FLOIM | Flexible Optical Injection Moulding of optoelectronic devices
01-09-2018
-31-08-2022
Projects overview
Showing 91 to 120 of 467 results
ROSSINI | RObot enhanced SenSing, INtelligence and actuation to Improve job quality in manufacturing
01-10-2018
-31-03-2022
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Technologies and enablers
Comment:
Human-machine interface is key to evaluate Job Quality in considering Human Factors analysis and therefore is very relevant in the HR cell design phase also
Comment:
Based on ROS, the Rossini Modular KIT aims to be fully scalable and widely adopted
Comment:
The ROSSINI modular KIT offers advance components for the different layers of a robotic application (sensing, perception, cognition, control, actuation and integration)
Comment:
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.
Comment:
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)
Comment:
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.
Comment:
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
Comment:
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.
Comment:
The Virtual Design Tool (CORE component of the ROSSINI solution) wants to ease the design process of a HR cell implementation (e.g., helping with the sensor placing or the hazard assessment evaluation)
Comment:
Job quality evaluation focused on HRC developments aim in improving well-being of workers (acceptance, trust, physical health) in contexts where robots may become actual co-workers.
Comment:
Trust and acceptance of the workforce are the enabling factor for an actual adoption of HRC solution.
Comment:
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.
Comment:
The ROSSINI vision is the on of guarantee above all safety and health of the operators.
SWARMs | Smart and Networking UnderWAter Robots in Cooperation Meshes
01-07-2015
-30-06-2018
CloudiFacturing | Cloudification of Production Engineering for Predictive Digital Manufacturing
01-10-2017
-31-03-2021
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Technologies and enablers
Comment:
CloudiFacturing will extend the field of action of the technology developed in CloudFlow and CloudSME from the product development process to the production process, in order to leverage factory data with analytics algorithms and simulation tools
Result items:
Thanks to cloud resources, enough power computing is available to analyze different scenarios in a few days instead of several weeks.
Designers of CATMARINE and SKA are now able to achieve high-quality products by analyzing different manufacturing scenarios without wasting time, money and material.
The platform is able to optimize the resin injections points/vents and verify the presence of defects in the final product, thus ensuring a complete and correct mold-filling.
Outcomes of the project creates base for the improvement of the existing design of the water quench and will be used for the development of the new generation of the nozzles.
It is expected that new nozzle design and thus new water quench will be available for the customers in 5 years time. It is expected that those new products will attract new clients: 5 new contracts in 1 year increasing to 10 new contracts in 5 years, which will increase the turnover of Ferram by 500k Euros in 1 year and 3,5 million Euros in 5 years after the experiment end.
Z-BRE4K | Strategies and Predictive Maintenance models wrapped around physical systems for Zero-unexpected-Breakdowns and increased operating life of Factories
01-10-2017
-31-03-2021
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Technologies and enablers
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.
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.
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.
Comment:
The suggestion beyond the state-of-the-art is to have intelligent machine simulators so an information and knowledge rich platform can provide an accurate account of the machine’s current state and provide predictive (look ahead) potential scenarios of future time type, severity and risks of breakdown. Collected, processed, integrated and aggregated data will be structured and fed in real-time into networked simulators enabling advanced analysis and visualization to provide smart services, higher fidelity and prediction accuracy for production and manufacturing assets management. Different schemes for data collection configuration are implemented (ranging dedicated IoT devices with independent methodologies) to collect raw data from sensors, pre-process and aggregate the information, and share the results with other services through an IDS connector. The Z-Bre4k IDS connectors have a reference architecture to ensure data sovereignty and integrity throughout this collection phase.
Comment:
Within Z-BRE4K a semantic data modelling is used for interoperability. Semantic representation is used for machinery, critical components, failure modes as well as optimal conditions. Statistical methods and machine learning algorithms are used in offline mode to discover patterns in the data and associate them with specific events (Pattern discovery, Association Rules), as well as infer causality events in cases such as quality control (Quality Estimation based on Machine Status.
Comment:
Within Z-BRE4K, a novel software application will be developed and added to I-LiKe Machine’s tech stack: A Knowledge Base System (KBS) to extract, store, and retrieve all the relevant information enriched with semantic annotations to guarantee a prompt identification of criticalities in the process.
Comment:
The KBS represents a step towards the implementation of novel and innovative solutions, still not common practice in manufacturing. The data repository is in the form of Triplestore which is designed to store identities conducted from triplex collections representing a subject-predicate-object relationship. On top of the repositories a reasoning engine creates relationships and allows to extract knowledge to be consumed by other applications.
Comment:
In the framework of Z-BRE4K, an IoT approach is applied to integrate end user machines to the Z-BRE4K platform. Through IoT gateways deployed at the shop floor, machine components are enabled to communicate their conditions, sending sensors data to the cloud where they are stored and analysed to provide predictive maintenance related information.
Comment:
The UI’s goal is to visualize data from maintainers and components statuses (real time data and relevant KPIs). The SPARQL Web service is used to send custom SPARQL queries against the Semantic Framework RDF repository as a general-purpose querying web service. The UI can visualise the probability of breakdown and RUL. CAD and CAE models would be useful in mapping these values into a 3D visualisation.
Comment:
Z-Bre4k provides a Semantic Framework as a RESTful web services API. Each request returns an HTTP status and optionally a document of result sets. Each results document can be serialized and may be expressed as RDF, pure XML or JSON. The operator input to the machine and threshold changes can be built as a UI. These parameters can be monitored directly in machine simulators. Furthermore, the dashboard application (M3 modules) will alert the shop floor operator about quality detected issues and suggests recommendations for the production adjustment and maintenance of the machine.
Comment:
The AUTOWARE apps development will be supported and linked to the FIWARE. AUTOWARE approach is to connect and extend the FIWARE for Industry resources and assets to the end of digital automation community, so synergies emerge both in terms of multi-sided business opportunities and amount of resources that are made available to the cognitive automation community to build their autonomous solutions and apps.
Comment:
FIWARE is a curated open source framework with components that can be assembled together with other third-party ones to accelerate the development of Smart Solutions. The Orion Context Broker is the core component of any “Powered by FIWARE” platform. It enables the system to perform updates and access to the current state of context.
Comment:
The choice of the AUTOWARE platform was based on the fact that is an open source project, and that is hardware agnostic.
Comment:
AUTOWARE platform will push forward and stablish an open CPPS ecosystem. In the AUTOWARE Framework, a collection of enablers has been defined as components/tools that will enable potential users of the tools, be it end users, system integrators or software developers, to easily apply the developed technical enablers to their daily work. Moreover, verification, validation and certification enablers will be introduced in the AUTOWARE platform.
Comment:
Z-BRE4K’s mission is to build a distributed software system solution including the Industry 4.0 principals towards cyber-physical, digital, virtual and resource-efficient factories. The ultimate goal is to develop intelligent maintenance systems for increased reliability of production systems. Additionally, special attention is going to be given on processes, advancing technologies and products, integrating knowledge, training, technology and industrial development in a market-oriented environment. Z-BRE4K’s intended impact to the European manufacturing industry in the increase of the in-service efficiency by 24%
Comment:
Z-BRE4K will contribute to the productivity increase of different critical manufacturing processes, such as joining (GESTAMP), cutting (PHILIPS) and forming (GESTAMP, PHILIPS, SACMI) by providing analytics and suggestion in to order to assist in minimizing the machines breakdowns. The main gain is operational and maintenance costs reduction. Furthermore, for the GESTAMP use case a real-time arc welding quality control system, based on infrared images, is being developed
Comment:
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.
Comment:
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.
Comment:
The modelling and simulation methods used in Z-BRE4K are mainly Finite Element Methods (FEM) where complex problems and processes from the real world are being simplified and solved using a numerical approach. First, an accurate digital model of the geometry and material properties of all involved objects, boundary conditions between these objects and process data is created (i.e. forces or temperature).
Then, the complex shape of all objects involved, is approximate using a finite number of simple geometries (i.e. triangles) which simplify the complex mathematical problem. A computer is capable of solving these mathematical operations at a rate impossible for humans and thus enables the user to analyse various scenarios, ranging from mechanical strains within the objects to rise in temperature or material fatigue. This information can be used to predict the remaining useful lifetime of a given tool.
Simulation platform is deployed by the physical equipment to create intuitive maintenance control and management systems. The Z-BRE4K’s platform simulation capabilities will estimate the remaining useful life calling for maintenance and suggesting the optimal times to place orders for spare parts, reducing the related costs. The increased predictability of the system and the failure prevention actions will reduce the number of failures, maximise the performance, reduce the repair/recover times reducing further the costs.
By applying time series analysis, we are able to detect special events that are known (Fault detection) or unknown (anomaly detection) during production. This information, correlated with sensor readings is fed into machine learning algorithms that create estimates of Remaining Useful Life (RUL), Health Indexes (HI) and forecast upcoming events (Likelihood of Failure). Special focus is given in techniques that can provide real-time information (Fast computation and high accuracy) as well as being scalable in order to use new data as it becomes available. Additional information such as meantime between failures based on historical data or an expert opinion, CAE data, quality control data, real time states etc. are also used to the design of machine simulators.
Repro-light | Re-usable and re-configurable parts for sustainable LED-based lighting systems
01-10-2017
-30-09-2020
AMable | AdditiveManufacturABLE
01-09-2017
-31-08-2021
MouldTex | FRICTION OPTIMISATION OF SEALS THROUGH ADVANCED LASER SURFACE TEXTURING OF MOULDS
01-11-2017
-30-04-2021
LASER4SURF | LASER FOR MASS PRODUCTION OF FUNCTIONALISED METALLIC SURFACES
01-10-2017
-31-03-2021
UPTIME | UNIFIED PREDICTIVE MAINTENANCE SYSTEM
01-09-2017
-31-08-2020
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Technologies and enablers
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
proactive computing principles.
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:
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.
- The UPTIME edge module will ensure data collection from machines, sensors, etc. and sent it on for analysis. It may also include some additional functionalities which require real-time results.
- The UPTIME Cloud module contains all the advanced functionalities of the solution, which do not require a real time result. There we will analyse the data collected on the edge, as well as data received from relevant information systems, and provide the expected predictions. “Cloud” can refer to remote servers or an internal cloud within the customer’s Plant or Enterprise, as is deemed necessary by the customer.
- Lastly the GUI module, through which the user will interact with the previously mentioned functionalities, whether it is to view data or configure the solution.
Comment:
4 main components in the cloudbased infastructure of the UPTIME platform include:
- UPTIME_DETECT and PREDICT component (extended version of PreIno prototype) processes mainly timeseries?based data from the field, to give further context to the data, e.g. to detect topical conditions of technical equipment and to predict probable future conditions.
- UPTIME_ANALYZE (a new developed prototype) 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.
- UPTIME_DECIDE component (extended version of PANDDA prototype) that implements a prescriptive analytics approach for proactive decision making in a streaming processing computational environment. It provides real-time prescriptions fo the optimal maintenance actions and the optimal time for their implementation on the basis of streams of predictions about future failures.
- UPTIME_FMECA (extended version of DRIFT prototype) provides estimation of possible failure modes and risk criticalities evolution through its data-driven FMECA approach.
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:
- Sensor signal processing, which collects the signals from equipment or other sensors, and pre-processes them before transmitting them on the cloud platform.
- Edge diagnosis for optional state detection diagnosis for certain use cases.
- Support functions for functions necessary for the correct operation of the edge module.
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.
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 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:
UPTIME has a common MySQL database that will handle the operations of the UPTIME system.
Comment:
UPTIME_ANALYZE is a data analytics engine driven by the need to leverage manufacturers’ legacy data and operational data related to maintenance, as well as to extract and correlate relevant knowledge. The data mining and analytics of ANALYZE component practically delivers the intelligence of the ANALYZE component by defining, training, executing and experimenting with different machine learning algorithms.
Comment:
UPTIME_VISUALIZE (extended version of SeaBAR prototype) component is responsible for the intuitive and uninterrupted human-machine interaction. The user interfaces, including the analytics dashboards and the notificaiton engine, will be customised or further developed in full accordance with the end-user business case. Taking an example ofthe UPTIME White Good business case for complex automatic production line to produce drums for dryer, the generation of early warnings to suggest autonomous activities to factory workers should be communicated through mobile devices or on-board devices.
Comment:
The data visualisation in UPTIME is performed by the UPTIME_VISUALIZE (SeaBAR prototype) component:
- By establishing standardised connectors to the key data/information/knowledge sources for maintenance, production planning, logistics and quality management.
- By faceting, filtering and semantic structuring of the collected data according to maintenance viewpoints.
- Context-sensitive, interactive visualizations in order to allow the end user to easily search and navigate through huge amounts of heterogeneous information with the aim to enable a maximum flexible analysis of all relevant information, e.g. to drill-down into the data according to a region, timeframe and machine or to generalise a specific critical situation and find similar (past) situations with appropriate measures and individual user experience (e.g. best practices).
- A customizable dashboard focusing on the specific information needs of e.g. maintenance engineers, quality managers and upper management by different context specific visualization and analytics tools.
- Continuous visualization of critical data and utilization of captured experience from past situations for providing useful insights in interaction with manufacturing companies’ systems.
- By turning from generic statistical data towards instance-specific data and enabling instance specific diagnosis and prognosis.
Comment:
UPTIME Platform is developed accroding to unified predictive maintenance framework and an associated unified information system to enable the predictive maintenance strategy implementation in manufacturing industries. The UPTIME predictive maintenance system will extend and unify the new digital, e-maintenance services and tools and will incorporate information from heterogeneous data sources, e.g. sensors, to more accurately estimate the process performances. The UPTIME predictive maintenance platform is developed mainly based on five baseline e-maintenance services and tools:
- USG (Universal Sensor Gateway): USG serves as a modular data acquisition and integration device to the Product Lifecycle Management (PLM) ecosystem of a product
- preInO: The preInO Processing Engine is able to detect and predict the state of a whole system or components with respect to mechanical systems such as windturbines, special-purpose vehicles, production machinery, etc.
- PANDDA (ProActive seNsing enterprise Decision configurator DAshboard): PANDDA is a software service that implements (i) proactive decision methods to provide recommendations about mitigating (perfect or imperfect) maintenance actions and the time for their implementation on the basis of real-time prognostic information; and (ii) a Sensor-Enabled Feedback (SEF) mechanism for continuous improvement of the generated recommendations.
- SeaBAR (Search Based Application Repository): SeaBAR is a modular software platform built on Big Data and Enterprise Search technology. The SeaBAR platform supports end users by means of data aggregation, data analysis and visualization.
- DRIFT (Data-Driven Failure Mode, Effects, and Criticality Analysis (FMECA) Tool): DRIFT is a tool that, on the basis of the information gathered in other modules (maintenance data, production, logistics, quality data) use them to identify what are the Failure Modes, Effects and Criticalities of the components and system according to literature available and novel correlation algorithms among modes failures, effects and critical impacts.
Comment:
To ease integration of all UPTIME components, the main programming language used by the components and the integrated platform is Java.
Comment:
The UPTIME platform will leverage crucial, and often hidden, data from machines and systems in real time and substantiate the benefits of advanced predictive maintenance analytics in boosting asset availability and service levels in manufacturing operations. Moreover, UPTIME delivers new ways for effective operational risk management in terms of preventing unexpected failures of a manufacturer’s assets, and effectively planning maintenance actions, thereby transforming the mentality of manufacturing industries in respect to maintenance services. With the help of the end-to-end UPTIME smart diagnosis-prognosis-decision making methods, manufacturers become leaner, more versatile and better prepared to act upon accidents and unexpected incidents in their everyday operations. The increased accident mitigation capabilities they acquire, allow them not only to accelerate the workplace safety and improve the workers’ health,
but also to reduce the incurring costs and become more competitive.
Comment:
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.
I4MS-Go | I4MS Going to Market Alliance
01-09-2017
-29-02-2020
PreCoM | Predictive Cognitive Maintenance Decision Support System
01-11-2017
-31-10-2020
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Technologies and enablers
L4MS | Logistics for Manufacturing SMEs
01-10-2017
-31-03-2021
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Technologies and enablers
SERENA | VerSatilE plug-and-play platform enabling remote pREdictive mainteNAnce
01-10-2017
-30-09-2020
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Technologies and enablers
Comment:
Digital models enahnced with real world data acquired from sensor devices will be used as the basis of physical phenomena that affect the operational condition of the equipment, such as degradation. THis will result in the improvement of the accuracy of the predictive maintenance functionalities of the SERENA platfrom and tools.
Comment:
The SERENA porject considers the support of data analytics functionalities for acquiring certainportion of sensor data to feed the machine learning algorithms responsible for predictive maintenance.
Comment:
The SERENA project includes the developments of a plug and play device for machine data acquisition.
Comment:
Digital models enahnced with real world data acquired from sensor devices will be used as the basis of physical phenomena that affect the operational condition of the equipment, such as degradation. THis will result in the improvement of the accuracy of the predictive maintenance functionalities of the SERENA platfrom and tools.
MIDIH | Manufacturing Industry Digital Innovation Hubs
01-10-2017
-30-09-2020
PROGRAMS | PROGnostics based Reliability Analysis for Maintenance Scheduling
01-10-2017
-30-09-2020
ACCURATE | ACHIEVING RESILIENCE THROUGH MANUFACTURING AS A SERVICE, DIGITAL TWINS AND ECOSYSTEMS
01-12-2023
-30-11-2026
AMPS | Automated Mass Production of SOC Stacks
01-06-2023
-31-05-2027
I-MECH | Intelligent Motion Control Platform for Smart Mechatronic Systems
01-06-2017
-31-05-2019
Productive4.0 | Electronics and ICT as enabler for digital industry and optimized supply chain management covering the entire product lifecycle
01-05-2017
-30-04-2020
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Technologies and enablers
THOMAS | Mobile dual arm robotic workers with embedded cognition for hybrid and dynamically reconfigurable manufacturing systems
01-10-2016
-30-09-2020
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Technologies and enablers
STREAM-0D | Simulation in Real Time for Manufacturing with Zero Defects
01-10-2016
-31-03-2020
Sensor data fusion from multiple and heterogeneous is at the core of the development of the RS4 Controller (CORE component of the Rossini Modular KIT)