Data processing is, generally, the collection and manipulation of items of data to produce meaningful information (https://en.wikipedia.org/wiki/Data_processing)
Directly mapped projects
Showing 10 last updated out of 22
Show all| Acronym | Title | |||||
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BEinCPPS | Business Experiments in Cyber Physical Production Systems | ||||
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A4BLUE | Adaptive Automation in Assembly For BLUE collar workers satisfaction in Evolvable context | ||||
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FACTS4WORKERS | Worker-Centric Workplaces in Smart Factories | ||||
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INCLUSIVE | Smart and adaptive interfaces for INCLUSIVE work environment | ||||
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ConnectedFactories | Industrial scenarios for connected factories | ||||
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ICP4Life | An Integrated Collaborative Platform for Managing the Product-Service Engineering Lifecycle | ||||
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Comment: Data processing in the services is automatic with different algorythms.
Data processing in the platform is manual and done by the user. Decisión is, in most cases, manual.
Comment: Several acquisition technologies depending on the service:
From sensors, from PLC, CNC, vibration sensors, counters, artificial visión cameras....
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UPTIME | UNIFIED PREDICTIVE MAINTENANCE SYSTEM | ||||
Comment:
UPTIME will reframe predictive maintenance strategy in a systematic and unified way with the aim to fully exploit the advancements in ICT and maintenance management by examining the potential of big data in an e-maintenance infrastructure taking into account the Gartner’s four levels of data analytics maturity and the
Comment:
UPTIME will enable manufacturing companies to reach Gartner's four levels of data analytics maturity for optimised decision making - each one building on the previous one: Monitor, Diagnose and Control, Manage, Optimize - aims to optimise in-service efficiency and contribute to increased accident mitigation capability by avoiding crucial breakdowns with significant consequences. UPTIME Components UPTIME_DETECT & UPTIME_PREDICT and UPTIME_ANALYZE aim to enhance the methodology framework for data processing and analytics. The key role for the UPTIME_DETECT and UPTIME_PREDICT components are data scientists who are in charge of developing, testing and deploying algorithmic calculations on data streams. In this way, the component is able to to identify the current condition of technical equipment and to give predictions. On the other hand, the UPTIME_ANALYZE is a data analytics engine driven by the need to leverage manufacturers’ legacy data and operational data related to maintenance, and to extract and correlate relevant knowledge.
Comment:
The "Persistence" layer in the UPTIME conceptual architecture includes a Database Abstraction Layer (DAL) and houses of relational database engine as well as a NoSQL database, where all information needed by the "User Interaction" and "Real-Time Procesing and Batch Processing" layers (refer to UPTIME conceptual architecture) is stored and retrieved. For the raw sensor data itself, this data storage concept is enhanced by a database for time-series data to ensure efficient and reliable storage, while visualization functionalities will use an indexing database to facilitate the exposure of analytics. In these databases, all information needed by the other three layers is stored and retrieved. The UPTIME solution aims to provide data harmonization in terms of manipulating streaming data coming from the sensors. Based upon these needs a time series database is needed and in the context of UPTIME three instances of influxDB (one instance per business case) are installed. Along with the influxDB instances, a common MySQL database that will handle the operations of the UPTIME system is created.
Comment:
UPTIME has a common MySQL database that will handle the operations of the UPTIME system.
Comment:
UPTIME Data are stored in appropriate, shared databases (NoSQL, time-series-based, relational) according to a common UPTIME predictive maintenance model in order to facilitate homogeneous access.
Comment:
In UPTIME, two data processing solutions are considered. (1) Batch processing of data at rest, through massively paralle processing, (2) real-time processing of data in motion, real time data from heterogeneous sources are processsed as a continuous "stream" of events (produced by some outside system or systems), and that data processing occurs so fast that all decisions are made without stopping the data stream and storing the information first.
Comment:
UPTIME main functionalities are structured in three main modules, namely: edge, cloud and GUI modules.
Comment:
4 main components in the cloudbased infastructure of the UPTIME platform include:
Comment:
UPTIME_SENSE component (USG prototype) is located in the edgebased infrastructure of the UPTIME platform. It aims to capture data from a high variety of sources and cloud environments. SENSE brings configurable diagnosis capabilities on the edge, e.g. for real-time or off-the-grid applications. SENSE addresses 3 high level functions:
Comment:
The UPTIME_SENSE component is responsible for the acquisition of sensor data from the field. It is utilised to enable previously disconnected assets, to communication with the UPTIME Cloud.
Comment:
The current draft of the UPTIME data model is designed based on international standards like MIMOSA (OSA-CBM v3.3.1 and OSA-EAI v3.2.3a), the initial historical data provided by the business cases and the previous implementations of UPTIME_FMECA and UPTIME_DECIDE. |
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Fortissimo 2 | Factories of the Future Resources, Technology, Infrastructure and Services for Simulation and Modelling 2 | ||||
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ENCOMPASS | ENgineering COMPASS | ||||
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COMPOSITION | Ecosystem for Collaborative Manufacturing Processes _ Intra- and Interfactory Integration and Automation | ||||
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Comment: Big data analytics in cloud environments to support manufacturing
scenarios.
Comment: The platform will apply smart data processing methods for investigating the dataset in order to find logical links. It will also investigate novel complex event processing approaches.
Comment: Data acquisition tools to instantiate virtual manufacturing processes.
Comment:
The COMPOSITION project contains a Digital Factory Model Component which offers representation of factory processes and resources in a common format based on well-known standards. |
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Directly mapped results & demonstrators
Showing 10 last updated out of 19
Show all| Acronym | Title | |||||
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| Nebbiolo USE-CASE-Precision-Machine | Machine Fog Node (MFN) Case Study | |||||
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ZHQ | Lombardy Champion - Zero-Hours Quality | ||||
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Comment: Cloud level: I2Lab
Comment: Cloud level: Test Back-end
Comment: Field level: Controller/Actuator (HW), TestExecutor (SW)
Factory level: FIWARE IDAS Gateway for CPPS
Comment: (offline): MSEE BIM Toolbox, UAModeler
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Machine Tool Digital Twin | Machine Tool Digital Twin with Life Cycle features | ||||
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Local Monitoring and Control System | Local Monitoring and Control System | ||||
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Fleet Management Platform for Machine Tools | Fleet Management Platform for Machine Tools | ||||
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Fortissimo Experiment 706 | HPC based high-resolution modelling of magnets | ||||
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Fortissimo Experiment 707 | Cloud-based-HPC optimisation of manufacturing processes | ||||
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Fortissimo Experiment 709 | Cloud-based simulation of box framed timber beams | ||||
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Fortissimo Experiment 710 | Near Real-time Analysis of Airframe Certification Test Data | ||||
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Fortissimo Experiment 701 | Cloud-based simulation for vehicle engineering | ||||