance, “listening to the machines” and understands that the key to success is close contact between technology providers and experts where data integration/architecture and machine learning are both very important projects.
Project: Z-BRE4K
Updated at: 04-10-2022
SACMI-CDS found out the importance of collaboration not only with a mechanical engineering/maintenance-related professionals but also with different technical background experts that together can improve multi-tasking and combining shopfloor and office-related activities as well as scheduling of activities during the work journey.
In general, after the solution implementation (TRL5), testing the system on the shop floor (TRL6) and validation of the Z-BRE4K solution (TRL7) at end users, the very final lesson learnt can be summarised as follows:
Project: Fortissimo 2
Updated at: 03-10-2022
Project: UPTIME
Updated at: 03-10-2022
Installation of sensor infrastructure: during the initial design to incorporate the new sensors into the existing infrastructure, it is necessary to take into consideration the extreme physical conditions present inside the milling station, which require special actions to avoid sensors being damaged or falling off. A flexible approach is adopted, which involves the combination of internal and external sensors to allow the sensor network prone to less failure. Quantity and quality of data: it is necessary to have a big amount of collected data for the training of algorithms. Moreover, the integration of real-time analytics and batch data analytics is expected to provide a better insight into the ways the milling and support rollers work and behave under various circumstances.
Project: UPTIME
Updated at: 03-10-2022
Quantity and quality of data need to be ensured from the beginning of the process. It is important to gather more data than needed and to have a high-quality dataset. Machine learning requires large sets of data to yield accurate results. Data collection needs however to be designed before the real need emerges. Moreover, it is important having a common ground to share information and knowledge between data scientists and process experts since in many cases they still don’t talk the same language and it takes significant time and effort from mediators to help them communicate properly.
Project: UPTIME
Updated at: 03-10-2022
Quantity and quality of data: the available data in the FFT use case mainly consists of legacy data from specific measurement campaigns. The campaigns were mainly targeted to obtain insights about the effect of operational loads on the health of the asset, which is therefore quite suitable to establish the range and type of physical parameters to be monitored by the UPTIME system. UPTIME_SENSE is capable of acquiring data of mobile assets in transit using different modes of transport. While this would have been achievable from a technical point of view, the possibility to perform field trials was limited by the operational requirements of the end-user. Therefore, only one field trial in one transport mode (road transport) was performed, which yielded insufficient data to develop useful state detection capability. Due to the limited availability of the jig, a laboratory demonstrator was designed to enable partially representative testing of UPTIME_SENSE under lab conditions, to allow improvement of data quantity and diversity and to establish a causal relationship between acquired data and observed failures to make maintenance recommendations.
Project: SYMBIO-TIC
Updated at: 29-09-2022
Project: SYMBIO-TIC
Updated at: 29-09-2022
Project: BEinCPPS
Updated at: 29-09-2022
Project: BEinCPPS
Updated at: 29-09-2022
Project: PROPHESY
Updated at: 26-09-2022
Project: PROPHESY
Updated at: 26-09-2022
Project: RebootIoTFactory
Updated at: 16-09-2022
Project: KYKLOS 4.0
Updated at: 01-06-2022
Project: KYKLOS 4.0
Updated at: 01-06-2022
Project: KYKLOS 4.0
Updated at: 01-06-2022
Project: KYKLOS 4.0
Updated at: 01-06-2022
Project: KYKLOS 4.0
Updated at: 01-06-2022
Project: KYKLOS 4.0
Updated at: 01-06-2022
Project: Arrowhead Tools
Updated at: 24-03-2022
Project: DigiPrime
Updated at: 03-02-2022
Project: DigiPrime
Updated at: 03-02-2022
The Battery Pilot will aim at demonstrating that the DigiPrime platform can unlock a sustainable business case targeting the remanufacturing and re-use of second life Li-Ion battery cells with a cross-sectorial approach linking the e-mobility sector and the renewable energy sector, specifically focusing on solar and wind energy applications.
As the proactive exploitation of the DigiPrime platform enables the car-monitored SOH tracing and availability, less testing is needed to assess the residual capacity of the battery. Moreover, by knowing the structure of the battery packs, a decision support system can be implemented to adjust the de-and remanufacturing strategy accordingly and select the most proper cells for re-assembly second-life modules, thus unlocking a systematic circular value chain for Li-ion battery cells re-use. Furthermore, excessively degraded cells which cannot be re-used can be sent to high-value recycling, based on the knowledge of their material compositions.
Project: AMable
Updated at: 03-02-2022
Project: Digital Fibre Ecosystem
Updated at: 03-02-2022
Benefits:
Project: Arrowhead Tools
Updated at: 23-01-2022
Project: Arrowhead Tools
Updated at: 23-01-2022
GESTAMP, besides getting familiar with Z-BRE4K’s solution validation and assessment methodology, got a better understanding of internal reflection and readiness to apply predictive maintenance solutions to its plants while new mitigation actions related to process flaws and defects identification were developed during the Z-BRE4K. Also, they have understood the importance of solution validation and assessment methodology defined in Z-BRE4K.