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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.

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

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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