CARTIF Projects
PREDICTIVO dB
Sound frequency based predictive maintenance model for wind turbines
Description
The project “Predictive maintenance model for wind turbines based on sound frequency PREDICTIVOdb” is the development of acoustic frequency analysis for the optimisation and predictive maintenance of wind turbines in service. This is intended as a reasonable supplement to conventional vibration analysis maintenance. It is also the starting point for improving the interventions of maintenance officers in the environment of wind turbine systems.
Objectives
-
- Characterise the optimal performance of a particular wind turbine and acoustically discern when it is not.
- Collect and analyse available data from existing monitoring.
- Development of specific acoustic acquisition equipment.
- Development of post-processing routines and predictive models based on a neural network model.
Actions
- Definition of technical requirements and analysis of the wind turbines operative environment.
- Technical assessment in relation with monitorisation and evaluation of the dynamic behaviour based in vibrations.
- Technical assessment for the comprehension and comparison of information based on vibrations and the one based on sound.
Expected results
- Proposals for improvement regarding signal acquisition.
- Identification of operating scenarios
R&D Line
- Research in modelling and diagnostics in predictive maintenance of assets subject to variable operating regimes.
Partners
Ministerio de Industria, Cultura y Turismo (MINCOTUR)
AEI-010500-2022b-97
Total Budget: 129,032€
CARTIF Budget: 15,887€
CARTIF Financing :10,326 €
Duration: 01/08/2022 – 28/04/2023
Responsible
Fernando Gayubo Rojo
Head of Industrial and Digital Systems Division
Networking
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