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

Total Cost €


EC-Contrib. €






 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.

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

Project "WINDMIL RT-DT" data sheet

The following table provides information about the project.


Organization address
address: Raemistrasse 101
postcode: 8092

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


Take a look of project's partnership.

# participants  country  role  EC contrib. [€] 


 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.


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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