1. Technologies and enablers
  2. Information and communication technologies
  3. Data collection, storage, analytics, processing and AI
  4. Data processing
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Data processing is, generally, the collection and manipulation of items of data to produce meaningful information (https://en.wikipedia.org/wiki/Data_processing)

Associated projects

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COMPOSITION | Ecosystem for Collaborative Manufacturing Processes _ Intra- and Interfactory Integration and Automation

01-09-2016

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31-08-2019

Calltopic: FoF.2016.11

: 117 | : 7
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Description One of the most imperative problems so far is to connect supply chain data and services between enterprises and to connect value chain data within a factory so that it can meaningfully support decision-making
Comments It will also investigate novel complex event processing approaches. Big data analytics in cloud environments to support manufacturing scenarios.
Result product International Data Space (IDS) Reference Architecture
 Big data mining and analytics tools I Result title Big data mining and analytics tools I
 Big data mining and analytics tools II Result title Big data mining and analytics tools I Result description On one side, the collection processing and management of data, which is the Big Data Analysis task.
 Big data mining and analytics tools II Result title Big data mining and analytics tools I
 Interoperability for M2M & HMI in factory environments II Result comments Connected IT and OT IoT enabled SCADA, MOM-MES, ERP (…) connectivity Humans actively connected Human Machine Interfaces Data communication and interoperability Reference docs
 Data processing Taxon title Data processing
 Data acquisition Taxon description Data acquisition systems, abbreviated by the acronyms DAS or DAQ, typically convert analog waveforms into digital values for processing.

SatisFactory | A collaborative and augmented-enabled ecosystem for increasing SATISfaction and working experience in smart FACTORY environments

01-01-2015

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31-12-2017

Calltopic: FoF.NMPB.2014.4

: 53 | : 29
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Comments with the participation in the Open Research Data (ORD) pilot.
 DELIVERABLE D6.2 - Data Management Plan Result title Data Management Plan
 Data Management Plan Result title Data Management Plan Result comments result_rcn_EC deliverableType_EC Open Research Data Pilot
 Periodic Reporting for period 2 - SatisFactory (A collaborative and augmented-enabled ecosystem for increasing SATISfaction and working experience in smart FACTORY environments) Result description Research and Development of advanced signal processing for early incident diagnosis at factory processes incorporating depth and thermal information processing. ?
 Manual data acquisition Taxon title Manual data acquisition
 Business model aspects of digital platform deployment Taxon description Another difference is the central role of data for platforms, meaning that data governance is one of the essential elements of the value proposition of platforms.   Comments with the participation in the Open Research Data (ORD) pilot.

ROSSINI | RObot enhanced SenSing, INtelligence and actuation to Improve job quality in manufacturing

01-10-2018

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31-03-2022

Calltopic: DT-FOF-02-2018

: 3 | : 0
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Comments Moreover, the deployment of artificial intelligence techniques for data processing will not be just aimed to enable robot perception, but will also make robot cognition possible.
 Data Management Plan Result title Data Management Plan Result description Data Management Plan (DMP): The plan will describe ways to manage all research data, and metadata, during and after the project duration; open data sets, means to access, updating, etc
 Data collection, storage, analytics, processing and AI Taxon title Data collection, storage, analytics, processing and AI
 Autonomous Smart Factories Comments Moreover, the deployment of artificial intelligence techniques for data processing will not be just aimed to enable robot perception, but will also make robot cognition possible.

Z-BRE4K | Strategies and Predictive Maintenance models wrapped around physical systems for Zero-unexpected-Breakdowns and increased operating life of Factories

01-10-2017

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31-03-2021

Calltopic: FoF.2017.09

: 11 | : 0
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Comments Different filtering and pre-processing algorithms are applied on the edge to clean real time raw data and reduce unwanted noise.
 Data Management Plan (DMP) Result title Data Management Plan (DMP) Result description The plan will describe ways to manage all research data, and metadata, during and after the project duration; open data sets, means to access, license attached, updating, etc.
 Data processing Taxon title Data processing Comments Different filtering and pre-processing algorithms are applied on the edge to clean real time raw data and reduce unwanted noise.
 Data acquisition Taxon description Data acquisition systems, abbreviated by the acronyms DAS or DAQ, typically convert analog waveforms into digital values for processing.
    Information and communication technologies
    Comment:

    The Z-Break solution uses a variety of communication protocols. HTTP, OPC-UA, IEEE 802.15.4e and IEC WirelessHART. The Hypertext Transfer Protocol (HTTP) is an application protocol for distributed, collaborative, hypermedia information systems. HTTP is the foundation of data communication for the World Wide Web. OPC UA supports two protocols. The binary protocol is opc.tcp://Server and http://Server is for Web Service. Otherwise OPC UA works completely transparent to the API. IEEE 802.15.4 is a technical standard which defines the operation of low-rate wireless personal area networks (LR-WPANs). It specifies the physical layer and media access control for LR-WPANs, and is maintained by the IEEE 802.15 working group, which defined the standard in 2003. WirelessHART is a wireless sensor networking technology based on the Highway Addressable Remote Transducer Protocol (HART). Developed as a multi-vendor, interoperable wireless standard, WirelessHART was defined for the requirements of process field device networks. Also, it uses the NGSI protocol. NGSI is a protocol developed to manage Context Information. It provides operations like managing the context information about context entities, for example the lifetime and quality of information and access (query, subscribe/notify) to the available context Information about context Entities.

      Data collection, storage, analytics, processing and AI
      Comment:

      Z-BRE4K solution provides a big data analytics framework for the identification of the deterioration trends to extended towards prescriptive maintenance. Advanced data analysis tools are under development, to be applied to the quality and production data to realise zero-defect and zero-break down production. Furthermore, it involves models for anomaly detection, that are capable of identifying the machine states where the operation deviated from the norm.  This is achieved by collecting the data from the machine sensors in chunks of time and processing them in batch through deep learning models. The models are trying to recreate their inputs, and this results in an observable measure called Reconstruction Error, which is used to identify states that the models aren’t capable of addressing sufficiently (which constitutes an anomaly.

        Data processing
        Comment:

        Z-BRE4K solution will provide an ontology with annotation mechanisms that include all the necessary information to perform predictive maintenance analysis to achieve extended operating life of assets in production facilities. This context includes the sensorial data processing to be used as simulation inputs and the simulation process itself (physics-based modelling). It also includes the machine learning application as well, due to the usage of prediction models in data-driven modelling. Knowledge Based System (KBS) will extract, store and retrieve all the relevant information enriched with semantic annotations to guarantee a prompt identification of criticalities. Shoop floor data is transformed into RDF (Resource Description Framework) data, a standard model for data interchange on the web, and stored in a triple store DB. Also, the M3 Gage platform serves for fast verification of the machine, condition monitoring, and as a data repository as well. It allows information interconnection from different data sources, and furthermore, the architecture proposed by AUTOWARE provides the ability to establish data processing and computing at the most appropriate abstraction level in the hierarchy: Field, Workcell/ Production Line, Factory and Enterprise. Different filtering and pre-processing algorithms are applied on the edge to clean real time raw data and reduce unwanted noise. In addition, convolutional neural networks are used to process high-throughput video streams, providing a non-time critical stream of features for cloud services.

DIY4U | Open Innovation Digital Platform and Fablabs for Collaborative Design and Production of personalised/customised FMCG

01-11-2019

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31-10-2022

Calltopic: DT-FOF-05-2019

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Comments personalized/customized formulations designed on the platform will be produced and characterized at MDFs (STEF and CPI) using existing high-throughput and other complex liquid/particle processing
 Data communication infrastructure Taxon title Data communication infrastructure
 Business model aspects of digital platform deployment Taxon description Another difference is the central role of data for platforms, meaning that data governance is one of the essential elements of the value proposition of platforms.  
 R&I Objective 4.4: Manufacturing Innovation and change management Comments personalized/customized formulations designed on the platform will be produced and characterized at MDFs (STEF and CPI) using existing high-throughput and other complex liquid/particle processing

THOMAS | Mobile dual arm robotic workers with embedded cognition for hybrid and dynamically reconfigurable manufacturing systems

01-10-2016

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30-09-2020

Calltopic: FoF.2016.02

: 13 | : 8
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 Pilot cell for automotive moving line final assembly Result description processing.
 Data processing Taxon title Data processing Taxon description Data processing is, generally, the collection and manipulation of items of data to produce meaningful information (https://en.wikipedia.org

DIGICOR | Decentralised Agile Coordination Across Supply Chains

01-10-2016

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30-09-2019

Calltopic: FoF.2016.11

: 22 | : 0
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Description The platform will provide seamless connectivity to the automation solutions, smart objects, and real-time data sources across the network.
Comments In order to facilitate the flow of data to and from the middleware, the project will develop Data (harmonisation and transformation) Maps.
 D5.8: Data access API & Reference data store (Final Release) Result title D5.8: Data access API & Reference data store (Final Release)
 Data access API & Reference data store (final release) Result description Final software release of the Data access API and data store. API will be published to external developers community.
 D6.2: Knowledge Protection Specification Result comments Data ownership - data governance Dynamic IT connections to new supply chain partners
 Data ownership - data governance Taxon title Data ownership - data governance
 AAA - Access, Authorisation and Authentication Taxon description Authorisation is the process of allowing an entity (humans, systems or devices) to access information systems or facilities where information and processing capabilities are being stored.
 Semantic/information interoperability Comments In order to facilitate the flow of data to and from the middleware, the project will develop Data (harmonisation and transformation) Maps.

CloudiFacturing | Cloudification of Production Engineering for Predictive Digital Manufacturing

01-10-2017

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31-03-2021

Calltopic: FoF.2017.12

: 7 | : 6
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Description , enriching the manufacturing engineering process with on-line data, and simulating and optimizing the production process with the vision to support it in real-time.
Comments As data can have huge volume, in the future highways not just for data but also for bringing algorithms/models to the data become more and more important.
 Optimizing efficiency of truck components manufacturing processes by data analytics Result title Optimizing efficiency of truck components manufacturing processes by data analytics
 Improving quality control and maintenance at manufacturing SMEs using big data analytics Result description This new solution models the behaviour of a system through a reverse engineering from the company’s old data to new advanced data.
 Optimizing design and production of electric devices Result comments Seamless, automated creation of production data avoids data handover errors and provides fast transition from simulation and design to manufacturing of motor prototypes.
 Data collection, storage, analytics, processing and AI Taxon title Data collection, storage, analytics, processing and AI
 Added Value from user perspective Taxon description In general, digital platforms offer added-value basd upon three main mechanisms: Reduction of transaction costs Network effects Use of data integration for new services
 R&I Objective 1.6: Data ‘highways’ and data spaces in support of smart factories in dynamic value networks Comments As data can have huge volume, in the future highways not just for data but also for bringing algorithms/models to the data become more and more important.

Z-Fact0r | Zero-defect manufacturing strategies towards on-line production management for European factories

01-10-2016

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31-03-2020

Calltopic: FoF.2016.03

: 11 | : 1
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Comments A final step of sintering or other processing used to finalize the repairing action. 
 Data Management Plan (DMP) Result title Data Management Plan (DMP)
 Periodic Reporting for period 1 - Z-Fact0r (Zero-defect manufacturing strategies towards on-line production management for European factories) Result description Lastly, CERTH used state of the art data analytics algorithms that contain machine learning methods, capable of processing multimodal data sources, as is the case for the multisensorial network of the
 Techniques for product reworking Result comments A final step of sintering or other processing used to finalize the repairing action. 
 Data processing Taxon title Data processing Taxon description Data processing is, generally, the collection and manipulation of items of data to produce meaningful information (https://en.wikipedia.org
 Additive manufacturing Comments A final step of sintering or other processing used to finalize the repairing action. 
    Information and communication technologies
      Data collection, storage, analytics, processing and AI
      Comment:

      Within the Z-Fact0r, the proposed (higher level) DSS, with the support of the knowledge base and the online inspection module (1st level decision support at single stage), produce, verify and validate decisions aligned with the quality control policies, production targets, desired product specifications and maintenance management requirements. Key functional characteristics of the envisioned DSS incorporates among others, techniques for monitoring and predicting product quality, action prioritization, root cause analysis, and mitigation planning algorithms (at product and workstation level). Moving beyond existing solutions that focus only on specific aspects of the production procedure, or that are restrained to diagnosis, the proposed DSS system incorporates autonomous, hierarchical decision support, based on process analytical technologies and newly developed suitably adjusted knowledge-based systems, and  combines product monitoring models and data analytics from heterogeneous sources. The envisioned DSS  takes into account a wide set of multiple factors and criteria, such as data uncertainty, lack of information and information quality, involvement of multiple actors, and real-time response. Thanks to the 5 intertwined zero-defect strategies (i.e. Z-PREDICT, Z-PREVENT, Z-DETECT, Z-REPAIR and Z-MANAGE) the overall solution presents a significant contribution to a spectacular improvement in the overall performance and reliability of the targeted multi-stage manufacturing systems and in the production agility (response to continuous adjustments in production targets).

        Data processing
        Comment:

        DATAPIXEL provides the information associated to the defect detection in the manufacturing parts selected. This information is used as an input for developing the defect detection algorithms of Z-Fact0r solution. Based on this input, a data conditioning methodology has been developed to extract information concerning to the defect position and type. This information will be used as baseline for the model validation, via comparison with the respective simulation results.

        The procedure that has been used is the following:

        • Image-based feature extraction: Convolutional Neural Networks (CNN) and Variational Auto Encoders (VAE) used as feature extractors. CNNs will define the appropriate features that have been used for the classification between healthy and defected parts, and VAEs will be utilized to distinguish that can be used for image generation.
        • Feature selection: An efficient filter feature selection (FS) method was developed for selecting informative and non- redundant feature subsets. In addition to enhanced accuracy rates and dimensionality reduction, the method have reasonably low computational demands. A robust and computationally efficient evaluation criterion with respect to patterns was defined allowing us to assess the redundancy between the features. The proposed FS technique was performed on a forward selection basis handling simultaneously both the discrimination power and the complementary characteristics between the extracted features. To decide on the number of retained features, a termination condition was finally introduced, thus avoiding the trial-and-error procedure usually employed in the common FS techniques of the literature.
        • Classification: The selected features were input in a virtual classification module. The role of this module is to provide a decision on the workpiece condition. The technologies used were Artificial Neural Networks (ANN) and deep learning algorithms.

A4BLUE | Adaptive Automation in Assembly For BLUE collar workers satisfaction in Evolvable context

01-10-2016

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30-09-2019

Calltopic: FoF.2016.04

: 7 | : 0
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 Periodic Reporting for period 1 - A4BLUE (Adaptive Automation in Assembly For BLUE collar workers satisfaction in Evolvable context) Result description Furthermore, once it is plugged the dual arm robot is ready to share updated data and allow remote control.
 Data processing Taxon title Data processing
 Data acquisition Taxon description Data acquisition systems, abbreviated by the acronyms DAS or DAQ, typically convert analog waveforms into digital values for processing.

Associated results & demonstrators

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