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WINDMIL RT-DT SIGNED

An autonomous Real-Time Decision Tree framework for monitoring and diagnostics on wind turbines

Total Cost €

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EC-Contrib. €

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Partnership

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 WINDMIL RT-DT project word cloud

Explore the words cloud of the WINDMIL RT-DT project. It provides you a very rough idea of what is the project "WINDMIL RT-DT" about.

innovation    creates    market    critical    lies    of    investments    carry    believe    operation    hardware    tool    wt    difficult    quantify    prototype    context    algorithm    manufacturers    time    extremely    green    classification    anomalies    autonomous    faults    smart    root    emergency    back    world    damage    mechanical    telemetry    wind    business    designed    operators    detecting    farm    position    total    running    solution    decision    risk    maintenance    first    abnormal    evident    co2    oriented    customers    platform    energy    emissions    players    selling    insurers    proof    infrastructure    proposition    deploying    reduce    innovative    repairs    power    alarmingly    lifespan    software    collaborators    components    pilot    object    offshore    ourselves    patterns    implementing    trace    diagnostics    few    tree    scheduling    industry    service    commercialisation    actions    monitoring    consists    errors    machine    turbines    learning    turbine    companies    structural    data    architecture    installations    installation    hindering   

Project "WINDMIL RT-DT" data sheet

The following table provides information about the project.

Coordinator
EIDGENOESSISCHE TECHNISCHE HOCHSCHULE ZUERICH 

Organization address
address: Raemistrasse 101
city: ZUERICH
postcode: 8092
website: https://www.ethz.ch/de.html

contact info
title: n.a.
name: n.a.
surname: n.a.
function: n.a.
email: n.a.
telephone: n.a.
fax: n.a.

 Coordinator Country Switzerland [CH]
 Total cost 148˙890 €
 EC max contribution 148˙890 € (100%)
 Programme 1. H2020-EU.1.1. (EXCELLENT SCIENCE - European Research Council (ERC))
 Code Call ERC-2018-PoC
 Funding Scheme ERC-POC
 Starting year 2018
 Duration (year-month-day) from 2018-11-01   to  2020-04-30

 Partnership

Take a look of project's partnership.

# participants  country  role  EC contrib. [€] 
1    EIDGENOESSISCHE TECHNISCHE HOCHSCHULE ZUERICH CH (ZUERICH) coordinator 148˙890.00

Map

 Project objective

Operation & Maintenance (O&M) costs may account for 30 % of the total cost of energy for offshore wind power. Alarmingly, only after a few years of installation, offshore wind turbines (WT) may need emergency repairs. They also feature an extremely short lifespan hindering investments to green energy, effectively designed to reduce CO2 emissions. We have designed real-time monitoring and diagnostics platform in the context of operation and maintenance scheduling of WT components. Using this architecture, we can quantify the risk of future failure of a given component and trace back the root-cause of the failure. This is business-critical information for Energy Companies and Wind Farm Operators. The platform consists of an autonomous software-hardware solution, implementing an Object Oriented Real-Time Decision Tree learning algorithm for smart monitoring and diagnostics of structural and mechanical WT components. The innovative concept lies in running WT telemetry data through a machine learning based decision tree classification algorithm in real-time for detecting faults, errors, damage patterns, anomalies and abnormal operation. We believe our innovation creates evident value and will raise great interest as decision-support tool for WT manufacturers, Wind Farm Operators, Service Companies and Insurers. In this project, we will carry out pre-commercialisation actions to position ourselves in the market, provide unique selling proposition for future customers as well as raise interest among potential R&D collaborators and pilot customers. We will also establish technology proof of concept for the platform. For the first time, we are applying our design in difficult-to-access energy infrastructure installations and deploying it on a real-world prototype wind turbine. The project will be carried out with technical and commercialisation support from key players within the wind energy industry.

 Publications

year authors and title journal last update
List of publications.
2020 Imad Abdallah, Konstantinos Tatsis, Eleni Chatzi
Unsupervised local cluster-weighted bootstrap aggregating the output from multiple stochastic simulators
published pages: 106876, ISSN: 0951-8320, DOI: 10.1016/j.ress.2020.106876
Reliability Engineering & System Safety 199 2020-04-15

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The information about "WINDMIL RT-DT" are provided by the European Opendata Portal: CORDIS opendata.

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