Summary
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.
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.
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More information & hyperlinks
| Web resources: | https://cordis.europa.eu/project/id/825833 |
| Start date: | 01-11-2018 |
| End date: | 31-07-2020 |
| Total budget - Public funding: | 148 890,00 Euro - 148 890,00 Euro |
Cordis data
Original description
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.
Status
CLOSEDCall topic
ERC-2018-PoCUpdate Date
27-04-2024
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