CARTIF Projects
PREDICTIVO dB
Sound frequency based predictive maintenance model for wind turbines
Description
PREDICTIVOdB seeks to develop an innovative solution for the maintenance of wind farms that is quick to implement and has low energy and economic requirements. This is a demonstration project, conceived as an initial phase for the enhancement of acoustic detection applied to this field and based on the development of its own instrumentation and the proposal of specific and intelligent algorithms as a complementary tool to current monitoring 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
- Technical assesment relative to existent monitorisation.
- Tehcnical assesment relative to acquisition and post-processing methodologies.
- Technical assesment relative to comprehension and data comparison.
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: 23/07/2022 – 28/04/2023
Responsible
Fernando Gayubo Rojo
Head of Industrial and Digital Systems Division
Networking
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