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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.

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

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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