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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.

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

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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