EU H2020 Project "WINDMIL RT-DT (An autonomous Real-Time Decision Tree framework for monitoring and..)": description, partecipants, costs and EC-fundings

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

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

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

EIDGENOESSISCHE TECHNISCHE HOCHSCHULE ZUERICH

CH (ZUERICH)

coordinator

148˙890.00

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

List of publications.
year	authors and title	journal	last update
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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